From 514e3be0e3d1c851816d0ec7d2848e6faf1ae00a Mon Sep 17 00:00:00 2001 From: Qile Date: Mon, 17 Jul 2023 02:38:07 +0200 Subject: [PATCH 001/142] made package more in-line with best practisces --- .Rbuildignore | 1 + .github/.gitignore | 1 + .github/workflows/R-CMD-check.yaml | 49 +++++++++ DESCRIPTION | 58 ++++++----- README.md | 5 + inst/WORDLIST | 156 +++++++++++++++++++++++++++++ scRepertoire.Rproj | 19 ++++ tests/spelling.R | 3 + tests/testthat.R | 12 +++ tests/testthat/test-utils.R | 10 ++ vignettes/vignette.Rmd | 16 +++ 11 files changed, 303 insertions(+), 27 deletions(-) create mode 100644 .github/.gitignore create mode 100644 .github/workflows/R-CMD-check.yaml create mode 100644 inst/WORDLIST create mode 100644 scRepertoire.Rproj create mode 100644 tests/spelling.R create mode 100644 tests/testthat.R create mode 100644 tests/testthat/test-utils.R diff --git a/.Rbuildignore b/.Rbuildignore index ebe8dfdb..a1bb2a56 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -1,3 +1,4 @@ ^scRepertoire\.Rproj$ ^\.Rproj\.user$ ^www$ +^\.github$ diff --git a/.github/.gitignore b/.github/.gitignore new file mode 100644 index 00000000..2d19fc76 --- /dev/null +++ b/.github/.gitignore @@ -0,0 +1 @@ +*.html diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml new file mode 100644 index 00000000..a3ac6182 --- /dev/null +++ b/.github/workflows/R-CMD-check.yaml @@ -0,0 +1,49 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + +name: R-CMD-check + +jobs: + R-CMD-check: + runs-on: ${{ matrix.config.os }} + + name: ${{ matrix.config.os }} (${{ matrix.config.r }}) + + strategy: + fail-fast: false + matrix: + config: + - {os: macos-latest, r: 'release'} + - {os: windows-latest, r: 'release'} + - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} + - {os: ubuntu-latest, r: 'release'} + - {os: ubuntu-latest, r: 'oldrel-1'} + + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + R_KEEP_PKG_SOURCE: yes + + steps: + - uses: actions/checkout@v3 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + r-version: ${{ matrix.config.r }} + http-user-agent: ${{ matrix.config.http-user-agent }} + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::rcmdcheck + needs: check + + - uses: r-lib/actions/check-r-package@v2 + with: + upload-snapshots: true diff --git a/DESCRIPTION b/DESCRIPTION index 710488ee..3bbd6857 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -13,32 +13,36 @@ Depends: ggplot2, R (>= 4.0) Imports: - stringdist, - dplyr, - reshape2, - ggalluvial, - stringr, - vegan, - powerTCR, - SingleCellExperiment, - SummarizedExperiment, - plyr, - parallel, - doParallel, - methods, - utils, - rlang, - igraph, - ggraph, - tidygraph, - SeuratObject, - stats + stringdist, + dplyr, + reshape2, + ggalluvial, + stringr, + vegan, + powerTCR, + SingleCellExperiment, + SummarizedExperiment, + plyr, + parallel, + doParallel, + methods, + utils, + rlang, + igraph, + ggraph, + tidygraph, + SeuratObject, + stats, + Seurat Suggests: - knitr, - rmarkdown, - BiocStyle, - circlize, - scales, - Seurat, - scater + knitr, + rmarkdown, + BiocStyle, + circlize, + scales, + scater, + testthat (>= 3.0.0), + spelling VignetteBuilder: knitr +Config/testthat/edition: 3 +Language: en-US diff --git a/README.md b/README.md index 38cec353..2ea856a8 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,9 @@ # scRepertoire + +[![BioC status](http://www.bioconductor.org/shields/build/release/bioc/scRepertoire.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/scRepertoire) +[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.borch.dev/uploads/vignette/vignette) + + #### A toolkit for single-cell immune profiling diff --git a/inst/WORDLIST b/inst/WORDLIST new file mode 100644 index 00000000..ab652fd4 --- /dev/null +++ b/inst/WORDLIST @@ -0,0 +1,156 @@ +AAACGGGAGATGGCGT +AIRR +Andreatta +BCR +BD +BioC +CATSATLRVVAEKLFF +CAVNGGSQGNLIF +CDR +CSAEREDTDTQYF +CTaa +CTgene +CTnt +CTstrict +Carmona +CellRanger +Chao +Clonotype +Clonotypes +CombineBCR +CombineTCR +Contig +Contigs +Corvino +Drs +Francesco +Github +Hyperexpanded +IGH +IGL +Ig +Jaccard +LD +Levenshtein +Massimo +Mazziotta +Morisita +PMID +PY +Pielou +Pielou's +ProjectTIL +SCE +SingleCellExperiment +Startrac +StartracDiversity +StartracOUt +StartracOut +TCR +TCRA +TCRB +TCRs +TRA +TRB +TRD +TRG +Trex +VDJ +VDJC +Vgene +WAT +Weizhou +Zhang +aa +airr +al +alluvialClonotypes +analyis +autencoding +barcode +barcodes +betwwen +changeNames +circlize +clonalNetwork +clonalOverlay +clonally +cloneCall +cloneType +cloneTypes +clonotype +clonotypes +clonotypic +colorectal +combineBCR +combineExpression +combineTCR +combinedContig +comparators +contig +contigs +createHTOContigList +csv +defualt +df +dispaly +doParallel +downsample +downsampling +et +expa +exportClones +exportTable +filterMulti +getCirclize +ggalluvial +ggplot +github +highlightClonotypes +igraph +individualy +jaccard +labled +loc +mRNA +majorCluster +migr +migra +morisita +multiplets +nt +num +objecter +occupiedscRepertoire +outputed +pIndex +paramater +paramter +permutated +powerTCR +preprocess +quantifications +rda +registerDoParallel +removeMulti +removeNA +reprtoire +runing +scRepetoire +scater +seurat +startrac +stripBarcode +subseted +subseting +subtype +sys +tcr +throughly +tran +transcriptomic +tsv +unqiue +unreturned +warpper +wheter diff --git a/scRepertoire.Rproj b/scRepertoire.Rproj new file mode 100644 index 00000000..566bd78d --- /dev/null +++ b/scRepertoire.Rproj @@ -0,0 +1,19 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 4 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +AutoAppendNewline: Yes + +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source diff --git a/tests/spelling.R b/tests/spelling.R new file mode 100644 index 00000000..6713838f --- /dev/null +++ b/tests/spelling.R @@ -0,0 +1,3 @@ +if(requireNamespace('spelling', quietly = TRUE)) + spelling::spell_check_test(vignettes = TRUE, error = FALSE, + skip_on_cran = TRUE) diff --git a/tests/testthat.R b/tests/testthat.R new file mode 100644 index 00000000..acc6ee60 --- /dev/null +++ b/tests/testthat.R @@ -0,0 +1,12 @@ +# This file is part of the standard setup for testthat. +# It is recommended that you do not modify it. +# +# Where should you do additional test configuration? +# Learn more about the roles of various files in: +# * https://r-pkgs.org/tests.html +# * https://testthat.r-lib.org/reference/test_package.html#special-files + +library(testthat) +library(scRepertoire) + +test_check("scRepertoire") diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R new file mode 100644 index 00000000..d08471ae --- /dev/null +++ b/tests/testthat/test-utils.R @@ -0,0 +1,10 @@ +test_that("'%!in%' works", { + vector <- c(1, 2, 3, 4, 5) + + expect_true(0 %!in% vector) + expect_true(6 %!in% vector) + expect_false(3 %!in% vector) + expect_false(5 %!in% vector) + expect_true(1 %!in% NULL) + expect_true(list(1) %!in% NA) +}) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index fabd88fa..5e1326a9 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -17,7 +17,23 @@ vignette: > ```{r, echo=FALSE, results="hide", message=FALSE} knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE) + +# to pass R CMD check, these can be installed in the knitted scope quietly +if (!require("BiocManager", quietly = TRUE)) { + install.packages("BiocManager") +} +library(BiocManager) + +if (!require("BiocStyle", quietly = TRUE)) { + BiocManager::install("BiocStyle") +} library(BiocStyle) + +if (!require("scater", quietly = TRUE)) { + BiocManager::install("scater") +} +library(scater) + ``` # Introduction From 7dff110d362691ee2877c2c21b487d037c43e243 Mon Sep 17 00:00:00 2001 From: Qile Date: Mon, 24 Jul 2023 14:31:21 +0200 Subject: [PATCH 002/142] few changes --- .github/workflows/R-CMD-check.yaml | 4 +-- .gitignore | 1 + R/combineContigs.R | 4 +-- R/seuratFunctions.R | 44 ++++++++++++++---------------- 4 files changed, 26 insertions(+), 27 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index a3ac6182..03256fee 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -2,9 +2,9 @@ # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: [main, master] + branches: [master, dev] pull_request: - branches: [main, master] + branches: [master, dev] name: R-CMD-check diff --git a/.gitignore b/.gitignore index cad79778..ae2558f5 100644 --- a/.gitignore +++ b/.gitignore @@ -2,3 +2,4 @@ .DS_Store .Rhistory .Rhistory +local_tests.R diff --git a/R/combineContigs.R b/R/combineContigs.R index e530717a..09f60ea9 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -63,9 +63,9 @@ combineTCR <- function(df, } df[[i]]$sample <- samples[i] df[[i]]$ID <- ID[i] - if (filterMulti == TRUE) { + if (filterMulti) { df[[i]] <- filteringMulti(df[[i]]) - } + } } #Prevents error caused by list containing elements with 0 rows blank.rows <- which(unlist(lapply(df, nrow)) == 0) diff --git a/R/seuratFunctions.R b/R/seuratFunctions.R index 63420779..ff5fe49b 100644 --- a/R/seuratFunctions.R +++ b/R/seuratFunctions.R @@ -76,7 +76,7 @@ combineExpression <- function(df, data <- data.frame(df[[i]], stringsAsFactors = FALSE) data2 <- unique(data[,c("barcode", cloneCall)]) data2 <- na.omit(data2[data2[,"barcode"] %in% cell.names,]) - if (proportion == TRUE) { + if (proportion) { data2 <- data2 %>% group_by(data2[,cloneCall]) %>% summarise(Frequency = n()/nrow(data2)) } else { @@ -102,14 +102,14 @@ combineExpression <- function(df, sub1 <- subset(data, data[,group.by] == x[i]) sub2 <- subset(data2, data2[,group.by] == x[i]) merge <- merge(sub1, sub2, by=cloneCall) - if (proportion == TRUE) { + if (proportion) { merge$Frequency <- merge$Frequency/length(merge$Frequency) } Con.df <- rbind.data.frame(Con.df, merge) } nsize <- Con.df %>% group_by(Con.df[,paste0(group.by, ".x")]) %>% summarise(n = n()) } else { - if (proportion == TRUE) { + if (proportion) { data <- data %>% group_by(data[,cloneCall]) %>% mutate(Frequency = n()/nrow(data)) @@ -141,15 +141,18 @@ combineExpression <- function(df, location <- which(colnames(PreMeta) == "Frequency") colnames(PreMeta)[location] <- paste0("Frequency.", group.by) } - if (inherits(x=sc, what ="Seurat")) { - if (length(which(rownames(PreMeta) %in% - rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { - warning("< 1% of barcodes match: Ensure the barcodes in + + warn_str <- "< 1% of barcodes match: Ensure the barcodes in the Seurat object match the barcodes in the combined immune receptor list from scRepertoire - most common issue is the addition of the prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() - functions") + functions" + + if (inherits(x=sc, what ="Seurat")) { + if (length(which(rownames(PreMeta) %in% + rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { + warning(warn_str) } col.name <- names(PreMeta) %||% colnames(PreMeta) sc[[col.name]] <- PreMeta @@ -157,18 +160,13 @@ combineExpression <- function(df, rownames <- rownames(colData(sc)) if (length(which(rownames(PreMeta) %in% rownames))/length(rownames) < 0.01) { - warning("< 1% of barcodes match: Ensure the barcodes - in the SingleCellExperiment object match the - barcodes in the combined immune receptor list from - scRepertoire - most common issue is the addition of the - prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() - functions") } + warning(warn_str) } colData(sc) <- cbind(colData(sc), PreMeta[rownames,])[, union(colnames(colData(sc)), colnames(PreMeta))] rownames(colData(sc)) <- rownames } - if (filterNA == TRUE) { sc <- filteringNA(sc) } + if (filterNA) { sc <- filteringNA(sc) } sc$cloneType <- factor(sc$cloneType, levels = rev(names(cloneTypes))) - return(sc) + sc } #' Highlighting specific clonotypes in Seurat @@ -215,7 +213,7 @@ highlightClonotypes <- function(sc, meta <- meta[,-(which(colnames(meta) == "ident"))] col.name <- names(meta) %||% colnames(meta) sc[[col.name]] <- meta - return(sc) + sc } #' Exploring interaction of clonotypes by seurat or SCE dynamics @@ -379,7 +377,7 @@ occupiedscRepertoire <- function(sc, prop = value/total) meta <- as.data.frame(meta) } - if (exportTable == TRUE) { + if (exportTable) { return(meta) } col <- length(unique(meta$cloneType)) @@ -403,10 +401,10 @@ occupiedscRepertoire <- function(sc, if (!is.null(facet.by)) { plot <- plot + facet_grid(.~meta[,facet.by]) } - if (label == TRUE) { + if (label) { plot <- plot + geom_text(aes(label = value), position = position_stack(vjust = 0.5)) - } - return(plot) + } + plot } #' Visualize distribution of clonal frequency overlaid on dimensional reduction plots @@ -467,7 +465,7 @@ clonalOverlay <- function(sc, if (!is.null(facet)) { plot <- plot + facet_wrap(~facet) } - return(plot) + plot } #' Generate a contig list from a multiplexed experiment @@ -541,5 +539,5 @@ createHTOContigList <- function(contig, } names(contig.list) <- unique.groups } - return(contig.list) + contig.list } From 66df30b32713672a09338521cb09b344aa07a248 Mon Sep 17 00:00:00 2001 From: Qile Date: Mon, 24 Jul 2023 15:04:16 +0200 Subject: [PATCH 003/142] made R CMD check only check the qile branch for now --- .github/workflows/R-CMD-check.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index 03256fee..fde02ff5 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -2,9 +2,9 @@ # Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help on: push: - branches: [master, dev] + branches: [qile] pull_request: - branches: [master, dev] + branches: [qile] name: R-CMD-check From e4adb056701187aa8f0af402d6bc61040db97b4a Mon Sep 17 00:00:00 2001 From: Qile Date: Mon, 24 Jul 2023 15:51:01 +0200 Subject: [PATCH 004/142] added license and badges --- .Rbuildignore | 2 + DESCRIPTION | 2 +- LICENSE.md | 277 +++++++++++++++++++++++++++++++++++++++++ README.md | 2 + vignettes/vignette.Rmd | 21 ++-- 5 files changed, 293 insertions(+), 11 deletions(-) create mode 100644 LICENSE.md diff --git a/.Rbuildignore b/.Rbuildignore index a1bb2a56..e56ddeb1 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -2,3 +2,5 @@ ^\.Rproj\.user$ ^www$ ^\.github$ +^local_tests\.R$ +^LICENSE\.md$ diff --git a/DESCRIPTION b/DESCRIPTION index 3bbd6857..b08eccde 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -4,7 +4,7 @@ Version: 1.11.0 Authors@R: c( person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com")) Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. -License: GPL-2 +License: GPL (>= 2) Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.3 diff --git a/LICENSE.md b/LICENSE.md new file mode 100644 index 00000000..f67ff502 --- /dev/null +++ b/LICENSE.md @@ -0,0 +1,277 @@ +GNU General Public License +========================== + +_Version 2, June 1991_ +_Copyright © 1989, 1991 Free Software Foundation, Inc.,_ +_51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA_ + +Everyone is permitted to copy and distribute verbatim copies +of this license document, but changing it is not allowed. + +### Preamble + +The licenses for most software are designed to take away your +freedom to share and change it. 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Many people have made +generous contributions to the wide range of software distributed +through that system in reliance on consistent application of that +system; it is up to the author/donor to decide if he or she is willing +to distribute software through any other system and a licensee cannot +impose that choice. + +This section is intended to make thoroughly clear what is believed to +be a consequence of the rest of this License. + +**8.** If the distribution and/or use of the Program is restricted in +certain countries either by patents or by copyrighted interfaces, the +original copyright holder who places the Program under this License +may add an explicit geographical distribution limitation excluding +those countries, so that distribution is permitted only in or among +countries not thus excluded. In such case, this License incorporates +the limitation as if written in the body of this License. + +**9.** The Free Software Foundation may publish revised and/or new versions +of the General Public License from time to time. Such new versions will +be similar in spirit to the present version, but may differ in detail to +address new problems or concerns. + +Each version is given a distinguishing version number. If the Program +specifies a version number of this License which applies to it and “any +later version”, you have the option of following the terms and conditions +either of that version or of any later version published by the Free +Software Foundation. If the Program does not specify a version number of +this License, you may choose any version ever published by the Free Software +Foundation. + +**10.** If you wish to incorporate parts of the Program into other free +programs whose distribution conditions are different, write to the author +to ask for permission. For software which is copyrighted by the Free +Software Foundation, write to the Free Software Foundation; we sometimes +make exceptions for this. Our decision will be guided by the two goals +of preserving the free status of all derivatives of our free software and +of promoting the sharing and reuse of software generally. + +### NO WARRANTY + +**11.** BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY +FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN +OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES +PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED +OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF +MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS +TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE +PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, +REPAIR OR CORRECTION. + +**12.** IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING +WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR +REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, +INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING +OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED +TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY +YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER +PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE +POSSIBILITY OF SUCH DAMAGES. + +END OF TERMS AND CONDITIONS diff --git a/README.md b/README.md index 2ea856a8..70fc39b3 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,8 @@ # scRepertoire [![BioC status](http://www.bioconductor.org/shields/build/release/bioc/scRepertoire.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/scRepertoire) +[![R-CMD-check](https://github.com/ncborcherding/scRepertoire/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ncborcherding/scRepertoire/actions/workflows/R-CMD-check.yaml) +[![Codecov test coverage](https://codecov.io/gh/ncborcherding/scRepertoire/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ncborcherding/scRepertoire?branch=master) [![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.borch.dev/uploads/vignette/vignette) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 5e1326a9..10590c62 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -18,22 +18,23 @@ vignette: > ```{r, echo=FALSE, results="hide", message=FALSE} knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE) -# to pass R CMD check, these can be installed in the knitted scope quietly +# to pass R CMD check, packages can be installed in the knitted environment if (!require("BiocManager", quietly = TRUE)) { - install.packages("BiocManager") + install.packages("BiocManager") } library(BiocManager) -if (!require("BiocStyle", quietly = TRUE)) { - BiocManager::install("BiocStyle") +quiet_bioc_load <- function(...) { + pkgs <- list(...) + for (pkg in pkgs) { + if (!require(pkg, character.only = TRUE, quietly = TRUE)) { + BiocManager::install(pkg) + } + suppressPackageStartupMessages(invisible(library(pkg, character.only = TRUE))) + } } -library(BiocStyle) - -if (!require("scater", quietly = TRUE)) { - BiocManager::install("scater") -} -library(scater) +quiet_bioc_load("BiocStyle", "scater") ``` # Introduction From cdf4028e697b9f813405ce9bd197f4654ed12a26 Mon Sep 17 00:00:00 2001 From: Qile Date: Wed, 2 Aug 2023 14:58:36 +0200 Subject: [PATCH 005/142] added citation, package docs, rcpp, and syntax adjustments --- .gitignore | 1 + DESCRIPTION | 5 ++++- R/combineContigs.R | 23 ++++++++++++----------- R/processing.R | 16 ++++++++++------ R/scRepertoire-package.R | 8 ++++++++ R/utils.R | 7 ++++--- R/viz.R | 8 +++++--- inst/CITATION | 13 +++++++++++++ scRepertoire.Rproj | 2 +- src/.gitignore | 3 +++ 10 files changed, 61 insertions(+), 25 deletions(-) create mode 100644 R/scRepertoire-package.R create mode 100644 inst/CITATION create mode 100644 src/.gitignore diff --git a/.gitignore b/.gitignore index ae2558f5..d39625f1 100644 --- a/.gitignore +++ b/.gitignore @@ -3,3 +3,4 @@ .Rhistory .Rhistory local_tests.R +.RData diff --git a/DESCRIPTION b/DESCRIPTION index b08eccde..f04f07bb 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -33,7 +33,8 @@ Imports: tidygraph, SeuratObject, stats, - Seurat + Seurat, + Rcpp Suggests: knitr, rmarkdown, @@ -46,3 +47,5 @@ Suggests: VignetteBuilder: knitr Config/testthat/edition: 3 Language: en-US +LinkingTo: + Rcpp diff --git a/R/combineContigs.R b/R/combineContigs.R index 09f60ea9..30b96def 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -188,9 +188,9 @@ combineBCR <- function(df, } } if (!is.null(samples)) { - out <- modifyBarcodes(df, samples, ID) + out <- modifyBarcodes(df, samples, ID) } else { - out <- df + out <- df } for (i in seq_along(out)) { data2 <- data.frame(out[[i]]) @@ -226,7 +226,7 @@ combineBCR <- function(df, if (!is.null(sample) & !is.null(ID)) { final[[i]]<- final[[i]][, c("barcode", "sample", "ID", heavy_lines[c(1,2,3)], light_lines[c(1,2,3)], CT_lines)] - } + } else if (!is.null(sample) & is.null(ID)) { final[[i]]<- final[[i]][, c("barcode", "sample", heavy_lines[c(1,2,3)], light_lines[c(1,2,3)], CT_lines)] @@ -234,18 +234,19 @@ combineBCR <- function(df, } names <- NULL for (i in seq_along(samples)) { - if (!is.null(samples) & !is.null(ID)) { - c <- paste(samples[i], "_", ID[i], sep="") - } else if (!is.null(samples) & is.null(ID)) { - c <- paste(samples[i], sep="") - } - names <- c(names, c)} + if (!is.null(samples) & !is.null(ID)) { + c <- paste(samples[i], "_", ID[i], sep="") + } else if (!is.null(samples) & is.null(ID)) { + c <- paste(samples[i], sep="") + } + names <- c(names, c) + } names(final) <- names for (i in seq_along(final)) { final[[i]] <- final[[i]][!duplicated(final[[i]]$barcode),] final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ]} - if (removeNA == TRUE) { final <- removingNA(final) } - if (removeMulti == TRUE) { final <- removingMulti(final) } + if (removeNA) { final <- removingNA(final) } + if (removeMulti) { final <- removingMulti(final) } return(final) } diff --git a/R/processing.R b/R/processing.R index 93a2e2ba..4e72aa89 100644 --- a/R/processing.R +++ b/R/processing.R @@ -9,12 +9,16 @@ #' stripBarcode(contig_list[[1]], column = 1, connector = "_", num_connects = 1) #' @export #' @return list with the suffixes of the barcodes removed. -stripBarcode <- function(contigs, column = 1, connector = "_", - num_connects = 3) { - count <- as.data.frame(t(data.frame(strsplit(contigs[,column], - paste("['", connector, "']", sep="")), - stringsAsFactors = FALSE)), - stringsAsFactors = FALSE)[num_connects] +stripBarcode <- function(contigs, column = 1, connector = "_", num_connects = 3) +{ + count <- as.data.frame( + t(data.frame( + strsplit(contigs[,column], paste("['", connector, "']", sep="")), + stringsAsFactors = FALSE + )), + stringsAsFactors = FALSE + )[num_connects] + contigs[,column] <- count return(contigs) } diff --git a/R/scRepertoire-package.R b/R/scRepertoire-package.R new file mode 100644 index 00000000..ce705cf9 --- /dev/null +++ b/R/scRepertoire-package.R @@ -0,0 +1,8 @@ +#' @keywords internal +"_PACKAGE" + +## usethis namespace: start +#' @importFrom Rcpp sourceCpp +#' @useDynLib scRepertoire, .registration = TRUE +## usethis namespace: end +NULL diff --git a/R/utils.R b/R/utils.R index 568b634d..9525cc45 100644 --- a/R/utils.R +++ b/R/utils.R @@ -404,7 +404,7 @@ parseBCR <- function (Con.df, unique_df, data2) { lengthDF <- function(df, cloneCall, chain, group, c1, c2){ Con.df <- NULL names <- names(df) - if (chain == "both") { + if (identical(chain, "both")) { for (i in seq_along(df)) { length <- nchar(gsub("_", "", df[[i]][,cloneCall])) val <- df[[i]][,cloneCall] @@ -417,7 +417,7 @@ lengthDF <- function(df, cloneCall, chain, group, c1, c2){ data <- na.omit(data.frame(length, val, names[i])) colnames(data) <- c("length", "CT", "values") Con.df<- rbind.data.frame(Con.df, data) }} - } else if (chain != "both") { + } else { for (x in seq_along(df)) { df[[x]] <- off.the.chain(df[[x]], chain, cloneCall) strings <- df[[x]][,cloneCall] @@ -438,7 +438,8 @@ lengthDF <- function(df, cloneCall, chain, group, c1, c2){ data <- subset(data, CT != "NA" & CT != "") Con.df<- rbind.data.frame(Con.df, data) }} } -return(Con.df)} + return(Con.df) +} #General combination of nucleotide, aa, and gene sequences for T/B cells assignCT <- function(cellType, Con.df) { diff --git a/R/viz.R b/R/viz.R index 953eed94..e1f85d01 100644 --- a/R/viz.R +++ b/R/viz.R @@ -37,8 +37,10 @@ quantContig <- function(df, split.by = NULL, order = TRUE, exportTable = FALSE) { - if (length(group.by) > 1) { stop("Only one item in the group.by variable can - be listed.") } + + if (length(group.by) > 1) { + stop("Only one item in the group.by variable can be listed.") + } df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) @@ -59,7 +61,7 @@ quantContig <- function(df, location <- which(colnames(df[[i]]) == group.by) Con.df[i,4] <- df[[i]][1,location] } col <- length(unique(Con.df[,group.by])) - if (scale == TRUE) { y <- "scaled" + if (scale) { y <- "scaled" Con.df$scaled <- Con.df$contigs/Con.df$total*100 ylab <- "Percent of Unique Clonotype" diff --git a/inst/CITATION b/inst/CITATION new file mode 100644 index 00000000..aa30aab3 --- /dev/null +++ b/inst/CITATION @@ -0,0 +1,13 @@ +citHeader("To cite scRepertoire in publications use:") + +citEntry( + entry = "Article", + title = "scRepertoire: An R-based toolkit for single-cell immune receptor analysis", + author = {Borcherding, Nicholas and Bormann, Nicholas L and Kraus, Gloria}, + journal = "F1000Research", + volume = 9, + year = 2022, + publisher = Faculty of 1000 Ltd + url = https://doi.org/10.12688/f1000research.22139.2, + textVersion = "Borcherding N, Bormann NL and Kraus G. scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved]. F1000Research 2020, 9:47 (https://doi.org/10.12688/f1000research.22139.2)" +) diff --git a/scRepertoire.Rproj b/scRepertoire.Rproj index 566bd78d..f5d9c6c8 100644 --- a/scRepertoire.Rproj +++ b/scRepertoire.Rproj @@ -5,7 +5,7 @@ SaveWorkspace: Default AlwaysSaveHistory: Default EnableCodeIndexing: Yes -UseSpacesForTab: Yes +UseSpacesForTab: No NumSpacesForTab: 4 Encoding: UTF-8 diff --git a/src/.gitignore b/src/.gitignore new file mode 100644 index 00000000..22034c46 --- /dev/null +++ b/src/.gitignore @@ -0,0 +1,3 @@ +*.o +*.so +*.dll From ac002559eba15b1754dcf9331cb530f957353114 Mon Sep 17 00:00:00 2001 From: Qile Date: Wed, 2 Aug 2023 15:23:19 +0200 Subject: [PATCH 006/142] improved readability in utils --- NAMESPACE | 2 + R/utils.R | 78 +++++++++++++++++++------------------ man/scRepertoire-package.Rd | 15 +++++++ 3 files changed, 57 insertions(+), 38 deletions(-) create mode 100644 man/scRepertoire-package.Rd diff --git a/NAMESPACE b/NAMESPACE index 4955b2f9..bfd24fb5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -33,6 +33,7 @@ export(vizGenes) exportClasses(StartracOut) import(dplyr) import(ggplot2) +importFrom(Rcpp,sourceCpp) importFrom(SeuratObject,Embeddings) importFrom(SeuratObject,Idents) importFrom(SingleCellExperiment,colData) @@ -97,3 +98,4 @@ importFrom(utils,combn) importFrom(utils,head) importFrom(vegan,diversity) importFrom(vegan,estimateR) +useDynLib(scRepertoire, .registration = TRUE) diff --git a/R/utils.R b/R/utils.R index 9525cc45..bd8815d7 100644 --- a/R/utils.R +++ b/R/utils.R @@ -1,4 +1,10 @@ +# readability functions "%!in%" <- Negate("%in%") +is_seurat_object <- function(obj) inherits(obj, "Seurat") +is_se_object <- function(obj) inherits(obj, "SummarizedExperiment") +is_seurat_or_se_object <- function(obj) { + is_seurat_object(obj) || is_se_object(obj) +} #Use to shuffle between chains off.the.chain <- function(dat, chain, cloneCall) { @@ -55,22 +61,20 @@ checkContigs <- function(df) { #' @importFrom dplyr bind_rows bound.input.return <- function(df) { - if (inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { - df <- grabMeta(df) - } else { - df <- bind_rows(df, .id = "element.names") - } - return(df) + if (is_seurat_or_se_object(df)) { + return(grabMeta(df)) + } + bind_rows(df, .id = "element.names") } list.input.return <- function(df, split.by) { - if (inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { - if(is.null(split.by)){ - split.by <- "cluster" - } - df <- expression2List(df, split.by) - } - return(df) + if (is_seurat_or_se_object(df)) { + if(is.null(split.by)){ + split.by <- "cluster" + } + df <- expression2List(df, split.by) + } + df } #Get UMAP or other coordinates @@ -79,9 +83,9 @@ get.coord <- function(sc, reduction) { if (is.null(reduction)) { reduction <- "pca" } - if (inherits(x=sc, what ="Seurat")) { + if (is_seurat_object(sc)) { coord <- sc@reductions[[reduction]]@cell.embeddings - } else if (inherits(x=sc, what ="SummarizedExperiment")) { + } else if (is_se_object(sc)) { coord <- reducedDim(sc, reduction) } return(coord) @@ -89,21 +93,22 @@ get.coord <- function(sc, reduction) { #This is to check the single-cell expression object checkSingleObject <- function(sc) { - if (!inherits(x=sc, what ="Seurat") && - !inherits(x=sc, what ="SummarizedExperiment")){ + if (!is_seurat_or_se_object(sc)){ stop("Object indicated is not of class 'Seurat' or 'SummarizedExperiment', make sure you are using - the correct data.") } + the correct data.") } +} #This is to grab the meta data from a seurat or SCE object #' @importFrom SingleCellExperiment colData #' @importFrom SeuratObject Idents grabMeta <- function(sc) { - if (inherits(x=sc, what ="Seurat")) { + if (is_seurat_object(sc)) { meta <- data.frame(sc[[]], slot(sc, "active.ident")) colnames(meta)[length(meta)] <- "ident" - } else if (inherits(x=sc, what ="SummarizedExperiment")){ + + } else if (is_se_object(sc)){ meta <- data.frame(colData(sc)) rownames(meta) <- sc@colData@rownames clu <- which(colnames(meta) == "ident") @@ -250,16 +255,15 @@ jaccardIndex <- function(df, length, cloneCall, coef_matrix) { df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] for (j in seq_along(length)){ if (i >= j){ next } - else { - df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) - coef_matrix[i,j] <- - overlap/(sum(length(df.i_unique[,cloneCall]), - length(df.j_unique[,cloneCall]))-overlap) - } + + df.j <- df[[j]] + df.j <- df.j[,c("barcode",cloneCall)] + df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] + overlap <- length(intersect(df.i_unique[,cloneCall], + df.j_unique[,cloneCall])) + coef_matrix[i,j] <- + overlap/(sum(length(df.i_unique[,cloneCall]), + length(df.j_unique[,cloneCall]))-overlap) } } return(coef_matrix) @@ -272,14 +276,12 @@ rawIndex <- function(df, length, cloneCall, coef_matrix) { df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] for (j in seq_along(length)){ if (i >= j){ next } - else { - df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) - coef_matrix[i,j] <- overlap - } + df.j <- df[[j]] + df.j <- df.j[,c("barcode",cloneCall)] + df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] + overlap <- length(intersect(df.i_unique[,cloneCall], + df.j_unique[,cloneCall])) + coef_matrix[i,j] <- overlap } } return(coef_matrix) diff --git a/man/scRepertoire-package.Rd b/man/scRepertoire-package.Rd new file mode 100644 index 00000000..8e5ddd84 --- /dev/null +++ b/man/scRepertoire-package.Rd @@ -0,0 +1,15 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/scRepertoire-package.R +\docType{package} +\name{scRepertoire-package} +\alias{scRepertoire} +\alias{scRepertoire-package} +\title{scRepertoire: A toolkit for single-cell immune receptor profiling} +\description{ +scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. +} +\author{ +\strong{Maintainer}: Nick Borcherding \email{ncborch@gmail.com} + +} +\keyword{internal} From 84c7109be39d6fe0cc54273e7330004db6da755b Mon Sep 17 00:00:00 2001 From: Qile Date: Tue, 8 Aug 2023 13:17:18 +0200 Subject: [PATCH 007/142] added seurat command, utils, started Rcpp src --- R/RcppExports.R | 7 ++ R/combineContigs.R | 3 +- R/seuratFunctions.R | 34 ++++++--- R/seurat_command.R | 87 ++++++++++++++++++++++++ R/utils.R | 47 ++++++++++--- src/RcppExports.cpp | 31 +++++++++ src/hello_world.cpp | 6 ++ tests/testthat/test-utils.R | 53 +++++++++++++-- tests/testthat/testdata/utils-testdata.R | 26 +++++++ 9 files changed, 269 insertions(+), 25 deletions(-) create mode 100644 R/RcppExports.R create mode 100644 R/seurat_command.R create mode 100644 src/RcppExports.cpp create mode 100644 src/hello_world.cpp create mode 100644 tests/testthat/testdata/utils-testdata.R diff --git a/R/RcppExports.R b/R/RcppExports.R new file mode 100644 index 00000000..7b828278 --- /dev/null +++ b/R/RcppExports.R @@ -0,0 +1,7 @@ +# Generated by using Rcpp::compileAttributes() -> do not edit by hand +# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +rcpp_hello_world <- function() { + invisible(.Call(`_scRepertoire_rcpp_hello_world`)) +} + diff --git a/R/combineContigs.R b/R/combineContigs.R index 30b96def..2c3e6c07 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -119,7 +119,8 @@ combineTCR <- function(df, final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ]} if (removeNA == TRUE) { final <- removingNA(final)} if (removeMulti == TRUE) { final <- removingMulti(final) } - return(final) } + return(final) +} #' Combining the list of B Cell Receptor contigs #' diff --git a/R/seuratFunctions.R b/R/seuratFunctions.R index ff5fe49b..7af55418 100644 --- a/R/seuratFunctions.R +++ b/R/seuratFunctions.R @@ -51,16 +51,21 @@ #' information #' -combineExpression <- function(df, - sc, - cloneCall="strict", - chain = "both", - group.by="none", - proportion = TRUE, - filterNA = FALSE, - cloneTypes=c(Rare = 1e-4, Small = 0.001, - Medium = 0.01, Large = 0.1, Hyperexpanded = 1), - addLabel = FALSE) { +combineExpression <- function( + df, + sc, + cloneCall="strict", + chain = "both", + group.by="none", + proportion = TRUE, + filterNA = FALSE, + cloneTypes = c( + Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1 + ), + addLabel = FALSE +) { + call_time <- Sys.time() + options( dplyr.summarise.inform = FALSE ) cloneTypes <- c(None = 0, cloneTypes) df <- checkList(df) @@ -149,7 +154,7 @@ combineExpression <- function(df, prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() functions" - if (inherits(x=sc, what ="Seurat")) { + if (is_seurat_object(sc)) { if (length(which(rownames(PreMeta) %in% rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { warning(warn_str) @@ -166,6 +171,13 @@ combineExpression <- function(df, } if (filterNA) { sc <- filteringNA(sc) } sc$cloneType <- factor(sc$cloneType, levels = rev(names(cloneTypes))) + + if(is_seurat_object(sc)) { + sc@commands[["combineExpression"]] <- make_screp_seurat_cmd( + call_time, sc@active.assay + ) + } + sc } diff --git a/R/seurat_command.R b/R/seurat_command.R new file mode 100644 index 00000000..b974954b --- /dev/null +++ b/R/seurat_command.R @@ -0,0 +1,87 @@ +# function needed for make_screp_seurat_cmd +seurat_extractfield <- function(string, field = 1, delim = "_") { + fields <- as.numeric( + x = unlist(x = strsplit(x = as.character(x = field), split = ",")) + ) + if (length(x = fields) == 1) { + return(strsplit(x = string, split = delim)[[1]][field]) + } + return(paste( + strsplit(x = string, split = delim)[[1]][fields], collapse = delim + )) +} + +# seurat's command adding but if an input is a dataframe or list of dataframes, +# completely omits them to save memory. +make_screp_seurat_cmd <- function(call_time, assay) { + if (as.character(x = sys.calls()[[1]])[1] == "do.call") { + call_string <- deparse(expr = sys.calls()[[1]]) + command_name <- as.character(x = sys.calls()[[1]])[2] + } else { + command_name <- as.character( + x = deparse(expr = sys.calls()[[sys.nframe() - 1]]) + ) + command_name <- gsub( + pattern = "\\.Seurat", + replacement = "", + x = command_name + ) + call_string <- command_name + command_name <- seurat_extractfield( + string = command_name, + field = 1, + delim = "\\(" + ) + } + + argnames <- names(x = formals(fun = sys.function(which = sys.parent(n = 1)))) + argnames <- grep( + pattern = "object", + x = argnames, + invert = TRUE, + value = TRUE + ) + argnames <- grep( + pattern = "anchorset", + x = argnames, + invert = TRUE, + value = TRUE + ) + argnames <- grep( + pattern = "\\.\\.\\.", + x = argnames, + invert = TRUE, + value = TRUE + ) + + params <- list() + p.env <- parent.frame(n = 1) + argnames <- intersect(x = argnames, y = ls(name = p.env)) + for (arg in argnames) { + param_value <- get(x = arg, envir = p.env) + if (is_seurat_object(param_value) || is_df_or_list_of_df(param_value)) { + next + } + params[[arg]] <- param_value + } + + command_name <- sub( + pattern = "[\\.]+$", + replacement = "", + x = command_name, + perl = TRUE + ) + command_name <- sub( + pattern = "\\.\\.", replacement = "\\.", x = command_name, perl = TRUE + ) + + # return the command object + methods::new( + Class = 'SeuratCommand', + name = command_name, + params = params, + time.stamp = call_time, + call.string = call_string, + assay.used = assay + ) +} diff --git a/R/utils.R b/R/utils.R index bd8815d7..4d2be726 100644 --- a/R/utils.R +++ b/R/utils.R @@ -45,18 +45,36 @@ groupList <- function(df, group.by) { return(df) } -#Ensure df is in list format +# Ensure df is in list format checkList <- function(df) { - df <- if(is(df)[1] != "list") list(df) else df - return(df) + df <- tryCatch( + { + if (is(df)[1] != "list") { + df <- list(df) + } + df + }, + error = function(e) { + stop( + "Please ensure that the input consists of at least one dataframe" + ) + } + ) + df } +#checkList <- function(df) { +# df <- if(is(df)[1] != "list") list(df) else df +# return(df) +#} + checkContigs <- function(df) { - df <- lapply(seq_len(length(df)), function(x) { - df[[x]] <- if(is(df[[x]])[1] != "data.frame") as.data.frame(df[[x]]) else df[[x]] - df[[x]][df[[x]] == ""] <- NA - df[[x]] - }) + df <- lapply(seq_len(length(df)), function(x) { + df[[x]] <- if(!is.data.frame(df[[x]])) as.data.frame(df[[x]]) else df[[x]] + df[[x]][df[[x]] == ""] <- NA + df[[x]] + }) + df } #' @importFrom dplyr bind_rows @@ -518,3 +536,16 @@ select.gene <- function(df, chain, gene, label) { return(df) } +# check if object is a dataframe or list of dataframes +is_df_or_list_of_df <- function(x) { + if (is.data.frame(x)) { + return(TRUE) + } else if (is.list(x)) { + if (length(x) == 0) { + return(FALSE) + } + return(all(sapply(x, is.data.frame))) + } else { + return(FALSE) + } +} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp new file mode 100644 index 00000000..0dc7fc4c --- /dev/null +++ b/src/RcppExports.cpp @@ -0,0 +1,31 @@ +// Generated by using Rcpp::compileAttributes() -> do not edit by hand +// Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 + +#include + +using namespace Rcpp; + +#ifdef RCPP_USE_GLOBAL_ROSTREAM +Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); +Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); +#endif + +// rcpp_hello_world +void rcpp_hello_world(); +RcppExport SEXP _scRepertoire_rcpp_hello_world() { +BEGIN_RCPP + Rcpp::RNGScope rcpp_rngScope_gen; + rcpp_hello_world(); + return R_NilValue; +END_RCPP +} + +static const R_CallMethodDef CallEntries[] = { + {"_scRepertoire_rcpp_hello_world", (DL_FUNC) &_scRepertoire_rcpp_hello_world, 0}, + {NULL, NULL, 0} +}; + +RcppExport void R_init_scRepertoire(DllInfo *dll) { + R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); + R_useDynamicSymbols(dll, FALSE); +} diff --git a/src/hello_world.cpp b/src/hello_world.cpp new file mode 100644 index 00000000..02307bba --- /dev/null +++ b/src/hello_world.cpp @@ -0,0 +1,6 @@ +#include + +// [[Rcpp::export]] +void rcpp_hello_world() { + Rcpp::Rcout << "hello, world!" << "\n"; +} diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index d08471ae..54c73563 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -1,10 +1,53 @@ +# test script for utils.R - testcases are NOT comprehensive! + test_that("'%!in%' works", { - vector <- c(1, 2, 3, 4, 5) + v <- c(1, 2, 3, 4, 5) - expect_true(0 %!in% vector) - expect_true(6 %!in% vector) - expect_false(3 %!in% vector) - expect_false(5 %!in% vector) + expect_true(0 %!in% v) + expect_true(6 %!in% v) + expect_false(3 %!in% v) + expect_false(5 %!in% v) expect_true(1 %!in% NULL) expect_true(list(1) %!in% NA) }) + +# off.the.chain +# checkBlanks +# groupList + +test_that("checkList works", { + data("contig_list") + expect_identical(checkList(contig_list), contig_list) + expect_identical(checkList(contig_list[[1]]), contig_list[[1]]) + # no idea what to put to make the stop message happen. perhaps with data.table(contig_list) ? but then it shouldnt fail :P +}) + +test_that("checkContigs works", { + input <- list( + df1 = data.frame(a = c("x", "", "z"), b = c("1", "2", "3")), + df2 = data.frame(c = c("foo", "bar", ""), d = c("", "spam", "eggs")) + ) + expected <- checkContigs(input) + expect_true(is.list(expected)) + expect_true(is.data.frame(expected[[1]])) + expect_equal(expected[[1]]$a, c("x", NA, "z")) + expect_equal(expected[[1]]$b, c("1", "2", "3")) + expect_true(is.data.frame(expected[[2]])) + expect_equal(expected[[2]]$c, c("foo", "bar", NA)) + expect_equal(expected[[2]]$d, c(NA, "spam", "eggs")) +}) + +#bound.input.return +#get.coord +#checkSingleObject +#grabMeta +#modifyBarcodes +#removingNA +#removingMulti + +test_that("filteringMulti works", { + source("testdata/utils-testdata.R") + expect_identical(filteringMulti(head(contig_list[[1]])), filteringMulti_expected) +}) + +# TODO: tests for the rest of the functions in R/utils.R diff --git a/tests/testthat/testdata/utils-testdata.R b/tests/testthat/testdata/utils-testdata.R new file mode 100644 index 00000000..df72909f --- /dev/null +++ b/tests/testthat/testdata/utils-testdata.R @@ -0,0 +1,26 @@ +# output of dput(filteringMulti(head(contig_list[[1]]))) +filteringMulti_expected <- structure(list(barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", + "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), is_cell = c(TRUE, TRUE, + TRUE, TRUE), contig_id = c("AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_2", + "AAACCTGAGCATCATC-1_contig_1", "AAACCTGAGTGGTCCC-1_contig_1"), + high_confidence = c(TRUE, TRUE, TRUE, TRUE), length = c(705L, + 567L, 693L, 593L), chain = c("TRB", "TRA", "TRB", "TRB"), + v_gene = c("TRBV20-1", "TRAV12-1", "TRBV5-1", "TRBV7-9"), + d_gene = c("TRBD1", "None", "TRBD2", "TRBD1"), j_gene = c("TRBJ1-5", + "TRAJ37", "TRBJ2-2", "TRBJ2-5"), c_gene = c("TRBC1", "TRAC", + "TRBC2", "TRBC2"), full_length = c(TRUE, TRUE, TRUE, TRUE + ), productive = c("TRUE", "TRUE", "TRUE", "TRUE"), cdr3 = c("CSASMGPVVSNQPQHF", + "CVVNDEGSSNTGKLIF", "CASSWSGAGDGELFF", "CASSPSEGGRQETQYF" + ), cdr3_nt = c("TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", + "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", + "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC"), reads = c(16718L, + 18297L, 26719L, 11218L), umis = c(6L, 6L, 11L, 6L), raw_clonotype_id = c("clonotype96", + "clonotype97", "clonotype97", "clonotype98"), raw_consensus_id = c("clonotype96_consensus_1", + "clonotype97_consensus_1", "clonotype97_consensus_2", "clonotype98_consensus_1" + )), class = c("grouped_df", "tbl_df", "tbl", "data.frame" + ), row.names = c(NA, -4L), groups = structure(list(barcode = c("AAACCTGAGAGCTGGT", + "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), + chain = c("TRB", "TRA", "TRB", "TRB"), .rows = structure(list( + 1L, 2L, 3L, 4L), ptype = integer(0), class = c("vctrs_list_of", + "vctrs_vctr", "list"))), row.names = c(NA, -4L), .drop = TRUE, class = c("tbl_df", + "tbl", "data.frame"))) From 01f2ebd579968d83033083b1156a15e8be7b8f0a Mon Sep 17 00:00:00 2001 From: Qile Date: Tue, 8 Aug 2023 17:31:49 +0200 Subject: [PATCH 008/142] added many utils testcases and testdata --- R/combineContigs.R | 34 +++--- R/seuratFunctions.R | 4 +- R/seurat_command.R | 7 +- R/utils.R | 4 +- tests/testthat/test-utils.R | 137 ++++++++++++++++++++--- tests/testthat/testdata/utils-testdata.R | 130 ++++++++++++++++----- 6 files changed, 258 insertions(+), 58 deletions(-) diff --git a/R/combineContigs.R b/R/combineContigs.R index 2c3e6c07..f5e7dccc 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -13,20 +13,25 @@ data1_lines <- c("TCR1", "cdr3", "cdr3_nt") data2_lines <- c("TCR2", "cdr3", "cdr3_nt") CT_lines <- c("CTgene", "CTnt", "CTaa", "CTstrict") -utils::globalVariables(c("heavy_lines", "light_lines", "l_lines", "k_lines", - "h_lines", "tcr1_lines", "tcr2_lines", "data1_lines", - "data2_lines", "CT_lines")) +utils::globalVariables(c( + "heavy_lines", "light_lines", "l_lines", "k_lines", "h_lines", "tcr1_lines", + "tcr2_lines", "data1_lines", "data2_lines", "CT_lines" +)) -#' Combining the list of T Cell Receptor contigs +#' @title Combining the list of T Cell Receptor contigs #' -#' This function consolidates a list of TCR sequencing results to the level of -#' the individual cell barcodes. Using the samples and ID parameters, the -#' function will add the strings as prefixes to prevent issues with repeated -#' barcodes. The resulting new barcodes will need to match the Seurat or SCE -#' object in order to use, \code{\link{combineExpression}}. Several -#' levels of filtering exist - remove or filterMulti are parameters that -#' control how the function deals with barcodes with multiple chains -#' recovered. +#' @description This function consolidates a list of TCR sequencing results to +#' the level of the individual cell barcodes. Using the samples and ID +#' parameters, the function will add the strings as prefixes to prevent issues +#' with repeated barcodes. The resulting new barcodes will need to match the +#' Seurat or SCE object in order to use, \code{\link{combineExpression}}. +#' Several levels of filtering exist - `removeNA`, `removeMulti`, or +#' `filterMulti` are parameters that control how the function deals with +#' barcodes with multiple chains recovered. +#' +#' @details For single-sample TCR sequencing experiments, where the input is +#' just a single data.frame, the function will add the consolidated information +#' columns after the existing raw clonotype data #' #' @examples #' combineTCR(contig_list, @@ -44,6 +49,7 @@ utils::globalVariables(c("heavy_lines", "light_lines", "l_lines", "k_lines", #' @import dplyr #' @export #' @return List of clonotypes for individual cell barcodes +#' combineTCR <- function(df, samples = NULL, ID = NULL, @@ -134,8 +140,8 @@ combineTCR <- function(df, #' and the corresponding v-gene. This index automatically calculates #' the Levenshtein distance between sequences with the same V gene and will #' index sequences with <= 0.15 normalized Levenshtein distance with the same -#' ID. After which, clonotype clusters are called using the igraph -#' component() function. Clonotype that are clustered across multiple +#' ID. After which, clonotype clusters are called using the +#' `igraph:: component()` function. Clonotype that are clustered across multiple #' sequences will then be labeled with "LD" in the CTstrict header. #' #' @examples diff --git a/R/seuratFunctions.R b/R/seuratFunctions.R index 7af55418..e752c04e 100644 --- a/R/seuratFunctions.R +++ b/R/seuratFunctions.R @@ -50,7 +50,6 @@ #' @return seurat or SingleCellExperiment object with attached clonotype #' information #' - combineExpression <- function( df, sc, @@ -324,7 +323,8 @@ alluvialClonotypes <- function(sc, plot <- plot + facet_wrap(.~lodes[,facet], scales="free_y") } else if (length(facet) == 0) { plot <- plot } plot <- plot + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) - return(plot)} + return(plot) +} #' Visualize the number of single cells with clonotype frequencies by cluster diff --git a/R/seurat_command.R b/R/seurat_command.R index b974954b..fa2e3206 100644 --- a/R/seurat_command.R +++ b/R/seurat_command.R @@ -1,3 +1,7 @@ +# script of slightly modified versions of the seurat command script in Seurat +# commands should be added to the seurat@command attribute if the seurat object +# is modified. + # function needed for make_screp_seurat_cmd seurat_extractfield <- function(string, field = 1, delim = "_") { fields <- as.numeric( @@ -11,9 +15,10 @@ seurat_extractfield <- function(string, field = 1, delim = "_") { )) } -# seurat's command adding but if an input is a dataframe or list of dataframes, +# seurat's command adding but if a param is a dataframe or list of dataframes, # completely omits them to save memory. make_screp_seurat_cmd <- function(call_time, assay) { + if (as.character(x = sys.calls()[[1]])[1] == "do.call") { call_string <- deparse(expr = sys.calls()[[1]]) command_name <- as.character(x = sys.calls()[[1]])[2] diff --git a/R/utils.R b/R/utils.R index 4d2be726..d65134be 100644 --- a/R/utils.R +++ b/R/utils.R @@ -488,7 +488,7 @@ makeGenes <- function(cellType, data2, chain1, chain2) { mutate(TCR2 = ifelse(chain %in% c("TRB", "TRD"), str_c(str_replace_na(v_gene), str_replace_na(d_gene), str_replace_na(j_gene), str_replace_na(c_gene), sep = "."), NA)) } - else { + else { # assume BCR (`c("B")`) heavy <- data2[data2$chain == "IGH",] %>% mutate(IGHct = str_c(str_replace_na(v_gene), str_replace_na(d_gene), str_replace_na(j_gene), str_replace_na(c_gene), sep = ".")) kappa <- data2[data2$chain == "IGK",] %>% @@ -497,7 +497,7 @@ makeGenes <- function(cellType, data2, chain1, chain2) { mutate(IGLct = str_c(str_replace_na(v_gene), str_replace_na(j_gene), str_replace_na(c_gene), sep = ".")) data2 <- bind_rows(heavy, kappa, lambda) } - return(data2) + data2 } short.check <- function(df, cloneCall) { diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index 54c73563..cd4c37b9 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -1,4 +1,5 @@ # test script for utils.R - testcases are NOT comprehensive! +source("testdata/utils-testdata.R") test_that("'%!in%' works", { v <- c(1, 2, 3, 4, 5) @@ -11,14 +12,15 @@ test_that("'%!in%' works", { expect_true(list(1) %!in% NA) }) -# off.the.chain -# checkBlanks -# groupList +# TODO off.the.chain +# TODO checkBlanks +# TODO groupList test_that("checkList works", { data("contig_list") expect_identical(checkList(contig_list), contig_list) - expect_identical(checkList(contig_list[[1]]), contig_list[[1]]) + expect_identical(checkList(contig_list[[1]]), list(contig_list[[1]])) + expect_identical(checkList(contig_list[[1]])[[1]], contig_list[[1]]) # no idea what to put to make the stop message happen. perhaps with data.table(contig_list) ? but then it shouldnt fail :P }) @@ -37,17 +39,124 @@ test_that("checkContigs works", { expect_equal(expected[[2]]$d, c(NA, "spam", "eggs")) }) -#bound.input.return -#get.coord -#checkSingleObject -#grabMeta -#modifyBarcodes -#removingNA -#removingMulti +# TODO bound.input.return +# TODO get.coord +# TODO checkSingleObject +# TODO grabMeta + +# Test cases +test_that("Modifying barcodes without ID works correctly", { + # Simulated dataframes for testing + df1 <- data.frame( + barcode = c("A", "B", "C"), + value = c(10, 20, 30) + ) + + df2 <- data.frame( + barcode = c("X", "Y", "Z"), + value = c(100, 200, 300) + ) + + df_list <- list(df1, df2) + + samples <- c("sample1", "sample2") + modified_data <- modifyBarcodes(df_list, samples, NULL) + + expected_modified_data <- list( + data.frame( + barcode = c("sample1_A", "sample1_B", "sample1_C"), + value = c(10, 20, 30) + ), + data.frame( + barcode = c("sample2_X", "sample2_Y", "sample2_Z"), + value = c(100, 200, 300) + ) + ) + + expect_identical(modified_data, expected_modified_data) +}) + +test_that("Modifying barcodes with ID works correctly", { + # Simulated dataframes for testing + df1 <- data.frame( + barcode = c("A", "B", "C"), + value = c(10, 20, 30) + ) + + df2 <- data.frame( + barcode = c("X", "Y", "Z"), + value = c(100, 200, 300) + ) + + df_list <- list(df1, df2) + + samples <- c("sample3", "sample4") + ID <- c("id1", "id2") + modified_data <- modifyBarcodes(df_list, samples, ID) + + expected_modified_data <- list( + data.frame( + barcode = c("sample3_id1_A", "sample3_id1_B", "sample3_id1_C"), + value = c(10, 20, 30) + ), + data.frame( + barcode = c("sample4_id2_X", "sample4_id2_Y", "sample4_id2_Z"), + value = c(100, 200, 300) + ) + ) + + expect_identical(modified_data, expected_modified_data) +}) + +# TODO removingNA +# TODO removingMulti test_that("filteringMulti works", { - source("testdata/utils-testdata.R") - expect_identical(filteringMulti(head(contig_list[[1]])), filteringMulti_expected) + expect_identical( + filteringMulti(head(contig_list[[1]])), + filteringMulti_expected + ) }) -# TODO: tests for the rest of the functions in R/utils.R +# TODO filteringNA +# TODO diversityCall +# TODO parseContigs +# TODO morisitaIndex +# TODO jaccardIndex +# TODO rawIndex +# TODO overlapIndex +# TODO quiet +# TODO theCall +# TODO parseTCR +# TODO parseBCR +# TODO lengthDF +# TODO assignCT + +test_that("makeGenes works for cellType T", { + expect_identical( + makeGenes("T", makeGenes_T_input), + makeGenes_T_expected + ) +}) + +# TODO makesGenes (cellType B) +# TODO short.check +# TODO select.gene + +test_that("Check if object is a dataframe or list of dataframes", { + df <- data.frame(x = 1:5, y = letters[1:5]) + list_of_dfs <- list( + data.frame(a = 1:3, b = letters[1:3]), + data.frame(x = 4:6, y = letters[4:6]) + ) + mixed_list <- list( + data.frame(a = 1:3, b = letters[1:3]), + "not a dataframe" + ) + + expect_true(is_df_or_list_of_df(df)) + expect_true(is_df_or_list_of_df(list_of_dfs)) + expect_false(is_df_or_list_of_df(mixed_list)) + expect_false(is_df_or_list_of_df(list())) + expect_false(is_df_or_list_of_df(c(1, 2, 3, 4, 5))) +}) diff --git a/tests/testthat/testdata/utils-testdata.R b/tests/testthat/testdata/utils-testdata.R index df72909f..78f76488 100644 --- a/tests/testthat/testdata/utils-testdata.R +++ b/tests/testthat/testdata/utils-testdata.R @@ -1,26 +1,106 @@ +# script for some of the testing data used in tests/testthat/test-utils.R +# produced by using dput() and chatGPT + # output of dput(filteringMulti(head(contig_list[[1]]))) -filteringMulti_expected <- structure(list(barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", - "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), is_cell = c(TRUE, TRUE, - TRUE, TRUE), contig_id = c("AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_2", - "AAACCTGAGCATCATC-1_contig_1", "AAACCTGAGTGGTCCC-1_contig_1"), - high_confidence = c(TRUE, TRUE, TRUE, TRUE), length = c(705L, - 567L, 693L, 593L), chain = c("TRB", "TRA", "TRB", "TRB"), - v_gene = c("TRBV20-1", "TRAV12-1", "TRBV5-1", "TRBV7-9"), - d_gene = c("TRBD1", "None", "TRBD2", "TRBD1"), j_gene = c("TRBJ1-5", - "TRAJ37", "TRBJ2-2", "TRBJ2-5"), c_gene = c("TRBC1", "TRAC", - "TRBC2", "TRBC2"), full_length = c(TRUE, TRUE, TRUE, TRUE - ), productive = c("TRUE", "TRUE", "TRUE", "TRUE"), cdr3 = c("CSASMGPVVSNQPQHF", - "CVVNDEGSSNTGKLIF", "CASSWSGAGDGELFF", "CASSPSEGGRQETQYF" - ), cdr3_nt = c("TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", - "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", - "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC"), reads = c(16718L, - 18297L, 26719L, 11218L), umis = c(6L, 6L, 11L, 6L), raw_clonotype_id = c("clonotype96", - "clonotype97", "clonotype97", "clonotype98"), raw_consensus_id = c("clonotype96_consensus_1", - "clonotype97_consensus_1", "clonotype97_consensus_2", "clonotype98_consensus_1" - )), class = c("grouped_df", "tbl_df", "tbl", "data.frame" - ), row.names = c(NA, -4L), groups = structure(list(barcode = c("AAACCTGAGAGCTGGT", - "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), - chain = c("TRB", "TRA", "TRB", "TRB"), .rows = structure(list( - 1L, 2L, 3L, 4L), ptype = integer(0), class = c("vctrs_list_of", - "vctrs_vctr", "list"))), row.names = c(NA, -4L), .drop = TRUE, class = c("tbl_df", - "tbl", "data.frame"))) +filteringMulti_expected <- structure(list( + barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), + is_cell = c(TRUE, TRUE, TRUE, TRUE), + contig_id = c("AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_2", "AAACCTGAGCATCATC-1_contig_1", "AAACCTGAGTGGTCCC-1_contig_1"), + high_confidence = c(TRUE, TRUE, TRUE, TRUE), + length = c(705L, 567L, 693L, 593L), + chain = c("TRB", "TRA", "TRB", "TRB"), + v_gene = c("TRBV20-1", "TRAV12-1", "TRBV5-1", "TRBV7-9"), + d_gene = c("TRBD1", "None", "TRBD2", "TRBD1"), + j_gene = c("TRBJ1-5", "TRAJ37", "TRBJ2-2", "TRBJ2-5"), + c_gene = c("TRBC1", "TRAC", "TRBC2", "TRBC2"), + full_length = c(TRUE, TRUE, TRUE, TRUE), + productive = c("TRUE", "TRUE", "TRUE", "TRUE"), + cdr3 = c("CSASMGPVVSNQPQHF", "CVVNDEGSSNTGKLIF", "CASSWSGAGDGELFF", "CASSPSEGGRQETQYF"), + cdr3_nt = c("TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", + "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", + "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", + "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC"), + reads = c(16718L, 18297L, 26719L, 11218L), + umis = c(6L, 6L, 11L, 6L), + raw_clonotype_id = c("clonotype96", "clonotype97", "clonotype97", "clonotype98"), + raw_consensus_id = c("clonotype96_consensus_1", "clonotype97_consensus_1", "clonotype97_consensus_2", "clonotype98_consensus_1")), + class = c("grouped_df", "tbl_df", "tbl", "data.frame"), + row.names = c(NA, -4L), + groups = structure(list( + barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), + chain = c("TRB", "TRA", "TRB", "TRB"), + .rows = structure(list(1L, 2L, 3L, 4L), + ptype = integer(0), + class = c("vctrs_list_of", "vctrs_vctr", "list")) + ), row.names = c(NA, -4L), .drop = TRUE, class = c("tbl_df", "tbl", "data.frame")) +) + +makeGenes_T_input <- structure(list( + barcode = c( + "AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", + "AAACCTGAGTGGTCCC", "AAACCTGCAAACGCGA", "AAACCTGCAAACGCGA" + ), + is_cell = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), + contig_id = c( + "AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_1", + "AAACCTGAGCATCATC-1_contig_2", "AAACCTGAGTGGTCCC-1_contig_1", + "AAACCTGCAAACGCGA-1_contig_1", "AAACCTGCAAACGCGA-1_contig_3" + ), + high_confidence = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), + length = c(705L, 693L, 567L, 593L, 659L, 514L), + chain = c("TRB", "TRB", "TRA", "TRB", "TRB", "TRA"), + v_gene = c("TRBV20-1", "TRBV5-1", "TRAV12-1", "TRBV7-9", "TRBV2", "TRAV29DV5"), + d_gene = c("TRBD1", "TRBD2", "None", "TRBD1", "TRBD1", "None"), + j_gene = c("TRBJ1-5", "TRBJ2-2", "TRAJ37", "TRBJ2-5", "TRBJ1-6", "TRAJ22"), + c_gene = c("TRBC1", "TRBC2", "TRAC", "TRBC2", "TRBC1", "TRAC"), + full_length = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), + productive = c("TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), + cdr3 = c( + "CSASMGPVVSNQPQHF", "CASSWSGAGDGELFF", "CVVNDEGSSNTGKLIF", + "CASSPSEGGRQETQYF", "CASRVQGNRGSPLHF", "CAASGYGSARQLTF" + ), + cdr3_nt = c( + "TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", + "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", + "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", + "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC", + "TGTGCCAGCAGGGTACAGGGTAATAGGGGTTCACCCCTCCACTTT", + "TGTGCAGCAAGCGGTTACGGTTCTGCAAGGCAACTGACCTTT" + ), + reads = c(16718L, 26719L, 18297L, 11218L, 10891L, 874L), + umis = c(6L, 11L, 6L, 6L, 4L, 1L), + raw_clonotype_id = c( + "clonotype96", "clonotype97", "clonotype97", + "clonotype98", "clonotype99", "clonotype99" + ), + raw_consensus_id = c( + "clonotype96_consensus_1", "clonotype97_consensus_2", + "clonotype97_consensus_1", "clonotype98_consensus_1", + "clonotype99_consensus_1", "clonotype99_consensus_2" + ) +), row.names = c(1L, 3L, 4L, 6L, 8L, 9L), class = "data.frame") + +makeGenes_T_expected <- structure(list( + barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC", "AAACCTGCAAACGCGA", "AAACCTGCAAACGCGA"), + is_cell = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), + contig_id = c("AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_1", "AAACCTGAGCATCATC-1_contig_2", "AAACCTGAGTGGTCCC-1_contig_1", "AAACCTGCAAACGCGA-1_contig_1", "AAACCTGCAAACGCGA-1_contig_3"), + high_confidence = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), + length = c(705L, 693L, 567L, 593L, 659L, 514L), + chain = c("TRB", "TRB", "TRA", "TRB", "TRB", "TRA"), + v_gene = c("TRBV20-1", "TRBV5-1", "TRAV12-1", "TRBV7-9", "TRBV2", "TRAV29DV5"), + d_gene = c("TRBD1", "TRBD2", "None", "TRBD1", "TRBD1", "None"), + j_gene = c("TRBJ1-5", "TRBJ2-2", "TRAJ37", "TRBJ2-5", "TRBJ1-6", "TRAJ22"), + c_gene = c("TRBC1", "TRBC2", "TRAC", "TRBC2", "TRBC1", "TRAC"), + full_length = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), + productive = c("TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), + cdr3 = c("CSASMGPVVSNQPQHF", "CASSWSGAGDGELFF", "CVVNDEGSSNTGKLIF", "CASSPSEGGRQETQYF", "CASRVQGNRGSPLHF", "CAASGYGSARQLTF"), + cdr3_nt = c("TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC", "TGTGCCAGCAGGGTACAGGGTAATAGGGGTTCACCCCTCCACTTT", "TGTGCAGCAAGCGGTTACGGTTCTGCAAGGCAACTGACCTTT"), + reads = c(16718L, 26719L, 18297L, 11218L, 10891L, 874L), + umis = c(6L, 11L, 6L, 6L, 4L, 1L), + raw_clonotype_id = c("clonotype96", "clonotype97", "clonotype97", "clonotype98", "clonotype99", "clonotype99"), + raw_consensus_id = c("clonotype96_consensus_1", "clonotype97_consensus_2", "clonotype97_consensus_1", "clonotype98_consensus_1", "clonotype99_consensus_1", "clonotype99_consensus_2"), + TCR1 = c(NA, NA, "TRAV12-1.TRAJ37.TRAC", NA, NA, "TRAV29DV5.TRAJ22.TRAC"), + TCR2 = c("TRBV20-1.TRBD1.TRBJ1-5.TRBC1", "TRBV5-1.TRBD2.TRBJ2-2.TRBC2", NA, "TRBV7-9.TRBD1.TRBJ2-5.TRBC2", "TRBV2.TRBD1.TRBJ1-6.TRBC1", NA)), + row.names = c(1L, 3L, 4L, 6L, 8L, 9L), + class = "data.frame" +) From 9084df606e18f7b5d322547220e9c21d3ef3450f Mon Sep 17 00:00:00 2001 From: Qile Date: Tue, 8 Aug 2023 21:31:48 +0200 Subject: [PATCH 009/142] more tests --- .github/workflows/R-CMD-check.yaml | 2 - DESCRIPTION | 2 +- R/combineContigs.R | 31 ++--- R/utils.R | 47 ++++---- man/combineBCR.Rd | 4 +- man/combineTCR.Rd | 21 ++-- tests/testthat/helper-testing_functions.R | 3 + tests/testthat/test-combineContigs.R | 18 +++ tests/testthat/test-utils.R | 41 ++----- .../combineTCR_list_expected.rds | Bin 0 -> 1192 bytes tests/testthat/testdata/utils-testdata.R | 106 ------------------ tests/testthat/testdata/utils/df_list.rds | Bin 0 -> 193 bytes .../utils/filteringMulti_expected.rds | Bin 0 -> 771 bytes .../testdata/utils/makeGenes_T_expected.rds | Bin 0 -> 856 bytes .../testdata/utils/makeGenes_T_input.rds | Bin 0 -> 772 bytes 15 files changed, 89 insertions(+), 186 deletions(-) create mode 100644 tests/testthat/helper-testing_functions.R create mode 100644 tests/testthat/test-combineContigs.R create mode 100644 tests/testthat/testdata/combineContigs/combineTCR_list_expected.rds delete mode 100644 tests/testthat/testdata/utils-testdata.R create mode 100644 tests/testthat/testdata/utils/df_list.rds create mode 100644 tests/testthat/testdata/utils/filteringMulti_expected.rds create mode 100644 tests/testthat/testdata/utils/makeGenes_T_expected.rds create mode 100644 tests/testthat/testdata/utils/makeGenes_T_input.rds diff --git a/.github/workflows/R-CMD-check.yaml b/.github/workflows/R-CMD-check.yaml index fde02ff5..6695bbd6 100644 --- a/.github/workflows/R-CMD-check.yaml +++ b/.github/workflows/R-CMD-check.yaml @@ -20,9 +20,7 @@ jobs: config: - {os: macos-latest, r: 'release'} - {os: windows-latest, r: 'release'} - - {os: ubuntu-latest, r: 'devel', http-user-agent: 'release'} - {os: ubuntu-latest, r: 'release'} - - {os: ubuntu-latest, r: 'oldrel-1'} env: GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} diff --git a/DESCRIPTION b/DESCRIPTION index f04f07bb..15945e03 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -12,6 +12,7 @@ biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Seq Depends: ggplot2, R (>= 4.0) + Seurat Imports: stringdist, dplyr, @@ -33,7 +34,6 @@ Imports: tidygraph, SeuratObject, stats, - Seurat, Rcpp Suggests: knitr, diff --git a/R/combineContigs.R b/R/combineContigs.R index f5e7dccc..83837a9a 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -89,10 +89,10 @@ combineTCR <- function(df, } else { out <- df } - for (i in seq_along(out)) { + for (i in seq_along(out)) { # ideally the nested code could be in a function for a better development/testing experience data2 <- out[[i]] data2 <- makeGenes(cellType = "T", data2) - unique_df <- unique(data2$barcode) + unique_df <- unique(data2$barcode) # could potentially display % here Con.df <- data.frame(matrix(NA, length(unique_df), 7)) colnames(Con.df) <- c("barcode",tcr1_lines, tcr2_lines) Con.df$barcode <- unique_df @@ -110,22 +110,23 @@ combineTCR <- function(df, } final[[i]] <- data3 } - names <- NULL + name_vector <- character(length(samples)) for (i in seq_along(samples)) { - if (!is.null(samples) & !is.null(ID)) { - c <- paste(samples[i], "_", ID[i], sep="") - } else if (!is.null(samples) & is.null(ID)) { - c <- paste(samples[i], sep="") - } - names <- c(names, c) + if (!is.null(samples) & !is.null(ID)) { + curr <- paste(samples[i], "_", ID[i], sep="") + } else if (!is.null(samples) & is.null(ID)) { + curr <- paste(samples[i], sep="") + } + name_vector[i] <- curr } - names(final) <- names + names(final) <- name_vector for (i in seq_along(final)){ - final[[i]]<-final[[i]][!duplicated(final[[i]]$barcode),] - final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ]} - if (removeNA == TRUE) { final <- removingNA(final)} - if (removeMulti == TRUE) { final <- removingMulti(final) } - return(final) + final[[i]]<-final[[i]][!duplicated(final[[i]]$barcode),] + final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ] + } + if (removeNA) { final <- removingNA(final)} + if (removeMulti) { final <- removingMulti(final) } + final } #' Combining the list of B Cell Receptor contigs diff --git a/R/utils.R b/R/utils.R index d65134be..d5056256 100644 --- a/R/utils.R +++ b/R/utils.R @@ -357,26 +357,32 @@ parseTCR <- function(Con.df, unique_df, data2) { barcode.i <- Con.df$barcode[y] location.i <- which(barcode.i == data2$barcode) for (z in seq_along(location.i)) { - where.chain <- data2[location.i[z],"chain"] - - if (where.chain == "TRA" | where.chain == "TRG") { - if(is.na(Con.df[y,"TCR1"])) { - Con.df[y,tcr1_lines] <- data2[location.i[z],data1_lines] - } else { - Con.df[y,tcr1_lines] <- paste(Con.df[y, tcr1_lines], - data2[location.i[z],data1_lines],sep=";") - } - } else if (where.chain == "TRB" | where.chain == "TRD") { - if(is.na(Con.df[y,"TCR2"])) { - Con.df[y,tcr2_lines] <- data2[location.i[z],data2_lines] - } else { - Con.df[y,tcr2_lines] <- paste(Con.df[y, tcr2_lines], - data2[location.i[z],data2_lines],sep=";") + where.chain <- data2[location.i[z],"chain"] + + if (where.chain == "TRA" | where.chain == "TRG") { + if (is.na(Con.df[y,"TCR1"])) { + Con.df[y,tcr1_lines] <- data2[location.i[z],data1_lines] + } else { + Con.df[y,tcr1_lines] <- paste( + Con.df[y, tcr1_lines], + data2[location.i[z],data1_lines], + sep=";" + ) + } + } else if (where.chain == "TRB" | where.chain == "TRD") { + if (is.na(Con.df[y,"TCR2"])) { + Con.df[y,tcr2_lines] <- data2[location.i[z],data2_lines] + } else { + Con.df[y,tcr2_lines] <- paste( + Con.df[y, tcr2_lines], + data2[location.i[z],data2_lines], + sep=";" + ) + } } - } } } - return(Con.df) + Con.df } #Assigning positions for BCR contig data @@ -469,11 +475,12 @@ assignCT <- function(cellType, Con.df) { Con.df$CTaa <- paste(Con.df$cdr3_aa1, Con.df$cdr3_aa2, sep="_") Con.df$CTstrict <- paste(Con.df$TCR1, Con.df$cdr3_nt1, Con.df$TCR2, Con.df$cdr3_nt2, sep="_") - } else { + } else { # assume cellType = B Con.df$CTgene <- paste(Con.df$IGH, Con.df$IGLC, sep="_") Con.df$CTnt <- paste(Con.df$cdr3_nt1, Con.df$cdr3_nt2, sep="_") - Con.df$CTaa <- paste(Con.df$cdr3_aa1, Con.df$cdr3_aa2, sep="_") } -return(Con.df) + Con.df$CTaa <- paste(Con.df$cdr3_aa1, Con.df$cdr3_aa2, sep="_") + } + return(Con.df) } diff --git a/man/combineBCR.Rd b/man/combineBCR.Rd index d73e9a75..ac7474ea 100644 --- a/man/combineBCR.Rd +++ b/man/combineBCR.Rd @@ -49,8 +49,8 @@ combineBCR produces a column CTstrict of an index of nucleotide sequence and the corresponding v-gene. This index automatically calculates the Levenshtein distance between sequences with the same V gene and will index sequences with <= 0.15 normalized Levenshtein distance with the same -ID. After which, clonotype clusters are called using the igraph -component() function. Clonotype that are clustered across multiple +ID. After which, clonotype clusters are called using the +`igraph:: component()` function. Clonotype that are clustered across multiple sequences will then be labeled with "LD" in the CTstrict header. } \examples{ diff --git a/man/combineTCR.Rd b/man/combineTCR.Rd index b893136d..f6ca727a 100644 --- a/man/combineTCR.Rd +++ b/man/combineTCR.Rd @@ -31,14 +31,19 @@ corresponding chains with the highest expression for a single barcode.} List of clonotypes for individual cell barcodes } \description{ -This function consolidates a list of TCR sequencing results to the level of -the individual cell barcodes. Using the samples and ID parameters, the -function will add the strings as prefixes to prevent issues with repeated -barcodes. The resulting new barcodes will need to match the Seurat or SCE -object in order to use, \code{\link{combineExpression}}. Several -levels of filtering exist - remove or filterMulti are parameters that -control how the function deals with barcodes with multiple chains -recovered. +This function consolidates a list of TCR sequencing results to +the level of the individual cell barcodes. Using the samples and ID +parameters, the function will add the strings as prefixes to prevent issues +with repeated barcodes. The resulting new barcodes will need to match the +Seurat or SCE object in order to use, \code{\link{combineExpression}}. +Several levels of filtering exist - `removeNA`, `removeMulti`, or +`filterMulti` are parameters that control how the function deals with +barcodes with multiple chains recovered. +} +\details{ +For single-sample TCR sequencing experiments, where the input is +just a single data.frame, the function will add the consolidated information +columns after the existing raw clonotype data } \examples{ combineTCR(contig_list, diff --git a/tests/testthat/helper-testing_functions.R b/tests/testthat/helper-testing_functions.R new file mode 100644 index 00000000..55f04651 --- /dev/null +++ b/tests/testthat/helper-testing_functions.R @@ -0,0 +1,3 @@ +getdata <- function(dir, name) { + readRDS(paste("testdata/", dir, "/", name, ".rds", sep = "")) +} diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R new file mode 100644 index 00000000..f13d5598 --- /dev/null +++ b/tests/testthat/test-combineContigs.R @@ -0,0 +1,18 @@ +# test script for combineContigs.R - testcases are NOT comprehensive! + +test_that("combineTCR works", { + data("contig_list") + trial <- combineTCR( + df = lapply(contig_list[1:3], head), + samples = c("PY", "PY", "PX"), + ID = c("P", "T", "P") + ) + expect_identical( + trial, + readRDS("testdata/combineContigs/combineTCR_list_expected.rds") + ) +}) + +# TODO combineTCR (need more edge cases, different args, errors, etc.) +# TODO combineBCR +# TODO lvCompare diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index cd4c37b9..b3d158b7 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -1,5 +1,4 @@ # test script for utils.R - testcases are NOT comprehensive! -source("testdata/utils-testdata.R") test_that("'%!in%' works", { v <- c(1, 2, 3, 4, 5) @@ -46,21 +45,10 @@ test_that("checkContigs works", { # Test cases test_that("Modifying barcodes without ID works correctly", { - # Simulated dataframes for testing - df1 <- data.frame( - barcode = c("A", "B", "C"), - value = c(10, 20, 30) - ) - - df2 <- data.frame( - barcode = c("X", "Y", "Z"), - value = c(100, 200, 300) - ) - - df_list <- list(df1, df2) - samples <- c("sample1", "sample2") - modified_data <- modifyBarcodes(df_list, samples, NULL) + modified_data <- modifyBarcodes( + df = getdata("utils", "df_list"), samples = samples, ID = NULL + ) expected_modified_data <- list( data.frame( @@ -77,22 +65,11 @@ test_that("Modifying barcodes without ID works correctly", { }) test_that("Modifying barcodes with ID works correctly", { - # Simulated dataframes for testing - df1 <- data.frame( - barcode = c("A", "B", "C"), - value = c(10, 20, 30) - ) - - df2 <- data.frame( - barcode = c("X", "Y", "Z"), - value = c(100, 200, 300) - ) - - df_list <- list(df1, df2) - samples <- c("sample3", "sample4") ID <- c("id1", "id2") - modified_data <- modifyBarcodes(df_list, samples, ID) + modified_data <- modified_data <- modifyBarcodes( + df = getdata("utils", "df_list"), samples = samples, ID = ID + ) expected_modified_data <- list( data.frame( @@ -114,7 +91,7 @@ test_that("Modifying barcodes with ID works correctly", { test_that("filteringMulti works", { expect_identical( filteringMulti(head(contig_list[[1]])), - filteringMulti_expected + getdata("utils", "filteringMulti_expected") ) }) @@ -134,8 +111,8 @@ test_that("filteringMulti works", { test_that("makeGenes works for cellType T", { expect_identical( - makeGenes("T", makeGenes_T_input), - makeGenes_T_expected + makeGenes("T", getdata("utils", "makeGenes_T_input")), + getdata("utils", "makeGenes_T_expected") ) }) diff --git a/tests/testthat/testdata/combineContigs/combineTCR_list_expected.rds b/tests/testthat/testdata/combineContigs/combineTCR_list_expected.rds new file mode 100644 index 0000000000000000000000000000000000000000..5d08d24f48bb15a5a5cd93a45d691cce670a16f1 GIT binary patch literal 1192 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a/tests/testthat/testdata/utils-testdata.R b/tests/testthat/testdata/utils-testdata.R deleted file mode 100644 index 78f76488..00000000 --- a/tests/testthat/testdata/utils-testdata.R +++ /dev/null @@ -1,106 +0,0 @@ -# script for some of the testing data used in tests/testthat/test-utils.R -# produced by using dput() and chatGPT - -# output of dput(filteringMulti(head(contig_list[[1]]))) -filteringMulti_expected <- structure(list( - barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), - is_cell = c(TRUE, TRUE, TRUE, TRUE), - contig_id = c("AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_2", "AAACCTGAGCATCATC-1_contig_1", "AAACCTGAGTGGTCCC-1_contig_1"), - high_confidence = c(TRUE, TRUE, TRUE, TRUE), - length = c(705L, 567L, 693L, 593L), - chain = c("TRB", "TRA", "TRB", "TRB"), - v_gene = c("TRBV20-1", "TRAV12-1", "TRBV5-1", "TRBV7-9"), - d_gene = c("TRBD1", "None", "TRBD2", "TRBD1"), - j_gene = c("TRBJ1-5", "TRAJ37", "TRBJ2-2", "TRBJ2-5"), - c_gene = c("TRBC1", "TRAC", "TRBC2", "TRBC2"), - full_length = c(TRUE, TRUE, TRUE, TRUE), - productive = c("TRUE", "TRUE", "TRUE", "TRUE"), - cdr3 = c("CSASMGPVVSNQPQHF", "CVVNDEGSSNTGKLIF", "CASSWSGAGDGELFF", "CASSPSEGGRQETQYF"), - cdr3_nt = c("TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", - "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", - "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", - "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC"), - reads = c(16718L, 18297L, 26719L, 11218L), - umis = c(6L, 6L, 11L, 6L), - raw_clonotype_id = c("clonotype96", "clonotype97", "clonotype97", "clonotype98"), - raw_consensus_id = c("clonotype96_consensus_1", "clonotype97_consensus_1", "clonotype97_consensus_2", "clonotype98_consensus_1")), - class = c("grouped_df", "tbl_df", "tbl", "data.frame"), - row.names = c(NA, -4L), - groups = structure(list( - barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC"), - chain = c("TRB", "TRA", "TRB", "TRB"), - .rows = structure(list(1L, 2L, 3L, 4L), - ptype = integer(0), - class = c("vctrs_list_of", "vctrs_vctr", "list")) - ), row.names = c(NA, -4L), .drop = TRUE, class = c("tbl_df", "tbl", "data.frame")) -) - -makeGenes_T_input <- structure(list( - barcode = c( - "AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", - "AAACCTGAGTGGTCCC", "AAACCTGCAAACGCGA", "AAACCTGCAAACGCGA" - ), - is_cell = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), - contig_id = c( - "AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_1", - "AAACCTGAGCATCATC-1_contig_2", "AAACCTGAGTGGTCCC-1_contig_1", - "AAACCTGCAAACGCGA-1_contig_1", "AAACCTGCAAACGCGA-1_contig_3" - ), - high_confidence = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), - length = c(705L, 693L, 567L, 593L, 659L, 514L), - chain = c("TRB", "TRB", "TRA", "TRB", "TRB", "TRA"), - v_gene = c("TRBV20-1", "TRBV5-1", "TRAV12-1", "TRBV7-9", "TRBV2", "TRAV29DV5"), - d_gene = c("TRBD1", "TRBD2", "None", "TRBD1", "TRBD1", "None"), - j_gene = c("TRBJ1-5", "TRBJ2-2", "TRAJ37", "TRBJ2-5", "TRBJ1-6", "TRAJ22"), - c_gene = c("TRBC1", "TRBC2", "TRAC", "TRBC2", "TRBC1", "TRAC"), - full_length = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), - productive = c("TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), - cdr3 = c( - "CSASMGPVVSNQPQHF", "CASSWSGAGDGELFF", "CVVNDEGSSNTGKLIF", - "CASSPSEGGRQETQYF", "CASRVQGNRGSPLHF", "CAASGYGSARQLTF" - ), - cdr3_nt = c( - "TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", - "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", - "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", - "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC", - "TGTGCCAGCAGGGTACAGGGTAATAGGGGTTCACCCCTCCACTTT", - "TGTGCAGCAAGCGGTTACGGTTCTGCAAGGCAACTGACCTTT" - ), - reads = c(16718L, 26719L, 18297L, 11218L, 10891L, 874L), - umis = c(6L, 11L, 6L, 6L, 4L, 1L), - raw_clonotype_id = c( - "clonotype96", "clonotype97", "clonotype97", - "clonotype98", "clonotype99", "clonotype99" - ), - raw_consensus_id = c( - "clonotype96_consensus_1", "clonotype97_consensus_2", - "clonotype97_consensus_1", "clonotype98_consensus_1", - "clonotype99_consensus_1", "clonotype99_consensus_2" - ) -), row.names = c(1L, 3L, 4L, 6L, 8L, 9L), class = "data.frame") - -makeGenes_T_expected <- structure(list( - barcode = c("AAACCTGAGAGCTGGT", "AAACCTGAGCATCATC", "AAACCTGAGCATCATC", "AAACCTGAGTGGTCCC", "AAACCTGCAAACGCGA", "AAACCTGCAAACGCGA"), - is_cell = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), - contig_id = c("AAACCTGAGAGCTGGT-1_contig_1", "AAACCTGAGCATCATC-1_contig_1", "AAACCTGAGCATCATC-1_contig_2", "AAACCTGAGTGGTCCC-1_contig_1", "AAACCTGCAAACGCGA-1_contig_1", "AAACCTGCAAACGCGA-1_contig_3"), - high_confidence = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), - length = c(705L, 693L, 567L, 593L, 659L, 514L), - chain = c("TRB", "TRB", "TRA", "TRB", "TRB", "TRA"), - v_gene = c("TRBV20-1", "TRBV5-1", "TRAV12-1", "TRBV7-9", "TRBV2", "TRAV29DV5"), - d_gene = c("TRBD1", "TRBD2", "None", "TRBD1", "TRBD1", "None"), - j_gene = c("TRBJ1-5", "TRBJ2-2", "TRAJ37", "TRBJ2-5", "TRBJ1-6", "TRAJ22"), - c_gene = c("TRBC1", "TRBC2", "TRAC", "TRBC2", "TRBC1", "TRAC"), - full_length = c(TRUE, TRUE, TRUE, TRUE, TRUE, TRUE), - productive = c("TRUE", "TRUE", "TRUE", "TRUE", "TRUE", "TRUE"), - cdr3 = c("CSASMGPVVSNQPQHF", "CASSWSGAGDGELFF", "CVVNDEGSSNTGKLIF", "CASSPSEGGRQETQYF", "CASRVQGNRGSPLHF", "CAASGYGSARQLTF"), - cdr3_nt = c("TGCAGTGCTAGCATGGGACCGGTAGTGAGCAATCAGCCCCAGCATTTT", "TGCGCCAGCAGCTGGTCAGGAGCGGGAGACGGGGAGCTGTTTTTT", "TGTGTGGTGAACGATGAAGGCTCTAGCAACACAGGCAAACTAATCTTT", "TGTGCCAGCAGCCCCTCCGAAGGGGGGAGACAAGAGACCCAGTACTTC", "TGTGCCAGCAGGGTACAGGGTAATAGGGGTTCACCCCTCCACTTT", "TGTGCAGCAAGCGGTTACGGTTCTGCAAGGCAACTGACCTTT"), - reads = c(16718L, 26719L, 18297L, 11218L, 10891L, 874L), - umis = c(6L, 11L, 6L, 6L, 4L, 1L), - raw_clonotype_id = c("clonotype96", "clonotype97", "clonotype97", "clonotype98", "clonotype99", "clonotype99"), - raw_consensus_id = c("clonotype96_consensus_1", "clonotype97_consensus_2", "clonotype97_consensus_1", "clonotype98_consensus_1", "clonotype99_consensus_1", 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R/contig_list.R | 6 -- R/{screp_example.R => data.R} | 7 ++ .../viz/quantcontig-mini-scaled-plot.svg | 80 +++++++++++++++++++ tests/testthat/helper-vdiffr.R | 15 ++++ tests/testthat/test-combineContigs.R | 17 ++-- tests/testthat/test-viz.R | 26 ++++++ 7 files changed, 142 insertions(+), 12 deletions(-) delete mode 100644 R/contig_list.R rename R/{screp_example.R => data.R} (50%) create mode 100644 tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg create mode 100644 tests/testthat/helper-vdiffr.R create mode 100644 tests/testthat/test-viz.R diff --git a/DESCRIPTION b/DESCRIPTION index 15945e03..fe05de59 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -42,8 +42,9 @@ Suggests: circlize, scales, scater, + spelling, testthat (>= 3.0.0), - spelling + vdiffr VignetteBuilder: knitr Config/testthat/edition: 3 Language: en-US diff --git a/R/contig_list.R b/R/contig_list.R deleted file mode 100644 index 31570298..00000000 --- a/R/contig_list.R +++ /dev/null @@ -1,6 +0,0 @@ -#' A data set of T cell contigs as a list outputed from the -#' filter_contig_annotation files. -#' @docType data -#' @name contig_list -#' -NULL diff --git a/R/screp_example.R b/R/data.R similarity index 50% rename from R/screp_example.R rename to R/data.R index c00afd42..b0404de7 100644 --- a/R/screp_example.R +++ b/R/data.R @@ -1,3 +1,10 @@ +#' A data set of T cell contigs as a list outputed from the +#' filter_contig_annotation files. +#' @docType data +#' @name contig_list +#' +NULL + #' A seurat object of 1000 single T cells derived #' from 3 clear cell renal carcinoma patients. #' @name screp_example diff --git a/tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg b/tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg new file mode 100644 index 00000000..7e8e9563 --- /dev/null +++ b/tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg @@ -0,0 +1,80 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0 +20 +40 +60 +80 + + + + + + + + + + + + +PY_P +PY_T +PX_P +PX_T +PZ_P +PZ_T +Samples +Percent of Unique Clonotype + +Samples + + + + + + +PY_P +PY_T +PX_P +PX_T +PZ_P +PZ_T +quantContig_mini_scaled_plot + + diff --git a/tests/testthat/helper-vdiffr.R b/tests/testthat/helper-vdiffr.R new file mode 100644 index 00000000..eb4b8ea0 --- /dev/null +++ b/tests/testthat/helper-vdiffr.R @@ -0,0 +1,15 @@ +# By default, if vdiffr is not installed, all visual tests are skipped unless +# VDIFFR_RUN_TESTS is explicitly set to "true", which should be the case only on +# a GitHub Actions CI runner with stable version of R. + +if (requireNamespace("vdiffr", quietly = TRUE) && utils::packageVersion('testthat') >= '3.0.3') { + expect_doppelganger <- vdiffr::expect_doppelganger +} else { + # If vdiffr is not available and visual tests are explicitly required, raise error. + if (identical(Sys.getenv("VDIFFR_RUN_TESTS"), "true")) { + rlang::abort("vdiffr is not installed") + } + + # Otherwise, assign a dummy function + expect_doppelganger <- function(...) skip("vdiffr is not installed.") +} diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R index f13d5598..4ef32f8a 100644 --- a/tests/testthat/test-combineContigs.R +++ b/tests/testthat/test-combineContigs.R @@ -2,15 +2,22 @@ test_that("combineTCR works", { data("contig_list") - trial <- combineTCR( + + trial1 <- combineTCR( df = lapply(contig_list[1:3], head), samples = c("PY", "PY", "PX"), ID = c("P", "T", "P") ) - expect_identical( - trial, - readRDS("testdata/combineContigs/combineTCR_list_expected.rds") - ) + expected1 <- readRDS("testdata/combineContigs/combineTCR_list_expected.rds") + + expect_identical(trial1, expected1) + + trial2 <- combineTCR( + head(contig_list[[1]]), samples = "PY", ID = "P" + )[[1]] + expected2 <- expected1[[1]] + + expect_identical(trial2, expected2) }) # TODO combineTCR (need more edge cases, different args, errors, etc.) diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R new file mode 100644 index 00000000..0d5cd9c2 --- /dev/null +++ b/tests/testthat/test-viz.R @@ -0,0 +1,26 @@ +# test script for viz.R - testcases are NOT comprehensive! + +test_that("quantContig works, assuming combineTCR works", { + expect_doppelganger( + "quantContig_mini_scaled_plot", + quantContig( + combineTCR( + contig_list, + samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), + ID = c("P", "T", "P", "T", "P", "T") + ), + scale = TRUE + ) + ) + + # TODO test for only a single sample + # TODO test the exportTable param +}) + +# TODO abundanceContig +# TODO lengthContig +# TODO compareClonotypes +# TODO scatterClonotype +# TODO clonesizeDistribution +# TODO makingLodes +# TODO vizGenes From c6e9c71a0d0e2679b25045d6a9fdc00fae46d3c7 Mon Sep 17 00:00:00 2001 From: Qile Date: Tue, 8 Aug 2023 22:24:36 +0200 Subject: [PATCH 011/142] more vdiffr tests --- .../viz/abundancecontig-scaled-plot.svg | 70 + .../viz/compareclonotypes-vignette-plot.svg | 141 ++ .../viz/lengthcontig-both-chain-plot.svg | 393 ++++++ ...d-plot.svg => quantcontig-scaled-plot.svg} | 2 +- .../_snaps/viz/vizgenes-bar-vignette-plot.svg | 686 ++++++++++ .../viz/vizgenes-heatmap-vignette-plot.svg | 1186 +++++++++++++++++ tests/testthat/test-viz.R | 74 +- 7 files changed, 2538 insertions(+), 14 deletions(-) create mode 100644 tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg create mode 100644 tests/testthat/_snaps/viz/lengthcontig-both-chain-plot.svg rename tests/testthat/_snaps/viz/{quantcontig-mini-scaled-plot.svg => quantcontig-scaled-plot.svg} (98%) create mode 100644 tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg create mode 100644 tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg diff --git a/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg b/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg new file mode 100644 index 00000000..94131b11 --- /dev/null +++ b/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg @@ -0,0 +1,70 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0 +1000 +2000 + + + + + + + +1 +10 +100 +Abundance +Number of Clonotypes + +Samples + + + + + + +PY_P +PY_T +PX_P +PX_T +PZ_P +PZ_T +abundanceContig_scaled_plot + + diff --git a/tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg b/tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg new file mode 100644 index 00000000..a9b640a5 --- /dev/null +++ b/tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg @@ -0,0 +1,141 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.00 +0.05 +0.10 +0.15 + + + + + + + +PX_P +PX_T +Proportion + +Clonotypes + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +CAASAKETSGSRLTF_CASSRILITSGSPLHF +CAASAKETSGSRLTF_NA +CAGDDRGSTLGRLYF_CASSLVEEDEQFF +CAMREGIGGYQKVTF_CSALPASGSDEQFF +CASLQNRDDKIIF_CASGAFGGIGRPYNSPLHF +CATVPNSGGYQKVTF_CASSRPPGGRVSDTQYF +CAVHPRDSSYKLIF_CASSLTVGSAETQYF +CAVRVRMDSSYKLIF_CASSPPDAAYNEQFF +NA_CASGAFGGIGRPYNSPLHF +NA_CASSLTVGSAETQYF +NA_CASSRILITSGSPLHF +CAAGTNAGGTSYGKLTF_CASSPGHDTEAFF +CAESLTDSWGKLQF_CASSPPSNDEQYF +CAESSPGGYNKLIF_CASSWNTGELFF +CAMSYGGSQGNLIF_CASSRDPTSDSTDTQYF +CLPYTQGGSEKLVF_CASRFLGDSPLHF +NA_CASSLVEEDEQFF +compareClonotypes_vignette_plot + + diff --git a/tests/testthat/_snaps/viz/lengthcontig-both-chain-plot.svg b/tests/testthat/_snaps/viz/lengthcontig-both-chain-plot.svg new file mode 100644 index 00000000..c7b08c07 --- /dev/null +++ b/tests/testthat/_snaps/viz/lengthcontig-both-chain-plot.svg @@ -0,0 +1,393 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0 +200 +400 +600 + + + + + + + + + + + + + + + + + + + + + + +7 +12 +17 +22 +27 +32 +37 +42 +47 +52 +57 +62 +67 +72 +77 +87 +92 +Length +Number of CDR3 (AA) + +Samples + + + + + + +PY_P +PY_T +PX_P +PX_T +PZ_P +PZ_T +lengthContig_both_chain_plot + + diff --git a/tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg b/tests/testthat/_snaps/viz/quantcontig-scaled-plot.svg similarity index 98% rename from tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg rename to tests/testthat/_snaps/viz/quantcontig-scaled-plot.svg index 7e8e9563..f72bada6 100644 --- a/tests/testthat/_snaps/viz/quantcontig-mini-scaled-plot.svg +++ b/tests/testthat/_snaps/viz/quantcontig-scaled-plot.svg @@ -75,6 +75,6 @@ PX_T PZ_P PZ_T -quantContig_mini_scaled_plot +quantContig_scaled_plot diff --git a/tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg b/tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg new file mode 100644 index 00000000..a319f7cc --- /dev/null +++ b/tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg @@ -0,0 +1,686 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+TRBV21-1 +TRBV12-5 +TRBV7-7 +TRBV10-2 +TRBV16 +TRBV7-4 +TRBV5-3 + +0.0 +0.1 +0.2 + + + + +0.0 +0.1 +0.2 + + + + +0.0 +0.1 +0.2 + + + +vizGenes_bar_vignette_plot + + diff --git a/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg b/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg new file mode 100644 index 00000000..c385183f --- /dev/null +++ b/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg @@ -0,0 +1,1186 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+TRBV4-1 +TRBV4-2 +TRBV4-3 +TRBV5-1 +TRBV5-3 +TRBV5-4 +TRBV5-5 +TRBV5-6 +TRBV5-8 +TRBV6-1 +TRBV6-3 +TRBV6-4 +TRBV6-5 +TRBV6-6 +TRBV7-2 +TRBV7-3 +TRBV7-6 +TRBV7-7 +TRBV7-8 +TRBV7-9 +TRBV9 + + +0.02 +0.04 +0.06 +Scaled Values + + + + + + +vizGenes_heatmap_vignette_plot + + diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R index 0d5cd9c2..29b5dba6 100644 --- a/tests/testthat/test-viz.R +++ b/tests/testthat/test-viz.R @@ -1,25 +1,73 @@ # test script for viz.R - testcases are NOT comprehensive! -test_that("quantContig works, assuming combineTCR works", { +# testdata: (assumes combineTCR works) +combined <- combineTCR( + contig_list, + samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), + ID = c("P", "T", "P", "T", "P", "T") +) + +test_that("quantContig works", { expect_doppelganger( - "quantContig_mini_scaled_plot", - quantContig( - combineTCR( - contig_list, - samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), - ID = c("P", "T", "P", "T", "P", "T") - ), - scale = TRUE - ) + "quantContig_scaled_plot", quantContig(combined, scale = TRUE) ) # TODO test for only a single sample # TODO test the exportTable param }) -# TODO abundanceContig -# TODO lengthContig -# TODO compareClonotypes +test_that("abundanceContig works", { + expect_doppelganger( + "abundanceContig_scaled_plot", abundanceContig(combined, scale = FALSE) + ) +}) + +test_that("lengthContig works", { + expect_doppelganger( + "lengthContig_both_chain_plot", lengthContig(combined, chain = "both") + ) +}) + +test_that("compareClonotypes works", { + expect_doppelganger( + "compareClonotypes_vignette_plot", + compareClonotypes( + combined, + numbers = 10, + samples = c("PX_P", "PX_T"), + cloneCall="aa", + graph = "alluvial" + ) + ) +}) + +test_that("vizGenes works", { + expect_doppelganger( + "vizGenes_bar_vignette_plot", + vizGenes( + combined, + gene = "V", + chain = "TRB", + plot = "bar", + order = "variance", + scale = TRUE + ) + ) + + expect_doppelganger( + "vizGenes_heatmap_vignette_plot", + vizGenes( + combined[c(1,3,5)], + gene = "V", + chain = "TRB", + y.axis = "J", + plot = "heatmap", + scale = TRUE, + order = "gene" + ) + ) +}) + # TODO scatterClonotype # TODO clonesizeDistribution # TODO makingLodes From f02718195e14e2589cd12aa08c5ac8821d3eb503 Mon Sep 17 00:00:00 2001 From: Qile Date: Wed, 9 Aug 2023 01:47:23 +0200 Subject: [PATCH 012/142] modified quantContig to display single run samples better, added seed param to scatterClonotype, added more vdiffr tests --- R/viz.R | 48 +- man/contig_list.Rd | 2 +- man/scatterClonotype.Rd | 5 +- man/screp_example.Rd | 2 +- .../clonesizedistribution-vignette-plot.svg | 82 + ...vg => compareclonotypes-alluvial-plot.svg} | 2 +- .../viz/compareclonotypes-area-plot.svg | 95 + .../viz/quantcontig-single-sample-plot.svg | 44 + .../viz/scatterclonotype-vignette-plot.svg | 3841 +++++++++++++++++ tests/testthat/test-viz.R | 73 +- 10 files changed, 4172 insertions(+), 22 deletions(-) create mode 100644 tests/testthat/_snaps/viz/clonesizedistribution-vignette-plot.svg rename tests/testthat/_snaps/viz/{compareclonotypes-vignette-plot.svg => compareclonotypes-alluvial-plot.svg} (99%) create mode 100644 tests/testthat/_snaps/viz/compareclonotypes-area-plot.svg create mode 100644 tests/testthat/_snaps/viz/quantcontig-single-sample-plot.svg create mode 100644 tests/testthat/_snaps/viz/scatterclonotype-vignette-plot.svg diff --git a/R/viz.R b/R/viz.R index e1f85d01..762b0d2d 100644 --- a/R/viz.R +++ b/R/viz.R @@ -83,7 +83,19 @@ quantContig <- function(df, ylab <- "Percent of Unique Clonotype" } else { y <- "contigs" ylab <- "Unique Clonotypes" } } - if (exportTable == TRUE) { return(Con.df) } + + if (exportTable) { + if (length(df) > 1) { + return(Con.df) + } + + # if a single sample, remove the "values" column if NA + if (is.na(Con.df[[2]])) { + Con.df[[2]] <- NULL + } + return(Con.df) + } + if(order & is.null(group.by)) { Con.df[,x] <- factor(Con.df[,x], levels = Con.df[,x]) } @@ -94,8 +106,18 @@ quantContig <- function(df, stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ theme_classic() + xlab("Samples") + ylab(ylab) + scale_fill_manual(values = colorblind_vector(col)) - return(plot) } - + + # if it is a single run, remove x axis labels if sample name missing + if ((length(df) == 1) && identical(names(df), NA_character_)) { + plot <- plot + + ggplot2::theme( + axis.title.x = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank() + ) + } + return(plot) +} #' Demonstrate the relative abundance of clonotypes by group or sample. #' @@ -420,19 +442,25 @@ compareClonotypes <- function(df, #' to group the new list. NULL will return clusters. #' @param graph graph either proportion or raw clonotype count #' @param exportTable Returns the data frame used for forming the graph. +#' @param seed the integer seed to set for the random variation of point coords. #' #' @import ggplot2 #' #' @export #' @return ggplot of the relative clonotype numbers -scatterClonotype <- function(df, cloneCall ="strict", - x.axis = NULL, y.axis = NULL, - chain = "both", - dot.size = "total", - split.by = NULL, - graph = "proportion", - exportTable = FALSE) { +scatterClonotype <- function( + df, cloneCall ="strict", + x.axis = NULL, y.axis = NULL, + chain = "both", + dot.size = "total", + split.by = NULL, + graph = "proportion", + exportTable = FALSE, + seed = NULL +) { + if (!is.null(seed)) {set.seed(seed)} + df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) diff --git a/man/contig_list.Rd b/man/contig_list.Rd index 97a2c118..b2440c3f 100644 --- a/man/contig_list.Rd +++ b/man/contig_list.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/contig_list.R +% Please edit documentation in R/data.R \docType{data} \name{contig_list} \alias{contig_list} diff --git a/man/scatterClonotype.Rd b/man/scatterClonotype.Rd index 62bd01f6..4208a509 100644 --- a/man/scatterClonotype.Rd +++ b/man/scatterClonotype.Rd @@ -13,7 +13,8 @@ scatterClonotype( dot.size = "total", split.by = NULL, graph = "proportion", - exportTable = FALSE + exportTable = FALSE, + seed = NULL ) } \arguments{ @@ -39,6 +40,8 @@ to group the new list. NULL will return clusters.} \item{graph}{graph either proportion or raw clonotype count} \item{exportTable}{Returns the data frame used for forming the graph.} + +\item{seed}{the integer seed to set for the random variation of point coords.} } \value{ ggplot of the relative clonotype numbers diff --git a/man/screp_example.Rd b/man/screp_example.Rd index c29ecd04..41ec8ed8 100644 --- a/man/screp_example.Rd +++ b/man/screp_example.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/screp_example.R +% Please edit documentation in R/data.R \docType{data} \name{screp_example} \alias{screp_example} diff --git a/tests/testthat/_snaps/viz/clonesizedistribution-vignette-plot.svg b/tests/testthat/_snaps/viz/clonesizedistribution-vignette-plot.svg new file mode 100644 index 00000000..fb5f72c6 --- /dev/null +++ b/tests/testthat/_snaps/viz/clonesizedistribution-vignette-plot.svg @@ -0,0 +1,82 @@ + + + + + + + + + + + + + + + + + + + +0.00 +0.05 +0.10 +0.15 +0.20 +0.25 + + + + + + + + + + + + + +PY_P + + + + + + + + +PY_T + + + + + + + + + + + + +PX_T +PZ_T + + + + + + + + +PX_P +PZ_P + + diff --git a/tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg b/tests/testthat/_snaps/viz/compareclonotypes-alluvial-plot.svg similarity index 99% rename from tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg rename to tests/testthat/_snaps/viz/compareclonotypes-alluvial-plot.svg index a9b640a5..419cbf5b 100644 --- a/tests/testthat/_snaps/viz/compareclonotypes-vignette-plot.svg +++ b/tests/testthat/_snaps/viz/compareclonotypes-alluvial-plot.svg @@ -136,6 +136,6 @@ CAMSYGGSQGNLIF_CASSRDPTSDSTDTQYF CLPYTQGGSEKLVF_CASRFLGDSPLHF NA_CASSLVEEDEQFF -compareClonotypes_vignette_plot +compareClonotypes_alluvial_plot diff --git a/tests/testthat/_snaps/viz/compareclonotypes-area-plot.svg b/tests/testthat/_snaps/viz/compareclonotypes-area-plot.svg new file mode 100644 index 00000000..8d1f931c --- /dev/null +++ b/tests/testthat/_snaps/viz/compareclonotypes-area-plot.svg @@ -0,0 +1,95 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.00 +0.05 +0.10 +0.15 + + + + + + + +PX_P +PX_T +Proportion + +Clonotypes + + + + + + + + + + + +CAASAKETSGSRLTF_CASSRILITSGSPLHF +CAASAKETSGSRLTF_NA +CAGDDRGSTLGRLYF_CASSLVEEDEQFF +CAMREGIGGYQKVTF_CSALPASGSDEQFF +CASLQNRDDKIIF_CASGAFGGIGRPYNSPLHF +CATVPNSGGYQKVTF_CASSRPPGGRVSDTQYF +CAVHPRDSSYKLIF_CASSLTVGSAETQYF +CAVRVRMDSSYKLIF_CASSPPDAAYNEQFF +NA_CASGAFGGIGRPYNSPLHF +NA_CASSLTVGSAETQYF +NA_CASSRILITSGSPLHF +compareClonotypes_area_plot + + diff --git a/tests/testthat/_snaps/viz/quantcontig-single-sample-plot.svg b/tests/testthat/_snaps/viz/quantcontig-single-sample-plot.svg new file mode 100644 index 00000000..4145234a --- /dev/null +++ b/tests/testthat/_snaps/viz/quantcontig-single-sample-plot.svg @@ -0,0 +1,44 @@ + + + + + + + + + + + + + + + + + + + + + + + + + +0 +1000 +2000 + + + + +Unique Clonotypes +quantContig_single_sample_plot + + diff --git a/tests/testthat/_snaps/viz/scatterclonotype-vignette-plot.svg b/tests/testthat/_snaps/viz/scatterclonotype-vignette-plot.svg new file mode 100644 index 00000000..3402bd43 --- /dev/null +++ b/tests/testthat/_snaps/viz/scatterclonotype-vignette-plot.svg @@ -0,0 +1,3841 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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n + + + +30 +60 +90 + +class + + + + + +PY_P.multiplet +PY_P.singlet +PY_T.multiplet +PY_T.singlet +dual.expanded +scatterClonotype_vignette_plot + + diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R index 29b5dba6..9d4650ea 100644 --- a/tests/testthat/test-viz.R +++ b/tests/testthat/test-viz.R @@ -1,4 +1,6 @@ # test script for viz.R - testcases are NOT comprehensive! +# TODO all functions need to be tested for a single sample - however, many don't +# really work properly for single samples # testdata: (assumes combineTCR works) combined <- combineTCR( @@ -7,13 +9,36 @@ combined <- combineTCR( ID = c("P", "T", "P", "T", "P", "T") ) +single_contig <- combineTCR(contig_list[[1]]) + +single_contig_with__sample <- combineTCR( + contig_list[[1]], samples = "PX", ID = "P" +) + test_that("quantContig works", { expect_doppelganger( "quantContig_scaled_plot", quantContig(combined, scale = TRUE) ) + expect_equal( + quantContig(combined, scale = TRUE, exportTable = TRUE), + data.frame( + "contigs" = c(2712L, 1585L, 823L, 918L, 1143L, 768L), + "values" = c("PY_P", "PY_T", "PX_P", "PX_T", "PZ_P", "PZ_T"), + "total" = c(3208L, 3119L, 1068L, 1678L, 1434L, 2768L), + "scaled" = c( + 84.5386533665835, 50.8175697338891, 77.059925093633, + 54.7079856972586, 79.7071129707113, 27.7456647398844 + ) + ) + ) - # TODO test for only a single sample - # TODO test the exportTable param + expect_doppelganger( + "quantContig_single_sample_plot", quantContig(single_contig) + ) + expect_identical( + quantContig(single_contig, exportTable = TRUE), + data.frame("contigs" = 2712L, "total" = 3208L) + ) }) test_that("abundanceContig works", { @@ -30,7 +55,7 @@ test_that("lengthContig works", { test_that("compareClonotypes works", { expect_doppelganger( - "compareClonotypes_vignette_plot", + "compareClonotypes_alluvial_plot", compareClonotypes( combined, numbers = 10, @@ -39,8 +64,45 @@ test_that("compareClonotypes works", { graph = "alluvial" ) ) + + expect_doppelganger( + "compareClonotypes_area_plot", + compareClonotypes( + combined, + numbers = 10, + samples = c("PX_P", "PX_T"), + cloneCall="aa", + graph = "area" + ) + ) +}) + +test_that("scatterClonotype works", { + expect_doppelganger( + "scatterClonotype_vignette_plot", + scatterClonotype( + combined, + cloneCall ="gene", + x.axis = "PY_P", + y.axis = "PY_T", + dot.size = "total", + graph = "proportion", + seed = 42 + ) + ) + + # TODO test the exportTable arg +}) + +test_that("clonesizeDistribution works", { + expect_doppelganger( + "clonesizeDistribution_vignette_plot", + clonesizeDistribution(combined, cloneCall = "gene+nt", method="ward.D2") + ) }) +# TODO makingLodes + test_that("vizGenes works", { expect_doppelganger( "vizGenes_bar_vignette_plot", @@ -67,8 +129,3 @@ test_that("vizGenes works", { ) ) }) - -# TODO scatterClonotype -# TODO clonesizeDistribution -# TODO makingLodes -# TODO vizGenes From 057173afc6d23fee5b10324a5fbf605746978bc2 Mon Sep 17 00:00:00 2001 From: Qile Date: Wed, 9 Aug 2023 18:30:52 +0200 Subject: [PATCH 013/142] fixed spelling, cleaned up files, added new example data for testing --- DESCRIPTION | 2 +- R/data.R | 23 +++++++++++++++++++++-- R/seuratFunctions.R | 11 +++++------ R/startrac.R | 8 ++++---- R/viz.R | 2 +- README.md | 4 ++-- data/combined_mini_contig_list.rda | Bin 0 -> 7995 bytes data/screp_example.rda | Bin 1365840 -> 1562830 bytes inst/CITATION | 21 +++++++++++++-------- inst/WORDLIST | 13 +++---------- man/Startrac.Rd | 4 ++-- man/Startrac.run.Rd | 6 +++--- man/combined_mini_contig_list.Rd | 21 +++++++++++++++++++++ man/screp_example.Rd | 6 +++--- man/vizGenes.Rd | 2 +- scRepertoire.Rproj | 1 + tests/testthat/test-viz.R | 3 ++- vignettes/vignette.Rmd | 19 +++++++++---------- 18 files changed, 92 insertions(+), 54 deletions(-) create mode 100644 data/combined_mini_contig_list.rda create mode 100644 man/combined_mini_contig_list.Rd diff --git a/DESCRIPTION b/DESCRIPTION index fe05de59..bd69f2d3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -11,7 +11,7 @@ RoxygenNote: 7.2.3 biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Depends: ggplot2, - R (>= 4.0) + R (>= 4.0), Seurat Imports: stringdist, diff --git a/R/data.R b/R/data.R index b0404de7..a3b676d1 100644 --- a/R/data.R +++ b/R/data.R @@ -5,10 +5,29 @@ #' NULL -#' A seurat object of 1000 single T cells derived +#' A seurat object of 100 single T cells derived #' from 3 clear cell renal carcinoma patients. +#' +#' @description The object is compatible with `contig_list` and the TCR +#' sequencing data can be added with `combineExpression`. +#' #' @name screp_example #' @docType data #' -#' NULL + +#' Processed subset of `contig_list` +#' +#' @description A list of 6 dataframes of T cell contigs outputted from the +#' `filtered_contig_annotation` files, but subsetted to about 92 valid T cells +#' which correspond to the same barcodes found in `screp_example` +#' +#' @usage data("combined_mini_contig_list") +#' +#' @format An R `list` of `data.frame` objects +#' +#' @docType data +#' +#' @seealso \code{\link{contig_list}} +#' +"combined_mini_contig_list" diff --git a/R/seuratFunctions.R b/R/seuratFunctions.R index e752c04e..08629d17 100644 --- a/R/seuratFunctions.R +++ b/R/seuratFunctions.R @@ -42,7 +42,7 @@ #' clonotype information #' @param addLabel This will add a label to the frequency header, allowing #' the user to try multiple group.by variables or recalculate frequencies after -#' subseting the data. +#' subsetting the data. #' @importFrom dplyr bind_rows %>% summarise #' @importFrom rlang %||% #' @importFrom SummarizedExperiment colData<- colData @@ -147,11 +147,10 @@ combineExpression <- function( } warn_str <- "< 1% of barcodes match: Ensure the barcodes in - the Seurat object match the - barcodes in the combined immune receptor list from - scRepertoire - most common issue is the addition of the - prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() - functions" + the Seurat object match the barcodes in the combined immune receptor + list from scRepertoire - most common issue is the addition of the + prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() + functions" if (is_seurat_object(sc)) { if (length(which(rownames(PreMeta) %in% diff --git a/R/startrac.R b/R/startrac.R index fe2ebe48..a5173c99 100644 --- a/R/startrac.R +++ b/R/startrac.R @@ -111,7 +111,7 @@ StartracDiversity <- function(sc, #' @slot pIndex.migr data.frame. Each line for a cluster; pairwise migration #' index between the two locations indicated in the column name. #' @slot pIndex.tran data.frame. Each line for a cluster; pairwise transition -#' index betwwen the two major clusters indicated by the row name and column name. +#' index between the two major clusters indicated by the row name and column name. #' @slot cluster.sig.data data.frame. Each line for a cluster; contains the #' p values of cluster indices. #' @slot pIndex.sig.migr data.frame. Each line for a cluster; contains the @@ -129,7 +129,7 @@ StartracDiversity <- function(sc, #' @name Startrac #' @rdname Startrac #' @aliases Startrac-class -#' @return method definition for runing startrac +#' @return method definition for running startrac Startrac <- setClass("Startrac", slots = c(aid = "character", cell.data = "data.frame", @@ -527,7 +527,7 @@ mcol.entropy <- function(x) return(H) } -#' warpper function for Startrac analysis +#' wrapper function for Startrac analysis #' @importFrom reshape2 dcast #' @importFrom plyr ldply adply llply #' @importFrom parallel makeCluster stopCluster @@ -537,7 +537,7 @@ mcol.entropy <- function(x) #' @param proj character. String used to annotate the project. #' @param cores integer. number of core to be used. default: NULL. #' @param n.perm integer. number of permutation will be performed. If NULL, no permutation. (default: NULL) -#' @param verbose logical. wheter return intermediate result (some Startrac objects) +#' @param verbose logical. whether return intermediate result (some Startrac objects) #' @details run the Startrac pipeline #' @keywords internal #' @return an list contains data.frame elements "cluster.data","pIndex.migr" and "pIndex.tran" diff --git a/R/viz.R b/R/viz.R index 762b0d2d..9dbf3302 100644 --- a/R/viz.R +++ b/R/viz.R @@ -668,7 +668,7 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' gene segments such as V, D, J, or C. #' @param order Categorical variable to organize the x-axis, either "gene" or "variance" #' @param scale Converts the individual count of genes to proportion using the total -#' respective reprtoire size +#' respective repertoire size #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. diff --git a/README.md b/README.md index 70fc39b3..3d1b1cf2 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine RNA and immune profiling, scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. ### Applying Deep Learning to VDJ data -scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/ncborcherding/Trex) for deep-learning-based autencoding of the T cell receptor and [Ibex](https://github.com/ncborcherding/Ibex) for the B cell receptor. +scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/ncborcherding/Trex) for deep-learning-based autoencoding of the T cell receptor and [Ibex](https://github.com/ncborcherding/Ibex) for the B cell receptor. ### Wrapper Functions scRepertoire v1.0.2 has the functionality of the [powerTCR](https://github.com/hillarykoch/powerTCR) approach to comparing clone size distribution, [please cite](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006571) the manuscript if using the ```clonesizeDistribution()``` function. In addition, we recently added the [Startrac](https://github.com/Japrin/STARTRAC) clonotype metrics, if using the ```StartracDiversity()``` please read and cite [the accompanying article](https://www.nature.com/articles/s41586-018-0694-x). @@ -44,7 +44,7 @@ BiocManager::install("scRepertoire") ### Getting Data -Unfortunately, Github limits the size of individual files. In order to access the seurat object paired with scRepetoire please download the .rda from [here](https://drive.google.com/file/d/1Iv6t2BScpnLLrFWaWFUGwne3XzRAwMOc/view?usp=share_link). +Unfortunately, Github limits the size of individual files. In order to access the seurat object paired with scRepertoire please download the .rda from [here](https://drive.google.com/file/d/1Iv6t2BScpnLLrFWaWFUGwne3XzRAwMOc/view?usp=share_link). ### Learning To Use scRepertoire diff --git a/data/combined_mini_contig_list.rda b/data/combined_mini_contig_list.rda new file mode 100644 index 0000000000000000000000000000000000000000..4cd0b7bc559a7a9b14746b0fe68b3457c78bb6c4 GIT binary patch literal 7995 zcmXAMcQhOB_kU13Vz)N2M@NViLW@$A*jlSK6SGBB1?}4oMQckmV((d7(Kli@5^B`m zlG0MDt*W-~Uq9d9>yLY0_qq4J&OOgP_nhZ3boPa*8aT<@Ii=TbmjUdR{`)^1{6bF6dv8qqIcxfT;lqa&7lzk0CWIE z)Bx7sW{fQ3`TnI<_IP+itV4PhGl9mMA5cSk>jJ>t? z)kwa$fbqhhkNSjP_N*^gwHjau;4R`;%uwtr!UY(bV6pj0Mm`X8pZ!BVKY-QP1)yonxht^!6j<#S7{Jq0nUt7W8(2i1_F_dsy&sYE~ zx|Xpao`Q+ zMzA%drTnv(9Z*qJt;pysmS4>|j4teE0AX7JDbPo$Xby$vREM|Q3N=eFJ}+VZ^oa1? zYW$r>{~}P%IS7z_G3lIp_l?A7cUnhAsr03)+}|0=(g0938<Po5cujS{FA0`ht9y5Y#r@C_%V0a*pIB!Z~FBlvRHEg6=%yluXmHh{LTNP$2}4f 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zw&}R712^gK42zVJr;uOntOo|l#D4l{3L?-}WZH7=Og)uQas>w3#SPyxKyR6ui$@P8 z&D7#AR(Ly6fl-1M@ICWst@poe{xriIQN3P4+Ak{OZ=ZgdrS+`)Sr%A{m2GT9^||G? zVD2WmWW$!xX*BJ^Nz#L)p+%cw=Fb8y?SN+FUZkaDlk1sC?nx6^K@A?JFVG{gYRI!A zNYSoRZgihpX^1G`p5i)^7h5 z*q_aakO6^l=y%V@C8ID$qWa|Cy3ALZ*5&7vuflQx> z@+}TC0ypEGxOE5Od>Vi --- ```{r, echo=FALSE, results="hide", message=FALSE} -knitr::opts_chunk$set(error=FALSE, message=FALSE, warning=FALSE) +knitr::opts_chunk$set(error = FALSE, message = FALSE, warning = FALSE) # to pass R CMD check, packages can be installed in the knitted environment if (!require("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } -library(BiocManager) +suppressPackageStartupMessages(invisible(base::library(BiocManager))) quiet_bioc_load <- function(...) { - pkgs <- list(...) - for (pkg in pkgs) { - if (!require(pkg, character.only = TRUE, quietly = TRUE)) { - BiocManager::install(pkg) - } - suppressPackageStartupMessages(invisible(library(pkg, character.only = TRUE))) - } + pkgs <- list(...) + for (pkg in pkgs) { + if (base::require(pkg, character.only = TRUE, quietly = TRUE)) {next} + BiocManager::install(pkg) + suppressPackageStartupMessages(invisible(base::library(pkg, character.only = TRUE))) + } } quiet_bioc_load("BiocStyle", "scater") @@ -760,7 +759,7 @@ sub_combined <- clusterTCR(combined[[2]], From the excellent work by Lei Zhang, et al in [Lineage tracking reveals dynamic relationships of T cells in colorectal cancer](https://www.nature.com/articles/s41586-018-0694-x), the authors introduce new methods for looking at clonotypes by cellular origins and cluster identification. Their [startrac](https://github.com/Japrin/STARTRAC) software has been incorporated into scRepertoire. If you are using the output of this specific function, please cite their excellent work. -In order to use the ```StartracDiversity()``` function, you will need to include the product of the ```combinedSeurat()``` function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data,**type** corresponds to the column "Type", which includes the "P" and "T" classifier. The indices can be subseted for a specific patient or examined overall using the **by** variable. Importantly, the function uses only the strict definition of clonotype of the VDJC genes and the CDR3 nucleotide sequence. +In order to use the ```StartracDiversity()``` function, you will need to include the product of the ```combinedSeurat()``` function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data,**type** corresponds to the column "Type", which includes the "P" and "T" classifier. The indices can be subsetted for a specific patient or examined overall using the **by** variable. Importantly, the function uses only the strict definition of clonotype of the VDJC genes and the CDR3 nucleotide sequence. *The indices output includes:* + expa - Clonal Expansion From c1edee628e592784f5a7f39d40320eb79cf04aa1 Mon Sep 17 00:00:00 2001 From: Qile Date: Thu, 10 Aug 2023 12:17:24 +0200 Subject: [PATCH 014/142] more tests and minor changes --- R/data.R | 31 ++++++ R/seuratFunctions.R | 13 ++- R/viz.R | 92 +++++++++--------- man/combineExpression.Rd | 2 +- scRepertoire.Rproj | 2 +- tests/testthat/test-seuratFunctions.R | 20 ++++ tests/testthat/test-seurat_command.R | 31 ++++++ tests/testthat/test-viz.R | 1 + .../combineExpression_new_metadata.rds | Bin 0 -> 7629 bytes 9 files changed, 139 insertions(+), 53 deletions(-) create mode 100644 tests/testthat/test-seuratFunctions.R create mode 100644 tests/testthat/test-seurat_command.R create mode 100644 tests/testthat/testdata/seuratFunctions/combineExpression_new_metadata.rds diff --git a/R/data.R b/R/data.R index a3b676d1..022400b3 100644 --- a/R/data.R +++ b/R/data.R @@ -31,3 +31,34 @@ NULL #' @seealso \code{\link{contig_list}} #' "combined_mini_contig_list" + +# # Code used for creating the combined_mini_contig_list: + +# library(hash, usethis) +# +#data("contig_list", "screp_example") + +#combined_mini_contig_list <- combineTCR( +# contig_list, +# samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), +# ID = c("P", "T", "P", "T", "P", "T") +#) +#all_barcodes <- names(screp_example@active.ident) +#barcode_set <- hash::hash(all_barcodes, all_barcodes) # a worse version of a set +#col_names <- names(combined_mini_contig_list[[1]]) + +#for (i in seq_along(combined_mini_contig_list)) { +# curr_df <- setNames( +# data.frame(replicate(length(col_names), character(0))), col_names +# ) +# len <- 0 +# for (j in seq_along(combined_mini_contig_list[[i]][[1]])) { +# if (is.null(barcode_set[[combined_mini_contig_list[[i]][[1]][[j]]]])) { +# next +# } +# len <- len + 1 +# curr_df[len, ] <- combined_mini_contig_list[[i]][j, ] +# } +# combined_mini_contig_list[[i]] <- curr_df +#} +#usethis::use_data(combined_mini_contig_list) diff --git a/R/seuratFunctions.R b/R/seuratFunctions.R index 08629d17..aa16385e 100644 --- a/R/seuratFunctions.R +++ b/R/seuratFunctions.R @@ -53,9 +53,9 @@ combineExpression <- function( df, sc, - cloneCall="strict", + cloneCall ="strict", chain = "both", - group.by="none", + group.by ="none", proportion = TRUE, filterNA = FALSE, cloneTypes = c( @@ -75,7 +75,7 @@ combineExpression <- function( if (group.by == "none" | !is.null(group.by)) { for (i in seq_along(df)) { if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) } data <- data.frame(df[[i]], stringsAsFactors = FALSE) data2 <- unique(data[,c("barcode", cloneCall)]) @@ -84,8 +84,8 @@ combineExpression <- function( data2 <- data2 %>% group_by(data2[,cloneCall]) %>% summarise(Frequency = n()/nrow(data2)) } else { - data2 <- data2 %>% group_by(data2[,cloneCall]) %>% - summarise(Frequency = n()) + data2 <- data2 %>% group_by(data2[,cloneCall]) %>% + summarise(Frequency = n()) } colnames(data2)[1] <- cloneCall data <- merge(data, data2, by = cloneCall, all = TRUE) @@ -175,9 +175,8 @@ combineExpression <- function( call_time, sc@active.assay ) } - sc -} +} # Qile: I think the barcode column added to the metadata is redundant? Since it matches the row names? #' Highlighting specific clonotypes in Seurat #' diff --git a/R/viz.R b/R/viz.R index 9dbf3302..25024cec 100644 --- a/R/viz.R +++ b/R/viz.R @@ -561,52 +561,56 @@ scatterClonotype <- function( #' @export #' @return ggplot dendrogram of the clone size distribution -clonesizeDistribution <- function(df, cloneCall ="strict", - chain = "both", - method = "ward.D2", - threshold = 1, - group.by = NULL, - split.by = NULL, - exportTable = FALSE) { - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - if(!is.null(group.by)) { - df <- groupList(df, group.by) - } - data <- bind_rows(df) - unique_df <- unique(data[,cloneCall]) - Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) - Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) - colnames(Con.df)[1] <- "clonotype" - for (i in seq_along(df)) { - if (chain != "both") { +clonesizeDistribution <- function( + df, + cloneCall ="strict", + chain = "both", + method = "ward.D2", + threshold = 1, + group.by = NULL, + split.by = NULL, + exportTable = FALSE +) { + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + if(!is.null(group.by)) { + df <- groupList(df, group.by) + } + data <- bind_rows(df) + unique_df <- unique(data[,cloneCall]) + Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) + Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) + colnames(Con.df)[1] <- "clonotype" + for (i in seq_along(df)) { + if (chain != "both") { df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - data <- df[[i]] - data <- data.frame(table(data[,cloneCall]), - stringsAsFactors = FALSE) - colnames(data) <- c(cloneCall, "Freq") - for (y in seq_along(unique_df)){ - clonotype.y <- Con.df$clonotype[y] - location.y <- which(clonotype.y == data[,cloneCall]) - Con.df[y,i+1] <- data[location.y[1],"Freq"] } } - colnames(Con.df)[2:(length(df)+1)] <- names(df) - Con.df[is.na(Con.df)] <- 0 - list <- list() - for (i in seq_along(df)) { - list[[i]] <- Con.df[,i+1] - list[[i]] <- suppressWarnings(fdiscgammagpd(list[[i]], useq = threshold)) - } - names(list) <- names(df) - grid <- 0:10000 - distances <- get_distances(list, grid, modelType="Spliced") - hclust <- hclust(as.dist(distances), method = method) - hcd <- as.dendrogram(hclust) - plot <- plot(hcd) - if (exportTable == TRUE) { return(distances) } - return(plot) + data <- df[[i]] + data <- data.frame(table(data[,cloneCall]), + stringsAsFactors = FALSE) + colnames(data) <- c(cloneCall, "Freq") + for (y in seq_along(unique_df)){ + clonotype.y <- Con.df$clonotype[y] + location.y <- which(clonotype.y == data[,cloneCall]) + Con.df[y,i+1] <- data[location.y[1],"Freq"] + } + } + colnames(Con.df)[2:(length(df)+1)] <- names(df) + Con.df[is.na(Con.df)] <- 0 + list <- list() + for (i in seq_along(df)) { + list[[i]] <- Con.df[,i+1] + list[[i]] <- suppressWarnings(fdiscgammagpd(list[[i]], useq = threshold)) + } + names(list) <- names(df) + grid <- 0:10000 + distances <- get_distances(list, grid, modelType="Spliced") + hclust <- hclust(as.dist(distances), method = method) + hcd <- as.dendrogram(hclust) + plot <- plot(hcd) + if (exportTable) { return(distances) } + return(plot) } #This is the basic color palette for the package diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index a18350a3..d43f56e3 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -44,7 +44,7 @@ clonotype information} \item{addLabel}{This will add a label to the frequency header, allowing the user to try multiple group.by variables or recalculate frequencies after -subseting the data.} +subsetting the data.} } \value{ seurat or SingleCellExperiment object with attached clonotype diff --git a/scRepertoire.Rproj b/scRepertoire.Rproj index 75941c0e..3d3340ee 100644 --- a/scRepertoire.Rproj +++ b/scRepertoire.Rproj @@ -17,4 +17,4 @@ AutoAppendNewline: Yes BuildType: Package PackageUseDevtools: Yes PackageInstallArgs: --no-multiarch --with-keep.source -PackageCheckArgs: --as-cran --use-valgrind +PackageCheckArgs: --as-cran diff --git a/tests/testthat/test-seuratFunctions.R b/tests/testthat/test-seuratFunctions.R new file mode 100644 index 00000000..4cb7625d --- /dev/null +++ b/tests/testthat/test-seuratFunctions.R @@ -0,0 +1,20 @@ +# test script for seuratFunctions.R - testcases are NOT comprehensive! + +test_that("combineExpression works with seurat objects", { + data("combined_mini_contig_list", "screp_example") + combined_test <- combineExpression(combined_mini_contig_list, screp_example) + + expect_length(combined_test@meta.data, 15) + expect_equal(combined_test@meta.data[, 1:8], screp_example@meta.data[, 1:8]) + expect_equal( + combined_test@meta.data[, 9:15], + getdata("seuratFunctions", "combineExpression_new_metadata") + ) +}) + +# TODO more testcases for combineEXpression, especially with SCE objects +# TODO highlightClonotypes +# TODO alluvialClonotypes +# TODO occupiedscRepertoire +# TODO clonalOverlay +# TODO createHTOContigList diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R new file mode 100644 index 00000000..f70d0667 --- /dev/null +++ b/tests/testthat/test-seurat_command.R @@ -0,0 +1,31 @@ +test_that("make_screp_seurat_cmd works", { + data("screp_example", "combined_mini_contig_list") + + test_obj <- combineExpression(combined_mini_contig_list, screp_example) + expect_true(!is.null(test_obj@commands[["combineExpression"]])) + + test_obj <- test_obj@commands[["combineExpression"]] + + expect_identical(test_obj@name, "combineExpression") + expect_identical(class(test_obj@time.stamp), c("POSIXct", "POSIXt")) + expect_identical(test_obj@assay.used, "integrated") + expect_identical( + test_obj@call.string, + "combineExpression(combined_mini_contig_list, screp_example)" + ) + expect_equal( + test_obj@params, + list( + cloneCall = "CTstrict", chain = "both", group.by = "none", + proportion = TRUE, filterNA = FALSE, + cloneTypes = c( + `None ( < X <= 0)` = 0, `Rare (0 < X <= 1e-04)` = 1e-04, + `Small (1e-04 < X <= 0.001)` = 0.001, + `Medium (0.001 < X <= 0.01)` = 0.01, + `Large (0.01 < X <= 0.1)` = 0.1, + `Hyperexpanded (0.1 < X <= 1)` = 1 + ), + addLabel = FALSE + ) + ) +}) diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R index 414c74a2..1ec5f91c 100644 --- a/tests/testthat/test-viz.R +++ b/tests/testthat/test-viz.R @@ -95,6 +95,7 @@ test_that("scatterClonotype works", { # TODO test the exportTable arg }) +# something in `clonesizeDistribution` prints "NULL" to the terminal test_that("clonesizeDistribution works", { expect_doppelganger( "clonesizeDistribution_vignette_plot", diff --git a/tests/testthat/testdata/seuratFunctions/combineExpression_new_metadata.rds b/tests/testthat/testdata/seuratFunctions/combineExpression_new_metadata.rds new file mode 100644 index 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2023 12:50:29 +0200 Subject: [PATCH 015/142] minor spelling and syntax fixes --- R/startrac.R | 29 ++++++++++++++++++----------- inst/WORDLIST | 2 ++ man/Startrac.Rd | 3 +-- man/StartracOut.Rd | 22 +++++++++++++++------- 4 files changed, 36 insertions(+), 20 deletions(-) diff --git a/R/startrac.R b/R/startrac.R index a5173c99..03acf3df 100644 --- a/R/startrac.R +++ b/R/startrac.R @@ -85,9 +85,9 @@ StartracDiversity <- function(sc, guides(fill="none") + theme(axis.title.x = element_blank()) } - if (exportTable == TRUE) { + if (exportTable) { return(indices) - } + } return(plot) } @@ -96,8 +96,7 @@ StartracDiversity <- function(sc, #' The Startrac Class #' -#' The Startrac object store the data for tcr-based T cell dynamics analyis. The slots contained -#' in Startrac object are listed below: +#' The Startrac object store the data for tcr-based T cell dynamics analysis. #' @slot aid character. aid of the object, used for identification of the object. #' For example, patient id. default: "AID" #' @slot cell.data data.frame. Each line for a cell, and these columns as @@ -430,13 +429,21 @@ setMethod("getSig", signature = "Startrac", definition = Startrac.getSig) #' The StartracOUt Class #' #' Object store the result of Startrac.run: -#' @slot proj character. identification of the object. For example, patient id. default: "AID" -#' @slot cluster.data data.frame. Each line for a cluster; contain the cluster level indexes information -#' @slot pIndex.migr data.frame. Each line for a cluster; pairwise migration index between the two locations indicated in the column name. -#' @slot pIndex.tran data.frame. Each line for a cluster; pairwise transition index betwwen the two major clusters indicated by the row name and column name. -#' @slot cluster.sig.data data.frame. Each line for a cluster; contains the p values of cluster indices. -#' @slot pIndex.sig.migr data.frame. Each line for a cluster; contains the p values of pairwise migration indices. -#' @slot pIndex.sig.tran data.frame. Each line for a cluster; contains the p values of pairwise transition indices. +#' @slot proj character. identification of the object. For example, patient id. +#' default: "AID" +#' @slot cluster.data data.frame. Each line for a cluster; contain the cluster +#' level indexes information +#' @slot pIndex.migr data.frame. Each line for a cluster; pairwise migration +#' index between the two locations indicated in the column name. +#' @slot pIndex.tran data.frame. Each line for a cluster; pairwise transition +#' index between the two major clusters indicated by the row name and column +#' name. +#' @slot cluster.sig.data data.frame. Each line for a cluster; contains the p +#' values of cluster indices. +#' @slot pIndex.sig.migr data.frame. Each line for a cluster; contains the p +#' values of pairwise migration indices. +#' @slot pIndex.sig.tran data.frame. Each line for a cluster; contains the p +#' values of pairwise transition indices. #' @slot objects list. other objects #' @name StartracOut #' @rdname StartracOut diff --git a/inst/WORDLIST b/inst/WORDLIST index 2ad426fa..e33e95f3 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -147,3 +147,5 @@ unreturned CMD Codecov coords +autoencoding +screp diff --git a/man/Startrac.Rd b/man/Startrac.Rd index 3783e76b..2f354b11 100644 --- a/man/Startrac.Rd +++ b/man/Startrac.Rd @@ -9,8 +9,7 @@ method definition for running startrac } \description{ -The Startrac object store the data for tcr-based T cell dynamics analyis. The slots contained -in Startrac object are listed below: +The Startrac object store the data for tcr-based T cell dynamics analysis. } \section{Slots}{ diff --git a/man/StartracOut.Rd b/man/StartracOut.Rd index bacd0e5b..0e8378d5 100644 --- a/man/StartracOut.Rd +++ b/man/StartracOut.Rd @@ -11,19 +11,27 @@ Object store the result of Startrac.run: \section{Slots}{ \describe{ -\item{\code{proj}}{character. identification of the object. For example, patient id. default: "AID"} +\item{\code{proj}}{character. identification of the object. For example, patient id. +default: "AID"} -\item{\code{cluster.data}}{data.frame. Each line for a cluster; contain the cluster level indexes information} +\item{\code{cluster.data}}{data.frame. Each line for a cluster; contain the cluster +level indexes information} -\item{\code{pIndex.migr}}{data.frame. Each line for a cluster; pairwise migration index between the two locations indicated in the column name.} +\item{\code{pIndex.migr}}{data.frame. Each line for a cluster; pairwise migration +index between the two locations indicated in the column name.} -\item{\code{pIndex.tran}}{data.frame. Each line for a cluster; pairwise transition index betwwen the two major clusters indicated by the row name and column name.} +\item{\code{pIndex.tran}}{data.frame. Each line for a cluster; pairwise transition +index between the two major clusters indicated by the row name and column +name.} -\item{\code{cluster.sig.data}}{data.frame. Each line for a cluster; contains the p values of cluster indices.} +\item{\code{cluster.sig.data}}{data.frame. Each line for a cluster; contains the p +values of cluster indices.} -\item{\code{pIndex.sig.migr}}{data.frame. Each line for a cluster; contains the p values of pairwise migration indices.} +\item{\code{pIndex.sig.migr}}{data.frame. Each line for a cluster; contains the p +values of pairwise migration indices.} -\item{\code{pIndex.sig.tran}}{data.frame. Each line for a cluster; contains the p values of pairwise transition indices.} +\item{\code{pIndex.sig.tran}}{data.frame. Each line for a cluster; contains the p +values of pairwise transition indices.} \item{\code{objects}}{list. other objects} }} From 8e4cf1c8aac402e3aca192ee256a6cfdb81b5eb6 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 15 Aug 2023 07:28:27 -0500 Subject: [PATCH 016/142] Update DESCRIPTION --- DESCRIPTION | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index bd69f2d3..3bb5f15a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -2,7 +2,8 @@ Package: scRepertoire Title: A toolkit for single-cell immune receptor profiling Version: 1.11.0 Authors@R: c( - person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com")) + person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"), + person(given = "Qile", family = "Yang", role = c("aut", "cre"), email = "qile0317@gmail.com")) Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. License: GPL (>= 2) Encoding: UTF-8 From e5aff14bfc7bf7a712db8339e93df28573b9ca93 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 5 Sep 2023 14:02:53 -0500 Subject: [PATCH 017/142] Create percentAA.R --- R/percentAA.R | 62 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 62 insertions(+) create mode 100644 R/percentAA.R diff --git a/R/percentAA.R b/R/percentAA.R new file mode 100644 index 00000000..8280f17e --- /dev/null +++ b/R/percentAA.R @@ -0,0 +1,62 @@ + + +#' @param df The product of combineTCR(), combineBCR(), +#' expression2List(), or combineExpression(). +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param order Maintain the order of the list when plotting +#' @param scale Converts the graphs into percentage of unique clonotypes. +#' @param exportTable Returns the data frame used for forming the graph +#' @import ggplot2 +#' @export +#' @return ggplot of the total or relative unique clonotypes +percentAA <- function(df, + chain = "TRB", + group.by = NULL, + split.by = NULL, + exportTable = FALSE) + df <- list.input.return(df, split.by) + df <- checkBlanks(df, "CTaa") + for(i in seq_along(df)) { + df[[i]] <- off.the.chain(df[[i]], chain, "CTaa") + } + + for (i in seq_along(df)) { + strings <- df[[i]][,"CTaa"] + strings <- do.call(c,str_split(strings, ";")) + strings <- strings[strings != "NA"] + aa.length <- max(nchar(strings)) + strings <- .padded_strings(strings, aa.length) + strings <- do.call(rbind, strings) + + #Summarizing the % of each position + aa.output <- apply(strings, 2, function(x) { + summary <- as.data.frame(prop.table(table(x, useNA = "always"))) + }) + + #Forming a matrix of % across each position and formatting + res <- suppressWarnings(Reduce(function(...) merge(..., all = TRUE, by="x"), aa.output)) + colnames(res) <- c("AA", paste0("pos.", seq_len(aa.length))) + res[1:20,][is.na(res[1:20,])] <- 0 + ###### + #Need to come up with a summary function + } + #Need to come up with a visualization + +} + +.padded_strings <- function(strings, max.length) { + max_length <- max.length + + x <- lapply(strings, function(str) { + str_len <- nchar(str) + str <- strsplit(str, split = "")[[1]] + if (str_len < max_length) { + c(str, rep(NA, max_length - str_len)) + } else { + str + } + }) + } From f07f0714601e33545ff9bca10342a8dcfb0907a4 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 05:35:11 -0500 Subject: [PATCH 018/142] Update DESCRIPTION --- DESCRIPTION | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 3bb5f15a..89da8d48 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,10 +1,10 @@ Package: scRepertoire Title: A toolkit for single-cell immune receptor profiling -Version: 1.11.0 +Version: 2.0.0 Authors@R: c( person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"), person(given = "Qile", family = "Yang", role = c("aut", "cre"), email = "qile0317@gmail.com")) -Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. +Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. License: GPL (>= 2) Encoding: UTF-8 LazyData: true From d0bbaf5805e4d33a6357674a498ccd7211e81414 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 08:11:33 -0500 Subject: [PATCH 019/142] example overhaul Redid the contig list and seurat sample to reduce overall size and have the most up-to-date seurat version object. Changed the corresponding references in .R files and separated out functions in .R files with multiple exported functions --- R/abundanceContig.R | 103 ++++ R/alluvialClonotypes.R | 126 +++++ R/clonalBias.R | 4 +- R/clonalNetwork.R | 6 +- R/clonalOverlay.R | 60 ++ R/clonesizeDistribution.R | 86 +++ R/combineExpression.R | 190 +++++++ R/compareClonotypes.R | 86 +++ R/createHTOContigList.R | 73 +++ R/data.R | 30 +- R/highlightClonotypes.R | 46 ++ R/lengthContig.R | 102 ++++ R/occupiedscRepertoire.R | 91 +++ R/processing.R | 12 +- R/quantContig.R | 120 ++++ R/scatterClonotypes.R | 112 ++++ R/seuratFunctions.R | 553 ------------------ R/startrac.R | 6 +- R/utils.R | 6 + R/viz.R | 775 -------------------------- R/vizGenes.R | 127 +++++ data/combined_mini_contig_list.rda | Bin 7995 -> 0 bytes data/contig_list.rda | Bin 699172 -> 1206516 bytes data/mini_contig_list.rda | Bin 0 -> 27696 bytes data/screp_example.rda | Bin 1562830 -> 208112 bytes tests/testthat/test-seuratFunctions.R | 6 +- tests/testthat/test-seurat_command.R | 6 +- vignettes/vignette.Rmd | 59 +- 28 files changed, 1392 insertions(+), 1393 deletions(-) create mode 100644 R/abundanceContig.R create mode 100644 R/alluvialClonotypes.R create mode 100644 R/clonalOverlay.R create mode 100644 R/clonesizeDistribution.R create mode 100644 R/combineExpression.R create mode 100644 R/compareClonotypes.R create mode 100644 R/createHTOContigList.R create mode 100644 R/highlightClonotypes.R create mode 100644 R/lengthContig.R create mode 100644 R/occupiedscRepertoire.R create mode 100644 R/quantContig.R create mode 100644 R/scatterClonotypes.R delete mode 100644 R/seuratFunctions.R delete mode 100644 R/viz.R create mode 100644 R/vizGenes.R delete mode 100644 data/combined_mini_contig_list.rda create mode 100644 data/mini_contig_list.rda diff --git a/R/abundanceContig.R b/R/abundanceContig.R new file mode 100644 index 00000000..440071bb --- /dev/null +++ b/R/abundanceContig.R @@ -0,0 +1,103 @@ +#' Demonstrate the relative abundance of clonotypes by group or sample. +#' +#' This function takes the output of combineTCR(), combineBCR(), or +#' expression2List() and displays the number of clonotypes at specific +#' frequencies by sample or group. Visualization can either be a line +#' graph using calculated numbers or if scale = TRUE, the output will +#' be a density plot. Multiple sequencing runs can be group together +#' using the group parameter. If a matrix output for the data is +#' preferred, set exportTable = TRUE. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' abundanceContig(combined, cloneCall = "gene", scale = FALSE) +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param group.by The column header for which you would like to analyze the data. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param order Maintain the order of the list when plotting +#' @param scale Converts the graphs into density plots in order to show +#' relative distributions. +#' @param exportTable Returns the data frame used for forming the graph +#' to the visualization. +#' @importFrom ggplot2 ggplot +#' @export +#' @return ggplot of the total or relative abundance of clonotypes +#' across quanta +abundanceContig <- function(df, + cloneCall = "strict", + chain = "both", + scale=FALSE, + group.by = NULL, + split.by = NULL, + order = TRUE, + exportTable = FALSE) { + df <- list.input.return(df,split.by) + Con.df <- NULL + xlab <- "Abundance" + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + names <- names(df) + if (!is.null(group.by)) { + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + data1 <- parseContigs(df, i, names, cloneCall) + label <- df[[i]][1,group.by] + data1[,paste(group.by)] <- label + Con.df<- rbind.data.frame(Con.df, data1) } + Con.df <- data.frame(Con.df) + col <- length(unique(Con.df[,group.by])) + fill <- group.by + if (scale == TRUE) { ylab <- "Density of Clonotypes" + plot <- ggplot(Con.df, aes(x=Abundance, fill=Con.df[,group.by])) + + geom_density(aes(y=after_stat(scaled)), alpha=0.5, + lwd=0.25, color="black", bw=0.5) + + scale_fill_manual(values = colorblind_vector(col)) + + labs(fill = fill) + } else { ylab <- "Number of Clonotypes" + plot <- ggplot(Con.df, aes(x=Abundance, group.by = values, + color = Con.df[,group.by])) + + geom_line(stat="count") + + scale_color_manual(values = colorblind_vector(col)) + + labs(color = fill)} + } else { + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + data1 <- parseContigs(df, i, names, cloneCall) + Con.df<- rbind.data.frame(Con.df, data1) } + Con.df <- data.frame(Con.df) + if(order) { + Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) + } + col <- length(unique(Con.df$values)) + fill <- "Samples" + if (scale == TRUE) { ylab <- "Density of Clonotypes" + plot <- ggplot(Con.df, aes(Abundance, fill=values)) + + geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, + color="black", bw=0.5) + + scale_fill_manual(values = colorblind_vector(col)) + + labs(fill = fill) + } else { ylab <- "Number of Clonotypes" + plot <- ggplot(Con.df, aes(x=Abundance, group = values, + color = values)) + + geom_line(stat="count") + + scale_color_manual(values = colorblind_vector(col)) + + labs(color = fill)} } + if (exportTable == TRUE) { return(Con.df) } + plot <- plot + scale_x_log10() + ylab(ylab) + xlab(xlab) + + theme_classic() + return(plot) +} \ No newline at end of file diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R new file mode 100644 index 00000000..c3d9943e --- /dev/null +++ b/R/alluvialClonotypes.R @@ -0,0 +1,126 @@ +#Making lodes to function in alluvial plots +#' @importFrom ggalluvial to_lodes_form +makingLodes <- function(meta2, color, alpha, facet, set.axes) { + if (!is.null(color) & !is.null(alpha) & !is.null(facet)) { + lodes <- to_lodes_form(meta2,key="x",value="stratum",id="alluvium", + axes=set.axes,diffuse=c(as.name(color),as.name(alpha),as.name(facet))) + } else if (!is.null(color) & !is.null(alpha) & is.null(facet)) { + lodes <- to_lodes_form(meta2,key="x",value="stratum",id="alluvium", + axes = set.axes, diffuse = c(as.name(color), as.name(alpha))) + } else if (!is.null(color) & is.null(alpha) & !is.null(facet)) { + lodes <- to_lodes_form(meta2,key="x",value="stratum",id ="alluvium", + axes =set.axes, diffuse = c(as.name(color), as.name(facet))) + } else if (is.null(color) & is.null(alpha) & !is.null(facet)) { + lodes <- to_lodes_form(meta2, key = "x", value = "stratum", + id="alluvium",axes=set.axes,diffuse=c(as.name(alpha),as.name(facet))) + } else if (is.null(color) & is.null(alpha) & !is.null(facet)) { + lodes <- to_lodes_form(meta2, key = "x", value = "stratum", + id = "alluvium", axes = set.axes, diffuse = c(as.name(facet))) + } else if (!is.null(color) & is.null(alpha) & is.null(facet)) { + lodes <- to_lodes_form(meta2, key = "x", value = "stratum", + id = "alluvium", axes = set.axes, diffuse = c(as.name(color))) + } else if (is.null(color) & !is.null(alpha) & is.null(facet)) { + lodes <- to_lodes_form(meta2, key = "x", value = "stratum", + id = "alluvium", axes = set.axes, diffuse = c(as.name(alpha))) + } else { lodes <- to_lodes_form(meta2, key = "x", value = "stratum", + id = "alluvium", axes = set.axes)} + return(lodes) } + +#' Exploring interaction of clonotypes by seurat or SCE dynamics +#' +#' View the proportional contribution of clonotypes by seurat or SCE object +#' meta data after combineExpression(). The visualization is based on the +#' ggalluvial package, which requires the aesthetics to be part of the axes +#' that are visualized. Therefore, alpha, facet, and color should be part of +#' the the axes you wish to view or will add an additional stratum/column to +#' the end of the graph. +#' +#' @examples +#' #Getting the combined contigs +#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example")) +#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) +#' sce <- Seurat::as.SingleCellExperiment(sce) +#' +#' #Using combineExpresion() +#' sce <- combineExpression(combined, sce) +#' +#' #Using alluvialClonotypes() +#' alluvialClonotypes(sce, cloneCall = "gene", +#' y.axes = c("Patient", "ident"), color = "ident") +#' +#' @param sc The seurat or SCE object to visualize after combineExpression(). +#' For SCE objects, the cluster variable must be in the meta data under +#' "cluster". +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt) or CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param y.axes The columns that will separate the proportional +#' visualizations. +#' @param color The column header or clonotype(s) to be highlighted. +#' @param facet The column label to separate. +#' @param alpha The column header to have gradated opacity. +#' @import ggplot2 +#' +#' @importFrom ggalluvial StatStratum geom_flow geom_stratum to_lodes_form geom_alluvium +#' @importFrom dplyr %>% mutate +#' +#' @export +#' @return Alluvial ggplot comparing clonotype distribution across +#' selected parameters. +alluvialClonotypes <- function(sc, + cloneCall = c("gene", "nt", "aa", "strict"), + chain = "both", + y.axes = NULL, + color = NULL, + alpha = NULL, + facet = NULL) { + checkSingleObject(sc) + cloneCall <- theCall(cloneCall) + if (length(y.axes) == 0) { + stop("Make sure you have selected the variable(s) to visualize") } + meta <- grabMeta(sc) + if (chain != "both") { + meta <- off.the.chain(meta, chain, cloneCall) + } + meta$barcodes <- rownames(meta) + check <- colnames(meta) == color + if (length(unique(check)) == 1 & unique(check)[1] == FALSE & + !is.null(color)) { + meta <- meta %>% mutate("clonotype(s)" = ifelse(meta[,cloneCall] %in% + color, "Selected", "Other")) + color <- "clonotype(s)" } + + y.axes <- unique(c(y.axes, color, alpha, facet)) + set.axes <- seq_along(y.axes) + meta2 <- meta[,c(y.axes, color, alpha, facet, cloneCall, "barcodes")] + meta2 <- unique(na.omit(meta2[!duplicated(as.list(meta2))])) + + lodes <- makingLodes(meta2, color, alpha, facet, set.axes) + plot <- ggplot(data = lodes, aes(x = x, stratum = stratum, + alluvium = alluvium, label = stratum)) + + geom_stratum() + theme_classic() + + theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) + if (is.null(color) & is.null(alpha)) { + plot <- plot + geom_alluvium() + } else if (!is.null(color) & is.null(alpha)) { + plot <- plot+geom_flow(aes(fill = lodes[,color]), + stat = "alluvium", lode.guidance = "forward") + labs(fill = color) + } else if (is.null(color) & !is.null(alpha)) { + plot <- plot + geom_flow(aes(alpha = lodes[,alpha]), stat = "alluvium", + lode.guidance = "forward") + labs(alpha = alpha) + }else { + plot <- plot+geom_flow(aes(alpha=lodes[,alpha], fill=lodes[,color]), + stat = "alluvium", lode.guidance = "forward") + + labs(fill = color, alpha = alpha) } + if (length(facet) == 1 & length(facet) < 2) { + plot <- plot + facet_wrap(.~lodes[,facet], scales="free_y") + } else if (length(facet) == 0) { plot <- plot } + plot <- plot + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) + return(plot) +} \ No newline at end of file diff --git a/R/clonalBias.R b/R/clonalBias.R index 382c995c..f1c3777e 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -15,8 +15,8 @@ #' rep(c("P", "T"), 3)) #' #' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) +#' scRep_example <- get(data("scRep_example")) +#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #' #' #Using combineExpresion() #' sce <- combineExpression(combined, sce) diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index 0442c7ba..f7bf4cc6 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -13,13 +13,13 @@ #' rep(c("P", "T"), 3)) #' #' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) +#' scRep_example <- get(data("scRep_example")) #' #' #Using combineExpresion() -#' screp_example <- combineExpression(combined, screp_example) +#' scRep_example <- combineExpression(combined, scRep_example) #' #' #Using clonalNetwork() -#' clonalNetwork(screp_example, reduction = "umap", +#' clonalNetwork(scRep_example, reduction = "umap", #' identity = "cluster") #' } #' diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R new file mode 100644 index 00000000..9309d3b9 --- /dev/null +++ b/R/clonalOverlay.R @@ -0,0 +1,60 @@ +#' Visualize distribution of clonal frequency overlaid on dimensional reduction plots +#' +#' This function allows the user to visualize the clonal expansion by overlaying the +#' cells with specific clonal frequency onto the dimensional reduction plots in Seurat. +#' Credit to the idea goes to Drs Andreatta and Carmona and their work with +#' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. +#' +#' @examples +#' #Getting the combined contigs +#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example")) +#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) +#' +#' #Using combineExpresion() +#' sce <- combineExpression(combined, sce) +#' +#' #Using clonalOverlay() +#' clonalOverlay(sce, reduction = "umap", freq.cutpoint = 0.3, bins = 5) +#' +#' @param sc The seurat or SCE object to visualize after combineExpression(). +#' @param reduction The dimensional reduction to visualize +#' @param freq.cutpoint The overlay cutpoint to include, this corresponds to the +#' Frequency variable in the single-cell objecter +#' @param bins The number of contours to the overlay +#' @param facet meta data variable to facet the comparison +#' +#' @import ggplot2 +#' @importFrom SeuratObject Embeddings +#' @export +#' @author Francesco Mazziotta, Nick Borcherding +#' +#' @return ggplot object + +clonalOverlay <- function(sc, + reduction = NULL, + freq.cutpoint = 30, + bins = 25, + facet = NULL) { + checkSingleObject(sc) + tmp <- data.frame(grabMeta(sc), identity = sc@active.ident, get.coord(sc, reduction)) + if (!is.null(facet)) { + facet <- tmp[,facet] + tmp <- data.frame(facet, tmp) + } + tmp$include <- ifelse(tmp$Frequency >= freq.cutpoint, "Yes", NA) + tmp2 <- subset(tmp, include == "Yes") + plot <- ggplot(tmp2, mapping = aes(x=tmp2[,(ncol(tmp2)-2)], y = tmp2[,(ncol(tmp2)-1)])) + + geom_point(tmp, mapping = aes(x=as.numeric(tmp[,(ncol(tmp)-2)]), y = as.numeric(tmp[,(ncol(tmp)-1)]), color = tmp[,"identity"]), size= 0.5) + + geom_density_2d(color = "black", lwd=0.25, bins = bins) + + theme_classic() + + labs(color = "Active Identity") + + xlab("Dimension 1") + ylab("Dimension 2") + if (!is.null(facet)) { + plot <- plot + facet_wrap(~facet) + } + plot +} \ No newline at end of file diff --git a/R/clonesizeDistribution.R b/R/clonesizeDistribution.R new file mode 100644 index 00000000..87a43dbf --- /dev/null +++ b/R/clonesizeDistribution.R @@ -0,0 +1,86 @@ +#' Hierarchical clustering of clonotypes on clonotype size and +#' Jensen-Shannon divergence +#' +#' This function produces a hierarchical clustering of clonotypes by sample +#' using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold +#' model in +#' \href{https://bioconductor.org/packages/devel/bioc/html/powerTCR.html}{powerTCR R package}. +#' Please read and cite PMID: 30485278 if using the function for analyses. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' clonesizeDistribution(combined, cloneCall = "strict", method="ward.D2") +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param threshold Numerical vector containing the thresholds +#' the grid search was performed over. +#' @param method The clustering parameter for the dendrogram. +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param exportTable Returns the data frame used for forming the graph. +#' @importFrom dplyr bind_rows +#' @importFrom ggplot2 ggplot +#' @importFrom powerTCR fdiscgammagpd get_distances +#' @export +#' @return ggplot dendrogram of the clone size distribution + +clonesizeDistribution <- function( + df, + cloneCall ="strict", + chain = "both", + method = "ward.D2", + threshold = 1, + group.by = NULL, + split.by = NULL, + exportTable = FALSE +) { + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + if(!is.null(group.by)) { + df <- groupList(df, group.by) + } + data <- bind_rows(df) + unique_df <- unique(data[,cloneCall]) + Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) + Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) + colnames(Con.df)[1] <- "clonotype" + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + data <- df[[i]] + data <- data.frame(table(data[,cloneCall]), + stringsAsFactors = FALSE) + colnames(data) <- c(cloneCall, "Freq") + for (y in seq_along(unique_df)){ + clonotype.y <- Con.df$clonotype[y] + location.y <- which(clonotype.y == data[,cloneCall]) + Con.df[y,i+1] <- data[location.y[1],"Freq"] + } + } + colnames(Con.df)[2:(length(df)+1)] <- names(df) + Con.df[is.na(Con.df)] <- 0 + list <- list() + for (i in seq_along(df)) { + list[[i]] <- Con.df[,i+1] + list[[i]] <- suppressWarnings(fdiscgammagpd(list[[i]], useq = threshold)) + } + names(list) <- names(df) + grid <- 0:10000 + distances <- get_distances(list, grid, modelType="Spliced") + hclust <- hclust(as.dist(distances), method = method) + hcd <- as.dendrogram(hclust) + plot <- plot(hcd) + if (exportTable) { return(distances) } + return(plot) +} \ No newline at end of file diff --git a/R/combineExpression.R b/R/combineExpression.R new file mode 100644 index 00000000..ad19aa70 --- /dev/null +++ b/R/combineExpression.R @@ -0,0 +1,190 @@ +#' Adding clonotype information to a Seurat or SCE object +#' +#' This function adds the immune receptor information to the Seurat or +#' SCE object to the meta data. By default this function also calculates +#' the frequencies of the clonotypes by sequencing run (group.by = "none"). +#' To change how the frequencies are calculated, select a column header for +#' the group.by variable. Importantly, before using combineExpression() +#' ensure the barcodes of the seurat or SCE object match the barcodes in the +#' output of the combinedContig() call. Check changeNames() to change the +#' prefix of the Seurat object. If combining more than one immune receptor +#' type, barcodes with both receptors will be removed during the combination +#' process. +#' +#' @examples +#' #Getting the combined contigs +#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example")) +#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) +#' sce <- Seurat::as.SingleCellExperiment(sce) +#' +#' #Using combineExpresion() +#' sce <- combineExpression(combined, sce) +#' +#' @param df The product of CombineTCR() or CombineBCR() or a list of +#' both c(CombineTCR(), combineBCR()) +#' @param sc The seurat or SingleCellExperiment (SCE) object to attach +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt) CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param group.by The column label in the combined contig object in which +#' clonotype frequency will be calculated. "none" will keep the list as is, +#' while NULL will merge all the contigs into a single data frame. +#' @param proportion Whether to use the total frequency (FALSE) or the +#' proportion (TRUE) of the clonotype based on the group.by variable. +#' @param cloneTypes The bins for the grouping based on frequency +#' @param filterNA Method to subset seurat object of barcodes without +#' clonotype information +#' @param addLabel This will add a label to the frequency header, allowing +#' the user to try multiple group.by variables or recalculate frequencies after +#' subsetting the data. +#' @importFrom dplyr bind_rows %>% summarise +#' @importFrom rlang %||% +#' @importFrom SummarizedExperiment colData<- colData +#' @export +#' @return seurat or SingleCellExperiment object with attached clonotype +#' information +#' +combineExpression <- function( + df, + sc, + cloneCall ="strict", + chain = "both", + group.by ="none", + proportion = TRUE, + filterNA = FALSE, + cloneTypes = c( + Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1 + ), + addLabel = FALSE +) { + call_time <- Sys.time() + + options( dplyr.summarise.inform = FALSE ) + cloneTypes <- c(None = 0, cloneTypes) + df <- checkList(df) + cloneCall <- theCall(cloneCall) + Con.df <- NULL + meta <- grabMeta(sc) + cell.names <- rownames(meta) + if (group.by == "none" | !is.null(group.by)) { + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + data <- data.frame(df[[i]], stringsAsFactors = FALSE) + data2 <- unique(data[,c("barcode", cloneCall)]) + data2 <- na.omit(data2[data2[,"barcode"] %in% cell.names,]) + if (proportion) { + data2 <- data2 %>% group_by(data2[,cloneCall]) %>% + summarise(Frequency = n()/nrow(data2)) + } else { + data2 <- data2 %>% group_by(data2[,cloneCall]) %>% + summarise(Frequency = n()) + } + colnames(data2)[1] <- cloneCall + data <- merge(data, data2, by = cloneCall, all = TRUE) + data <- data[,c("barcode", "CTgene", "CTnt", + "CTaa", "CTstrict", "Frequency")] + Con.df <- rbind.data.frame(Con.df, data) + } + } else if (group.by != "none" | is.null(group.by)) { + data <- data.frame(bind_rows(df), stringsAsFactors = FALSE) + data2 <- na.omit(unique(data[,c("barcode", cloneCall, group.by)])) + data2 <- data2[data2[,"barcode"] %in% cell.names, ] + data2 <- as.data.frame(data2 %>% group_by(data2[,cloneCall], + data2[,group.by]) %>% summarise(Frequency = n())) + if(!is.null(group.by)) { + colnames(data2)[c(1,2)] <- c(cloneCall, group.by) + x <- unique(data[,group.by]) + for (i in seq_along(x)) { + sub1 <- subset(data, data[,group.by] == x[i]) + sub2 <- subset(data2, data2[,group.by] == x[i]) + merge <- merge(sub1, sub2, by=cloneCall) + if (proportion) { + merge$Frequency <- merge$Frequency/length(merge$Frequency) + } + Con.df <- rbind.data.frame(Con.df, merge) + } + nsize <- Con.df %>% group_by(Con.df[,paste0(group.by, ".x")]) %>% summarise(n = n()) + } else { + if (proportion) { + data <- data %>% + group_by(data[,cloneCall]) %>% + mutate(Frequency = n()/nrow(data)) + } else { + data <- data %>% + group_by(data[,cloneCall]) %>% + mutate(Frequency = n()) + } + Con.df <- data[,c("barcode", "CTgene", "CTnt", + "CTaa", "CTstrict", "Frequency")] + nsize <- length(Con.df) + } + + } + + Con.df$cloneType <- NA + for (x in seq_along(cloneTypes)) { names(cloneTypes)[x] <- + paste0(names(cloneTypes[x]), ' (', cloneTypes[x-1], + ' < X <= ', cloneTypes[x], ')') } + for (i in 2:length(cloneTypes)) { Con.df$cloneType <- + ifelse(Con.df$Frequency > cloneTypes[i-1] & Con.df$Frequency + <= cloneTypes[i], names(cloneTypes[i]), Con.df$cloneType) } + PreMeta <- unique(Con.df[,c("barcode", "CTgene", "CTnt", + "CTaa", "CTstrict", "Frequency", "cloneType")]) + dup <- PreMeta$barcode[which(duplicated(PreMeta$barcode))] + PreMeta <- PreMeta[PreMeta$barcode %!in% dup,] + rownames(PreMeta) <- PreMeta$barcode + if (group.by != "none" && addLabel) { + location <- which(colnames(PreMeta) == "Frequency") + colnames(PreMeta)[location] <- paste0("Frequency.", group.by) + } + + warn_str <- "< 1% of barcodes match: Ensure the barcodes in + the Seurat object match the barcodes in the combined immune receptor + list from scRepertoire - most common issue is the addition of the + prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() + functions" + + if (is_seurat_object(sc)) { + if (length(which(rownames(PreMeta) %in% + rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { + warning(warn_str) + } + col.name <- names(PreMeta) %||% colnames(PreMeta) + sc[[col.name]] <- PreMeta + } else { + rownames <- rownames(colData(sc)) + if (length(which(rownames(PreMeta) %in% + rownames))/length(rownames) < 0.01) { + warning(warn_str) } + colData(sc) <- cbind(colData(sc), PreMeta[rownames,])[, union(colnames(colData(sc)), colnames(PreMeta))] + rownames(colData(sc)) <- rownames + } + if (filterNA) { sc <- filteringNA(sc) } + sc$cloneType <- factor(sc$cloneType, levels = rev(names(cloneTypes))) + + if(is_seurat_object(sc)) { + sc@commands[["combineExpression"]] <- make_screp_seurat_cmd( + call_time, sc@active.assay + ) + } + sc +} # Qile: I think the barcode column added to the metadata is redundant? Since it matches the row names? + + + + + + + + + + + diff --git a/R/compareClonotypes.R b/R/compareClonotypes.R new file mode 100644 index 00000000..9a103788 --- /dev/null +++ b/R/compareClonotypes.R @@ -0,0 +1,86 @@ +#' Demonstrate the difference in clonal proportion between clonotypes +#' +#' This function produces an alluvial or area graph of the proportion of +#' the indicated clonotypes for all or selected samples. Clonotypes can be +#' selected using the clonotypes parameter with the specific sequence of +#' interest or using the number parameter with the top n clonotypes by +#' proportion to be visualized. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' compareClonotypes(combined, numbers = 10, +#' samples = c("PX_P", "PX_T"), cloneCall="aa") +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param samples The specific samples to isolate for visualization. +#' @param clonotypes The specific sequences of interest. +#' @param numbers The top number clonotype sequences per group +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param graph The type of graph produced, either "alluvial" or "area". +#' @param exportTable Returns the data frame used for forming the graph. +#' @import ggplot2 +#' +#' @export +#' @return ggplot of the proportion of total sequencing read of +#' selecting clonotypes +compareClonotypes <- function(df, + cloneCall = "strict", + chain = "both", + samples = NULL, + clonotypes = NULL, + numbers = NULL, + split.by = NULL, + graph = "alluvial", + exportTable = FALSE){ + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + if (!is.null(numbers) & !is.null(clonotypes)) { + stop("Make sure your inputs are either numbers or clonotype sequences.") + } + Con.df <- NULL + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + tbl <- as.data.frame(table(df[[i]][,cloneCall])) + tbl[,2] <- tbl[,2]/sum(tbl[,2]) + colnames(tbl) <- c("Clonotypes", "Proportion") + tbl$Sample <- names(df[i]) + Con.df <- rbind.data.frame(Con.df, tbl) + } + if (!is.null(samples)) { + Con.df <- Con.df[Con.df$Sample %in% samples,] } + if (!is.null(clonotypes)) { + Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] } + if (!is.null(numbers)) { + top <- Con.df %>% + group_by(Con.df[,3]) %>% + slice_max(n = numbers, order_by = Proportion, with_ties = FALSE) + Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] } + if (nrow(Con.df) < length(unique(Con.df$Sample))) { + stop("Reasses the filtering strategies here, there is not + enough clonotypes to examine.") } + if (exportTable == TRUE) { return(Con.df)} + + plot <- ggplot(Con.df, aes(x = Sample, fill = Clonotypes, group = Clonotypes, + stratum = Clonotypes, alluvium = Clonotypes, + y = Proportion, label = Clonotypes)) + + theme_classic() + + theme(axis.title.x = element_blank()) + if (graph == "alluvial") { + plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") + } else if (graph == "area") { + plot <- plot + + geom_area(aes(group = Clonotypes), color = "black") } + return(plot) +} \ No newline at end of file diff --git a/R/createHTOContigList.R b/R/createHTOContigList.R new file mode 100644 index 00000000..507bfd0c --- /dev/null +++ b/R/createHTOContigList.R @@ -0,0 +1,73 @@ +#' Generate a contig list from a multiplexed experiment +#' +#' Multiplexing single-cell sequencing runs is an efficient method for quantifying +#' multiple samples or conditions simultaneously. Unfortunately, the hashing information +#' is not stored in the TCR sequence data. In order preprocess and form a contig list +#' for downstream analysis in scRepertoire, createHTOContigList() take the filtered contig +#' annotation output and the single-cell RNA object to create the list. If using an +#' integrated single-cell object, it is recommended to split the object by sequencing run +#' and remove extra prefixes and suffixes on the barcode before using createHTOContigList(). +#' Alternatively, the variable multi.run can be used to separate a list of contigs by a meta data +#' variable. This may have issues with the repeated barcodes. +#' +#' @examples +#' \dontrun{ +#' filtered.contig <- read.csv(".../Sample/outs/filtered_contig_annotations.csv") +#' contig.list <- createHTOContigList(contig = filtered.contig, +#' sc = Seurat.Obj, group.by = "HTO_maxID") +#' } +#' +#' @param contig The filtered contig annotation file from multiplexed experiment +#' @param sc The Seurat or SCE object. +#' @param group.by One or more meta data headers to create the contig list based on. +#' If more than one header listed, the function combines them into a single variable. +#' @param multi.run If using integrated single-cell object, the meta data variable +#' that indicates the sequencing run. +#' @export +#' @return Returns a list of contigs corresponding to the multiplexed Seurat +#' or Single-Cell Experiment object + + +createHTOContigList <- function(contig, + sc, + group.by = NULL, + multi.run = NULL){ + contig.list <- NULL + checkSingleObject(sc) + meta <- grabMeta(sc) + if (length(group.by) > 1) { + meta["group.by"] <- apply(meta[ , group.by] , 1 , paste , collapse = "." ) + } else { + meta[,"group.by"] <- meta[ , group.by] + } + unique.groups <- unique(meta$group.by) + + if (!is.null(multi.run) & inherits(x=contig, what ="list")) { + tmp.list <- list() + runs <- unique(meta[,multi.run]) + for (x in seq_along(contig)) { + cont.tmp <- contig[[x]] + cont.tmp$barcode <- paste0(cont.tmp$barcode, "_", x) + meta.tmp <- meta[meta[,multi.run] == runs[x],] #Subset meta in batch + rownames(meta.tmp) <- substring(rownames(meta.tmp), nchar(rownames(meta.tmp)[1])-17, nchar(rownames(meta.tmp)[1])) + cont.tmp <- cont.tmp[cont.tmp$barcode %in% row.names(meta.tmp) ] #subset contig in new meta + for (y in seq_along(unique.groups)) { + sub.con <- cont.tmp[cont.tmp$barcode %in% rownames(subset(meta.tmp, group.by == unique.groups[y])),] + tmp.list[[y]] <- sub.con + } + if(x == 1) { + contig.list <- tmp.list + } else { + contig.list <- c(contig.list, tmp.list) + } + } + } else { + cont.tmp <- contig[contig$barcode %in% rownames(meta), ] + for (i in seq_along(unique.groups)) { + sub.con <- cont.tmp[cont.tmp$barcode %in% rownames(subset(meta, group.by == unique.groups[i])),] + contig.list[[i]] <- sub.con + } + names(contig.list) <- unique.groups + } + contig.list +} \ No newline at end of file diff --git a/R/data.R b/R/data.R index 022400b3..33615823 100644 --- a/R/data.R +++ b/R/data.R @@ -11,7 +11,7 @@ NULL #' @description The object is compatible with `contig_list` and the TCR #' sequencing data can be added with `combineExpression`. #' -#' @name screp_example +#' @name scRep_example #' @docType data #' NULL @@ -20,9 +20,9 @@ NULL #' #' @description A list of 6 dataframes of T cell contigs outputted from the #' `filtered_contig_annotation` files, but subsetted to about 92 valid T cells -#' which correspond to the same barcodes found in `screp_example` +#' which correspond to the same barcodes found in `scRep_example` #' -#' @usage data("combined_mini_contig_list") +#' @usage data("mini_contig_list") #' #' @format An R `list` of `data.frame` objects #' @@ -30,35 +30,35 @@ NULL #' #' @seealso \code{\link{contig_list}} #' -"combined_mini_contig_list" +"mini_contig_list" -# # Code used for creating the combined_mini_contig_list: +# # Code used for creating the mini_contig_list: # library(hash, usethis) # -#data("contig_list", "screp_example") +#data("contig_list", "scRep_example") -#combined_mini_contig_list <- combineTCR( +#mini_contig_list <- combineTCR( # contig_list, # samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), # ID = c("P", "T", "P", "T", "P", "T") #) -#all_barcodes <- names(screp_example@active.ident) +#all_barcodes <- names(scRep_example@active.ident) #barcode_set <- hash::hash(all_barcodes, all_barcodes) # a worse version of a set -#col_names <- names(combined_mini_contig_list[[1]]) +#col_names <- names(mini_contig_list[[1]]) -#for (i in seq_along(combined_mini_contig_list)) { +#for (i in seq_along(mini_contig_list)) { # curr_df <- setNames( # data.frame(replicate(length(col_names), character(0))), col_names # ) # len <- 0 -# for (j in seq_along(combined_mini_contig_list[[i]][[1]])) { -# if (is.null(barcode_set[[combined_mini_contig_list[[i]][[1]][[j]]]])) { +# for (j in seq_along(mini_contig_list[[i]][[1]])) { +# if (is.null(barcode_set[[mini_contig_list[[i]][[1]][[j]]]])) { # next # } # len <- len + 1 -# curr_df[len, ] <- combined_mini_contig_list[[i]][j, ] +# curr_df[len, ] <- mini_contig_list[[i]][j, ] # } -# combined_mini_contig_list[[i]] <- curr_df +# mini_contig_list[[i]] <- curr_df #} -#usethis::use_data(combined_mini_contig_list) +#usethis::use_data(mini_contig_list) diff --git a/R/highlightClonotypes.R b/R/highlightClonotypes.R new file mode 100644 index 00000000..d1d4106e --- /dev/null +++ b/R/highlightClonotypes.R @@ -0,0 +1,46 @@ +#' Highlighting specific clonotypes in Seurat +#' +#' Use a specific clonotype sequence to highlight on top of the dimensional +#' reduction in seurat object. +#' +#' @examples +#' #' #Getting the combined contigs +#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example ")) +#' +#' #Using combineExpresion() +#' scRep_example <- combineExpression(combined, scRep_example ) +#' +#' #Using highlightClonotype() +#' scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", +#' sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF")) +#' +#' @param sc The Seurat object to attach +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param sequence The specific sequence or sequence to highlight +#' +#' @export +#' @return Seurat object with new meta data column for indicated clones +highlightClonotypes <- function(sc, + cloneCall = c("gene", "nt", "aa", "strict"), + sequence = NULL){ + if (!inherits(x=sc, what ="Seurat")) { + stop("Object indicated is not of class 'Seurat', make sure you + are using the correct data.") } + cloneCall <- theCall(cloneCall) + meta <- grabMeta(sc) + meta$highlight <- NA + for(i in seq_along(sequence)) { + meta$highlight <- ifelse(meta[,cloneCall] == sequence[i], + sequence[i], meta$highlight) + } + meta <- meta[,-(which(colnames(meta) == "ident"))] + col.name <- names(meta) %||% colnames(meta) + sc[[col.name]] <- meta + sc +} \ No newline at end of file diff --git a/R/lengthContig.R b/R/lengthContig.R new file mode 100644 index 00000000..b1ba15eb --- /dev/null +++ b/R/lengthContig.R @@ -0,0 +1,102 @@ +#' Demonstrate the distribution of lengths filtered contigs. +#' +#' This function takes the output of combineTCR(), combineBCR(), or +#' expression2List() and displays either the nucleotide (nt) or amino +#' acid (aa) sequence length. The sequence length visualized can be +#' selected using the chains parameter, either the combined clonotype +#' (both chains) or across all single chains. Visualization can either +#' be a histogram or if scale = TRUE, the output will be a density plot. +#' Multiple sequencing runs can be group together using the +#' group parameter. If a matrix output for the data is preferred, set +#' exportTable = TRUE. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' lengthContig(combined, cloneCall="aa", chain = "both") +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), +#' CDR3 amino acid (aa). +#' @param group.by The group header for which you would like to analyze +#' the data. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param scale Converts the graphs into density plots in order to show +#' relative distributions. +#' @param order Maintain the order of the list when plotting +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param exportTable Returns the data frame used for forming the graph. +#' @importFrom stringr str_split +#' @importFrom ggplot2 ggplot +#' @export +#' @return ggplot of the discrete or relative length distributions of +#' clonotype sequences +lengthContig <- function(df, + cloneCall = "aa", + chain = "both", + group.by = NULL, + split.by = NULL, + order = TRUE, + scale = FALSE, + exportTable = FALSE) { + df <- list.input.return(df, split.by) + if(cloneCall == "nt") { cloneCall <- "CTnt" + ylab <- "CDR3 (NT)" + } else if (cloneCall == "aa") { cloneCall <- "CTaa" + ylab <- "CDR3 (AA)" + } else { stop("Please make a selection of the type of + CDR3 sequence to analyze by using `cloneCall`") } + AB <- grep("TRA", df[[1]]$TCR1) + IG <- grep("IGH", df[[1]]$TCR1) + if (length(AB) > 1 && length(IG) == 0) { + c1 <- "TRA" + c2 <- "TRB" + } else if (length(AB) > 1) { + c1 <- "IGH" + c2 <- "IGL" + } else { + c1 <- "TRG" + c2 <- "TRD" + } + xlab <- "Length" + Con.df <- NULL + Con.df <- lengthDF(df, cloneCall, chain, group.by, c1, c2) + if(is.null(group.by) & order) { + Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) + } + names <- names(df) + if (!is.null(group.by)) { + fill <- group.by + col <- length(unique(Con.df[,group.by])) + if (scale == TRUE) { yplus <- "Percent of " + plot <- ggplot(Con.df, aes(fill=Con.df[,group.by], + length,(after_stat(count))/sum(after_stat(count))*100)) + + geom_density(aes(y=after_stat(scaled)),alpha=.5,lwd=.25,color="black") + } else { yplus <- "Number of " + plot<-ggplot(Con.df,aes(as.factor(length),fill=Con.df[,group.by]))+ + geom_bar(position = position_dodge2(preserve = "single"), + color="black", lwd=0.25, width=0.9) + + scale_x_discrete(breaks = round(seq(min(Con.df$length), + max(Con.df$length), by = 5),10)) } + } else if (is.null(group.by)){ + fill <- "Samples" + col <- length(unique(Con.df$values)) + if (scale == TRUE) { yplus <- "Percent of " + plot <- ggplot(Con.df, aes(length, (after_stat(count))/sum(after_stat(count))*100, + fill=values)) + geom_density(aes(y=after_stat(scaled)), alpha=0.5, + lwd=0.25, color="black") + } else { yplus <- "Number of " + plot <- ggplot(Con.df, aes(as.factor(length), fill=values)) + + geom_bar(position = position_dodge2(preserve = "single"), + color="black", lwd=0.25) + + scale_x_discrete(breaks = round(seq(min(Con.df$length), + max(Con.df$length), by = 5),10))} } + plot <- plot + scale_fill_manual(values = colorblind_vector(col)) + + labs(fill = fill) + ylab(paste(yplus, ylab, sep="")) + + xlab(xlab) + theme_classic() + if (exportTable == TRUE) { return(Con.df) } + return(plot)} \ No newline at end of file diff --git a/R/occupiedscRepertoire.R b/R/occupiedscRepertoire.R new file mode 100644 index 00000000..f881a7a2 --- /dev/null +++ b/R/occupiedscRepertoire.R @@ -0,0 +1,91 @@ +#' Visualize the number of single cells with clonotype frequencies by cluster +#' +#' View the count of clonotypes frequency group in seurat or SCE object +#' meta data after combineExpression(). The visualization will take the +#' new meta data variable "cloneType" and plot the number of cells with +#' each designation using a secondary variable, like cluster. Credit to +#' the idea goes to Drs. Carmona and Andreatta and their work with +#' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. +#' +#' @examples +#' #Getting the combined contigs +#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example")) +#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) +#' +#' #Using combineExpresion() +#' sce <- combineExpression(combined, sce) +#' +#' #Using occupiedscRepertoire() +#' occupiedscRepertoire(sce, x.axis = "ident") +#' table <- occupiedscRepertoire(sce, x.axis = "ident", exportTable = TRUE) +#' +#' @param sc The Seurat or SCE object to visualize after combineExpression(). +#' @param x.axis The variable in the meta data to graph along the x.axis +#' @param label Include the number of clonotype in each category by x.axis variable +#' @param facet.by The column header used for faceting the graph +#' @param proportion Convert the stacked bars into relative proportion +#' @param na.include Visualize NA values or not. +#' @param exportTable Exports a table of the data into the global +#' environment in addition to the visualization +#' @importFrom dplyr %>% group_by mutate +#' @importFrom reshape2 melt +#' @import ggplot2 +#' @export +#' @return Stacked bar plot of counts of cells by clonotype frequency group + +occupiedscRepertoire <- function(sc, + x.axis = "ident", + label = TRUE, + facet.by = NULL, + proportion = FALSE, + na.include = FALSE, + exportTable = FALSE) { + checkSingleObject(sc) + meta <- grabMeta(sc) + meta <- melt(table(meta[!is.na(meta$Frequency), + c(x.axis, facet.by, "cloneType")], useNA = "ifany")) + if (!na.include) { + meta <- na.omit(meta) + } + meta <- meta[meta$value != 0,] + + if(proportion) { + meta <- meta %>% + group_by(meta[,1]) %>% + mutate(total = sum(value), + prop = value/total) + meta <- as.data.frame(meta) + } + if (exportTable) { + return(meta) + } + col <- length(unique(meta$cloneType)) + if(proportion) { + plot <- ggplot(meta, aes(x = meta[,x.axis], y = prop, fill = cloneType)) + + geom_bar(stat = "identity") + lab <- "Proportion of Cells" + + } else { + plot <- ggplot(meta, aes(x = meta[,x.axis], y = value, fill = cloneType)) + + geom_bar(stat = "identity") + lab <- "Single Cells" + + } + plot <- plot + + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + + scale_fill_manual(values = c(colorblind_vector(col))) + + ylab(lab) + + theme_classic() + + theme(axis.title.x = element_blank()) + if (!is.null(facet.by)) { + plot <- plot + facet_grid(.~meta[,facet.by]) + } + if (label) { + plot <- plot + geom_text(aes(label = value), position = position_stack(vjust = 0.5)) + } + plot +} \ No newline at end of file diff --git a/R/processing.R b/R/processing.R index 4e72aa89..fcfc0e76 100644 --- a/R/processing.R +++ b/R/processing.R @@ -103,11 +103,11 @@ subsetContig <- function(df, name, variables = NULL) { #' rep(c("P", "T"), 3)) #' #' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' screp_example <- combineExpression(combined, screp_example) +#' scRep_example <- get(data("scRep_example")) +#' scRep_example <- combineExpression(combined, scRep_example) #' #' #Using expression2List -#' newList <- expression2List(screp_example, split.by = "seurat_clusters") +#' newList <- expression2List(scRep_example, split.by = "seurat_clusters") #' #' @param sc object after combineExpression(). #' @param split.by The column header to group the new list. NULL will return clusters. @@ -149,11 +149,11 @@ expression2List <- function(sc, split.by) { #' rep(c("P", "T"), 3)) #' #' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' screp_example <- combineExpression(combined, screp_example) +#' scRep_example <- get(data("scRep_example")) +#' scRep_example <- combineExpression(combined, scRep_example) #' #' #Getting data frame output for Circilize -#' circles <- getCirclize(screp_example, group.by = "seurat_clusters") +#' circles <- getCirclize(scRep_example, group.by = "seurat_clusters") #' #' #' @param sc object after combineExpression(). diff --git a/R/quantContig.R b/R/quantContig.R new file mode 100644 index 00000000..43552a9e --- /dev/null +++ b/R/quantContig.R @@ -0,0 +1,120 @@ +#' Quantify the unique clonotypes in the filtered contigs. +#' +#' This function takes the output from combineTCR(), combineBCR(), or +#' expression2List() and quantifies unique clonotypes. The unique clonotypes +#' can be either reported as a raw output or scaled to the total number of +#' clonotypes recovered using the scale parameter. Multiple sequencing +#' runs can be group together using the group parameter. If a matrix output +#' for the data is preferred, set exportTable = TRUE. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' quantContig(combined, cloneCall="strict", scale = TRUE) +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param order Maintain the order of the list when plotting +#' @param scale Converts the graphs into percentage of unique clonotypes. +#' @param exportTable Returns the data frame used for forming the graph +#' @import ggplot2 +#' @export +#' @return ggplot of the total or relative unique clonotypes +quantContig <- function(df, + cloneCall = "strict", + chain = "both", + scale=FALSE, + group.by = NULL, + split.by = NULL, + order = TRUE, + exportTable = FALSE) { + + if (length(group.by) > 1) { + stop("Only one item in the group.by variable can be listed.") + } + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + if (chain != "both") { + for(i in seq_along(df)) { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + } + if (!is.null(group.by)) { + x <- group.by + labs <- group.by + Con.df <- data.frame(matrix(NA, length(df), 4)) + colnames(Con.df) <- c("contigs","values", "total", group.by) + for (i in seq_along(df)) { + Con.df[i,1] <- length(unique(df[[i]][,cloneCall])) + Con.df[i,2] <- names(df)[i] + Con.df[i,3] <- length(df[[i]][,cloneCall]) + location <- which(colnames(df[[i]]) == group.by) + Con.df[i,4] <- df[[i]][1,location] } + col <- length(unique(Con.df[,group.by])) + if (scale) { y <- "scaled" + Con.df$scaled <- Con.df$contigs/Con.df$total*100 + ylab <- "Percent of Unique Clonotype" + + } else { y <- "contigs" + x <- group.by + ylab <- "Unique Clonotypes"} + } else { + x <- "values" + labs <- "Samples" + Con.df <- data.frame(matrix(NA, length(df), 3)) + colnames(Con.df) <- c("contigs","values", "total") + for (i in seq_along(df)) { + Con.df[i,1] <- length(unique(df[[i]][,cloneCall])) + Con.df[i,2] <- names(df)[i] + Con.df[i,3] <- length(df[[i]][,cloneCall]) } + col <- length(unique(Con.df$values)) + if (scale == TRUE) { y <- "scaled" + Con.df$scaled <- Con.df$contigs/Con.df$total*100 + ylab <- "Percent of Unique Clonotype" + } else { y <- "contigs" + ylab <- "Unique Clonotypes" } } + + if (exportTable) { + if (length(df) > 1) { + return(Con.df) + } + + # if a single sample, remove the "values" column if NA + if (is.na(Con.df[[2]])) { + Con.df[[2]] <- NULL + } + return(Con.df) + } + + if(order & is.null(group.by)) { + Con.df[,x] <- factor(Con.df[,x], levels = Con.df[,x]) + } + plot <- ggplot(aes(x=Con.df[,x], y=Con.df[,y], + fill=as.factor(Con.df[,x])), data = Con.df) + + stat_summary(geom = "errorbar", fun.data = mean_se, + position = "dodge", width=.5) + labs(fill = labs) + + stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ + theme_classic() + xlab("Samples") + ylab(ylab) + + scale_fill_manual(values = colorblind_vector(col)) + + # if it is a single run, remove x axis labels if sample name missing + if ((length(df) == 1) && identical(names(df), NA_character_)) { + plot <- plot + + ggplot2::theme( + axis.title.x = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank() + ) + } + return(plot) +} \ No newline at end of file diff --git a/R/scatterClonotypes.R b/R/scatterClonotypes.R new file mode 100644 index 00000000..32618ac5 --- /dev/null +++ b/R/scatterClonotypes.R @@ -0,0 +1,112 @@ +#' Scatter plot comparing the expansion of two samples +#' +#' This function produces a scatter plot directly comparing the specific clonotypes +#' between two samples. The clonotypes will be categorized by counts into singlets or multiplets, +#' either exclusive or shared between the selected samples. Visualization inspired +#' by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' scatterClonotype(combined, x.axis = "PY_P", y.axis = "PY_T", +#' graph = "proportion") +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param x.axis name of the list element to appear on the x.axis +#' @param y.axis name of the list element to appear on the y.axis +#' @param dot.size either total or the name of the list element to +#' use for size of dots +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param graph graph either proportion or raw clonotype count +#' @param exportTable Returns the data frame used for forming the graph. +#' @param seed the integer seed to set for the random variation of point coords. +#' +#' @import ggplot2 +#' +#' @export +#' @return ggplot of the relative clonotype numbers + +scatterClonotype <- function( + df, cloneCall ="strict", + x.axis = NULL, y.axis = NULL, + chain = "both", + dot.size = "total", + split.by = NULL, + graph = "proportion", + exportTable = FALSE, + seed = NULL +) { + if (!is.null(seed)) {set.seed(seed)} + + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) + if (chain != "both") { + for (i in axes) { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + } + x.df <- as.data.frame(table(df[[x.axis]][,cloneCall])) + colnames(x.df)[2] <- x.axis + y.df <- as.data.frame(table(df[[y.axis]][,cloneCall])) + colnames(y.df)[2] <- y.axis + combined.df <- merge(x.df, y.df, by = "Var1", all = TRUE) + if (dot.size != "total") { + if (dot.size %!in% colnames(combined.df)) { + size.df <- as.data.frame(table(df[[dot.size]][,cloneCall])) + colnames(size.df)[2] <- dot.size + combined.df <- merge(combined.df, size.df, by = "Var1", all = TRUE) } + combined.df[is.na(combined.df)] <- 0 + combined.df[,paste0("size", ".fraction")] <- combined.df[,dot.size]/sum(combined.df[,dot.size]) + labeling <- unique(c(x.axis, y.axis, dot.size)) + } else { + combined.df[is.na(combined.df)] <- 0 + labeling <- unique(c(x.axis, y.axis)) } + combined.df[,"class"] <- NA + combined.df[,"sum"] <- rowSums(combined.df[,labeling]) + for (i in seq_along(labeling)) { + if (length(labeling) > 2) { + combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] == 1 & rowSums(combined.df[,labeling[which(labeling != labeling[i])]]) == 0, + paste0(labeling[i], ".singlet"), combined.df[,"class"]) + combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] > 1 & rowSums(combined.df[,labeling[which(labeling != labeling[i])]]) == 0, + paste0(labeling[i], ".multiplet"), combined.df[,"class"]) + } else if (length(labeling) == 2) { + combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] == 1 & combined.df[,labeling[which(labeling != labeling[i])]] == 0, + paste0(labeling[i], ".singlet"), combined.df[,"class"]) + combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] > 1 & combined.df[,labeling[which(labeling != labeling[i])]] == 0, + paste0(labeling[i], ".multiplet"), combined.df[,"class"]) + }} + combined.df[,"class"] <- ifelse(combined.df[,y.axis] >= 1 & combined.df[,x.axis] >= 1, paste0("dual.expanded"), combined.df[,"class"]) + combined.df[,paste0(x.axis, ".fraction")] <- combined.df[,x.axis]/sum(combined.df[,x.axis]) + combined.df[,paste0(y.axis, ".fraction")] <- combined.df[,y.axis]/sum(combined.df[,y.axis]) + if (graph == "proportion") { + x <- combined.df[,paste0(x.axis, ".fraction")] + y <- combined.df[,paste0(y.axis, ".fraction")] + } else if (graph == "count") { + x <- combined.df[,x.axis] + y <- combined.df[,y.axis] } + if (dot.size != "total") { + size <- combined.df[,dot.size] + } else { size <- combined.df[,"sum"] } + if (exportTable == TRUE) { return(combined.df) } + plot <- ggplot(combined.df, aes(x=x, y = y, color = class)) + + theme_classic() + + scale_color_manual(values = colorblind_vector(length(unique(combined.df$class)))) + + xlab(x.axis) + ylab(y.axis) + labs(size = "Total n") + if (graph == "proportion") { + plot <- plot + geom_abline(slope = 1, intercept = 0, alpha = 0.4, lty=2) + + scale_y_sqrt() + scale_x_sqrt() + } else if (graph == "count") { + plot <- plot + ylim(0, max(x,y)) + xlim(0, max(x,y)) + } + plot <- plot + geom_jitter(aes(size = size)) + return(plot) +} \ No newline at end of file diff --git a/R/seuratFunctions.R b/R/seuratFunctions.R deleted file mode 100644 index aa16385e..00000000 --- a/R/seuratFunctions.R +++ /dev/null @@ -1,553 +0,0 @@ -#' Adding clonotype information to a Seurat or SCE object -#' -#' This function adds the immune receptor information to the Seurat or -#' SCE object to the meta data. By default this function also calculates -#' the frequencies of the clonotypes by sequencing run (group.by = "none"). -#' To change how the frequencies are calculated, select a column header for -#' the group.by variable. Importantly, before using combineExpression() -#' ensure the barcodes of the seurat or SCE object match the barcodes in the -#' output of the combinedContig() call. Check changeNames() to change the -#' prefix of the Seurat object. If combining more than one immune receptor -#' type, barcodes with both receptors will be removed during the combination -#' process. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' -#' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) -#' sce <- Seurat::as.SingleCellExperiment(sce) -#' -#' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) -#' -#' @param df The product of CombineTCR() or CombineBCR() or a list of -#' both c(CombineTCR(), combineBCR()) -#' @param sc The seurat or SingleCellExperiment (SCE) object to attach -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt) CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column label in the combined contig object in which -#' clonotype frequency will be calculated. "none" will keep the list as is, -#' while NULL will merge all the contigs into a single data frame. -#' @param proportion Whether to use the total frequency (FALSE) or the -#' proportion (TRUE) of the clonotype based on the group.by variable. -#' @param cloneTypes The bins for the grouping based on frequency -#' @param filterNA Method to subset seurat object of barcodes without -#' clonotype information -#' @param addLabel This will add a label to the frequency header, allowing -#' the user to try multiple group.by variables or recalculate frequencies after -#' subsetting the data. -#' @importFrom dplyr bind_rows %>% summarise -#' @importFrom rlang %||% -#' @importFrom SummarizedExperiment colData<- colData -#' @export -#' @return seurat or SingleCellExperiment object with attached clonotype -#' information -#' -combineExpression <- function( - df, - sc, - cloneCall ="strict", - chain = "both", - group.by ="none", - proportion = TRUE, - filterNA = FALSE, - cloneTypes = c( - Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1 - ), - addLabel = FALSE -) { - call_time <- Sys.time() - - options( dplyr.summarise.inform = FALSE ) - cloneTypes <- c(None = 0, cloneTypes) - df <- checkList(df) - cloneCall <- theCall(cloneCall) - Con.df <- NULL - meta <- grabMeta(sc) - cell.names <- rownames(meta) - if (group.by == "none" | !is.null(group.by)) { - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - data <- data.frame(df[[i]], stringsAsFactors = FALSE) - data2 <- unique(data[,c("barcode", cloneCall)]) - data2 <- na.omit(data2[data2[,"barcode"] %in% cell.names,]) - if (proportion) { - data2 <- data2 %>% group_by(data2[,cloneCall]) %>% - summarise(Frequency = n()/nrow(data2)) - } else { - data2 <- data2 %>% group_by(data2[,cloneCall]) %>% - summarise(Frequency = n()) - } - colnames(data2)[1] <- cloneCall - data <- merge(data, data2, by = cloneCall, all = TRUE) - data <- data[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency")] - Con.df <- rbind.data.frame(Con.df, data) - } - } else if (group.by != "none" | is.null(group.by)) { - data <- data.frame(bind_rows(df), stringsAsFactors = FALSE) - data2 <- na.omit(unique(data[,c("barcode", cloneCall, group.by)])) - data2 <- data2[data2[,"barcode"] %in% cell.names, ] - data2 <- as.data.frame(data2 %>% group_by(data2[,cloneCall], - data2[,group.by]) %>% summarise(Frequency = n())) - if(!is.null(group.by)) { - colnames(data2)[c(1,2)] <- c(cloneCall, group.by) - x <- unique(data[,group.by]) - for (i in seq_along(x)) { - sub1 <- subset(data, data[,group.by] == x[i]) - sub2 <- subset(data2, data2[,group.by] == x[i]) - merge <- merge(sub1, sub2, by=cloneCall) - if (proportion) { - merge$Frequency <- merge$Frequency/length(merge$Frequency) - } - Con.df <- rbind.data.frame(Con.df, merge) - } - nsize <- Con.df %>% group_by(Con.df[,paste0(group.by, ".x")]) %>% summarise(n = n()) - } else { - if (proportion) { - data <- data %>% - group_by(data[,cloneCall]) %>% - mutate(Frequency = n()/nrow(data)) - } else { - data <- data %>% - group_by(data[,cloneCall]) %>% - mutate(Frequency = n()) - } - Con.df <- data[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency")] - nsize <- length(Con.df) - } - - } - - Con.df$cloneType <- NA - for (x in seq_along(cloneTypes)) { names(cloneTypes)[x] <- - paste0(names(cloneTypes[x]), ' (', cloneTypes[x-1], - ' < X <= ', cloneTypes[x], ')') } - for (i in 2:length(cloneTypes)) { Con.df$cloneType <- - ifelse(Con.df$Frequency > cloneTypes[i-1] & Con.df$Frequency - <= cloneTypes[i], names(cloneTypes[i]), Con.df$cloneType) } - PreMeta <- unique(Con.df[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency", "cloneType")]) - dup <- PreMeta$barcode[which(duplicated(PreMeta$barcode))] - PreMeta <- PreMeta[PreMeta$barcode %!in% dup,] - rownames(PreMeta) <- PreMeta$barcode - if (group.by != "none" && addLabel) { - location <- which(colnames(PreMeta) == "Frequency") - colnames(PreMeta)[location] <- paste0("Frequency.", group.by) - } - - warn_str <- "< 1% of barcodes match: Ensure the barcodes in - the Seurat object match the barcodes in the combined immune receptor - list from scRepertoire - most common issue is the addition of the - prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() - functions" - - if (is_seurat_object(sc)) { - if (length(which(rownames(PreMeta) %in% - rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { - warning(warn_str) - } - col.name <- names(PreMeta) %||% colnames(PreMeta) - sc[[col.name]] <- PreMeta - } else { - rownames <- rownames(colData(sc)) - if (length(which(rownames(PreMeta) %in% - rownames))/length(rownames) < 0.01) { - warning(warn_str) } - colData(sc) <- cbind(colData(sc), PreMeta[rownames,])[, union(colnames(colData(sc)), colnames(PreMeta))] - rownames(colData(sc)) <- rownames - } - if (filterNA) { sc <- filteringNA(sc) } - sc$cloneType <- factor(sc$cloneType, levels = rev(names(cloneTypes))) - - if(is_seurat_object(sc)) { - sc@commands[["combineExpression"]] <- make_screp_seurat_cmd( - call_time, sc@active.assay - ) - } - sc -} # Qile: I think the barcode column added to the metadata is redundant? Since it matches the row names? - -#' Highlighting specific clonotypes in Seurat -#' -#' Use a specific clonotype sequence to highlight on top of the dimensional -#' reduction in seurat object. -#' -#' @examples -#' #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' -#' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' -#' #Using combineExpresion() -#' screp_example <- combineExpression(combined, screp_example ) -#' -#' #Using highlightClonotype() -#' screp_example <- highlightClonotypes(screp_example, cloneCall= "aa", -#' sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF")) -#' -#' @param sc The Seurat object to attach -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param sequence The specific sequence or sequence to highlight -#' -#' @export -#' @return Seurat object with new meta data column for indicated clones -highlightClonotypes <- function(sc, - cloneCall = c("gene", "nt", "aa", "strict"), - sequence = NULL){ - if (!inherits(x=sc, what ="Seurat")) { - stop("Object indicated is not of class 'Seurat', make sure you - are using the correct data.") } - cloneCall <- theCall(cloneCall) - meta <- grabMeta(sc) - meta$highlight <- NA - for(i in seq_along(sequence)) { - meta$highlight <- ifelse(meta[,cloneCall] == sequence[i], - sequence[i], meta$highlight) - } - meta <- meta[,-(which(colnames(meta) == "ident"))] - col.name <- names(meta) %||% colnames(meta) - sc[[col.name]] <- meta - sc -} - -#' Exploring interaction of clonotypes by seurat or SCE dynamics -#' -#' View the proportional contribution of clonotypes by seurat or SCE object -#' meta data after combineExpression(). The visualization is based on the -#' ggalluvial package, which requires the aesthetics to be part of the axes -#' that are visualized. Therefore, alpha, facet, and color should be part of -#' the the axes you wish to view or will add an additional stratum/column to -#' the end of the graph. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' -#' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) -#' sce <- Seurat::as.SingleCellExperiment(sce) -#' -#' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) -#' -#' #Using alluvialClonotypes() -#' alluvialClonotypes(sce, cloneCall = "gene", -#' y.axes = c("Patient", "ident"), color = "ident") -#' -#' @param sc The seurat or SCE object to visualize after combineExpression(). -#' For SCE objects, the cluster variable must be in the meta data under -#' "cluster". -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt) or CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param y.axes The columns that will separate the proportional -#' visualizations. -#' @param color The column header or clonotype(s) to be highlighted. -#' @param facet The column label to separate. -#' @param alpha The column header to have gradated opacity. -#' @import ggplot2 -#' -#' @importFrom ggalluvial StatStratum geom_flow geom_stratum to_lodes_form geom_alluvium -#' @importFrom dplyr %>% mutate -#' -#' @export -#' @return Alluvial ggplot comparing clonotype distribution across -#' selected parameters. -alluvialClonotypes <- function(sc, - cloneCall = c("gene", "nt", "aa", "strict"), - chain = "both", - y.axes = NULL, - color = NULL, - alpha = NULL, - facet = NULL) { - checkSingleObject(sc) - cloneCall <- theCall(cloneCall) - if (length(y.axes) == 0) { - stop("Make sure you have selected the variable(s) to visualize") } - meta <- grabMeta(sc) - if (chain != "both") { - meta <- off.the.chain(meta, chain, cloneCall) - } - meta$barcodes <- rownames(meta) - check <- colnames(meta) == color - if (length(unique(check)) == 1 & unique(check)[1] == FALSE & - !is.null(color)) { - meta <- meta %>% mutate("clonotype(s)" = ifelse(meta[,cloneCall] %in% - color, "Selected", "Other")) - color <- "clonotype(s)" } - - y.axes <- unique(c(y.axes, color, alpha, facet)) - set.axes <- seq_along(y.axes) - meta2 <- meta[,c(y.axes, color, alpha, facet, cloneCall, "barcodes")] - meta2 <- unique(na.omit(meta2[!duplicated(as.list(meta2))])) - - lodes <- makingLodes(meta2, color, alpha, facet, set.axes) - plot <- ggplot(data = lodes, aes(x = x, stratum = stratum, - alluvium = alluvium, label = stratum)) + - geom_stratum() + theme_classic() + - theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) - if (is.null(color) & is.null(alpha)) { - plot <- plot + geom_alluvium() - } else if (!is.null(color) & is.null(alpha)) { - plot <- plot+geom_flow(aes(fill = lodes[,color]), - stat = "alluvium", lode.guidance = "forward") + labs(fill = color) - } else if (is.null(color) & !is.null(alpha)) { - plot <- plot + geom_flow(aes(alpha = lodes[,alpha]), stat = "alluvium", - lode.guidance = "forward") + labs(alpha = alpha) - }else { - plot <- plot+geom_flow(aes(alpha=lodes[,alpha], fill=lodes[,color]), - stat = "alluvium", lode.guidance = "forward") + - labs(fill = color, alpha = alpha) } - if (length(facet) == 1 & length(facet) < 2) { - plot <- plot + facet_wrap(.~lodes[,facet], scales="free_y") - } else if (length(facet) == 0) { plot <- plot } - plot <- plot + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) - return(plot) -} - - -#' Visualize the number of single cells with clonotype frequencies by cluster -#' -#' View the count of clonotypes frequency group in seurat or SCE object -#' meta data after combineExpression(). The visualization will take the -#' new meta data variable "cloneType" and plot the number of cells with -#' each designation using a secondary variable, like cluster. Credit to -#' the idea goes to Drs. Carmona and Andreatta and their work with -#' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' -#' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) -#' -#' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) -#' -#' #Using occupiedscRepertoire() -#' occupiedscRepertoire(sce, x.axis = "ident") -#' table <- occupiedscRepertoire(sce, x.axis = "ident", exportTable = TRUE) -#' -#' @param sc The Seurat or SCE object to visualize after combineExpression(). -#' @param x.axis The variable in the meta data to graph along the x.axis -#' @param label Include the number of clonotype in each category by x.axis variable -#' @param facet.by The column header used for faceting the graph -#' @param proportion Convert the stacked bars into relative proportion -#' @param na.include Visualize NA values or not. -#' @param exportTable Exports a table of the data into the global -#' environment in addition to the visualization -#' @importFrom dplyr %>% group_by mutate -#' @importFrom reshape2 melt -#' @import ggplot2 -#' @export -#' @return Stacked bar plot of counts of cells by clonotype frequency group - -occupiedscRepertoire <- function(sc, - x.axis = "ident", - label = TRUE, - facet.by = NULL, - proportion = FALSE, - na.include = FALSE, - exportTable = FALSE) { - checkSingleObject(sc) - meta <- grabMeta(sc) - meta <- melt(table(meta[!is.na(meta$Frequency), - c(x.axis, facet.by, "cloneType")], useNA = "ifany")) - if (!na.include) { - meta <- na.omit(meta) - } - meta <- meta[meta$value != 0,] - - if(proportion) { - meta <- meta %>% - group_by(meta[,1]) %>% - mutate(total = sum(value), - prop = value/total) - meta <- as.data.frame(meta) - } - if (exportTable) { - return(meta) - } - col <- length(unique(meta$cloneType)) - if(proportion) { - plot <- ggplot(meta, aes(x = meta[,x.axis], y = prop, fill = cloneType)) + - geom_bar(stat = "identity") - lab <- "Proportion of Cells" - - } else { - plot <- ggplot(meta, aes(x = meta[,x.axis], y = value, fill = cloneType)) + - geom_bar(stat = "identity") - lab <- "Single Cells" - - } - plot <- plot + - theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + - scale_fill_manual(values = c(colorblind_vector(col))) + - ylab(lab) + - theme_classic() + - theme(axis.title.x = element_blank()) - if (!is.null(facet.by)) { - plot <- plot + facet_grid(.~meta[,facet.by]) - } - if (label) { - plot <- plot + geom_text(aes(label = value), position = position_stack(vjust = 0.5)) - } - plot -} - -#' Visualize distribution of clonal frequency overlaid on dimensional reduction plots -#' -#' This function allows the user to visualize the clonal expansion by overlaying the -#' cells with specific clonal frequency onto the dimensional reduction plots in Seurat. -#' Credit to the idea goes to Drs Andreatta and Carmona and their work with -#' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' -#' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) -#' -#' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) -#' -#' #Using clonalOverlay() -#' clonalOverlay(sce, reduction = "umap", freq.cutpoint = 0.3, bins = 5) -#' -#' @param sc The seurat or SCE object to visualize after combineExpression(). -#' @param reduction The dimensional reduction to visualize -#' @param freq.cutpoint The overlay cutpoint to include, this corresponds to the -#' Frequency variable in the single-cell objecter -#' @param bins The number of contours to the overlay -#' @param facet meta data variable to facet the comparison -#' -#' @import ggplot2 -#' @importFrom SeuratObject Embeddings -#' @export -#' @author Francesco Mazziotta, Nick Borcherding -#' -#' @return ggplot object - -clonalOverlay <- function(sc, - reduction = NULL, - freq.cutpoint = 30, - bins = 25, - facet = NULL) { - checkSingleObject(sc) - tmp <- data.frame(grabMeta(sc), identity = sc@active.ident, get.coord(sc, reduction)) - if (!is.null(facet)) { - facet <- tmp[,facet] - tmp <- data.frame(facet, tmp) - } - tmp$include <- ifelse(tmp$Frequency >= freq.cutpoint, "Yes", NA) - tmp2 <- subset(tmp, include == "Yes") - plot <- ggplot(tmp2, mapping = aes(x=tmp2[,(ncol(tmp2)-2)], y = tmp2[,(ncol(tmp2)-1)])) + - geom_point(tmp, mapping = aes(x=as.numeric(tmp[,(ncol(tmp)-2)]), y = as.numeric(tmp[,(ncol(tmp)-1)]), color = tmp[,"identity"]), size= 0.5) + - geom_density_2d(color = "black", lwd=0.25, bins = bins) + - theme_classic() + - labs(color = "Active Identity") + - xlab("Dimension 1") + ylab("Dimension 2") - if (!is.null(facet)) { - plot <- plot + facet_wrap(~facet) - } - plot -} - -#' Generate a contig list from a multiplexed experiment -#' -#' Multiplexing single-cell sequencing runs is an efficient method for quantifying -#' multiple samples or conditions simultaneously. Unfortunately, the hashing information -#' is not stored in the TCR sequence data. In order preprocess and form a contig list -#' for downstream analysis in scRepertoire, createHTOContigList() take the filtered contig -#' annotation output and the single-cell RNA object to create the list. If using an -#' integrated single-cell object, it is recommended to split the object by sequencing run -#' and remove extra prefixes and suffixes on the barcode before using createHTOContigList(). -#' Alternatively, the variable multi.run can be used to separate a list of contigs by a meta data -#' variable. This may have issues with the repeated barcodes. -#' -#' @examples -#' \dontrun{ -#' filtered.contig <- read.csv(".../Sample/outs/filtered_contig_annotations.csv") -#' contig.list <- createHTOContigList(contig = filtered.contig, -#' sc = Seurat.Obj, group.by = "HTO_maxID") -#' } -#' -#' @param contig The filtered contig annotation file from multiplexed experiment -#' @param sc The Seurat or SCE object. -#' @param group.by One or more meta data headers to create the contig list based on. -#' If more than one header listed, the function combines them into a single variable. -#' @param multi.run If using integrated single-cell object, the meta data variable -#' that indicates the sequencing run. -#' @export -#' @return Returns a list of contigs corresponding to the multiplexed Seurat -#' or Single-Cell Experiment object - - -createHTOContigList <- function(contig, - sc, - group.by = NULL, - multi.run = NULL){ - contig.list <- NULL - checkSingleObject(sc) - meta <- grabMeta(sc) - if (length(group.by) > 1) { - meta["group.by"] <- apply(meta[ , group.by] , 1 , paste , collapse = "." ) - } else { - meta[,"group.by"] <- meta[ , group.by] - } - unique.groups <- unique(meta$group.by) - - if (!is.null(multi.run) & inherits(x=contig, what ="list")) { - tmp.list <- list() - runs <- unique(meta[,multi.run]) - for (x in seq_along(contig)) { - cont.tmp <- contig[[x]] - cont.tmp$barcode <- paste0(cont.tmp$barcode, "_", x) - meta.tmp <- meta[meta[,multi.run] == runs[x],] #Subset meta in batch - rownames(meta.tmp) <- substring(rownames(meta.tmp), nchar(rownames(meta.tmp)[1])-17, nchar(rownames(meta.tmp)[1])) - cont.tmp <- cont.tmp[cont.tmp$barcode %in% row.names(meta.tmp) ] #subset contig in new meta - for (y in seq_along(unique.groups)) { - sub.con <- cont.tmp[cont.tmp$barcode %in% rownames(subset(meta.tmp, group.by == unique.groups[y])),] - tmp.list[[y]] <- sub.con - } - if(x == 1) { - contig.list <- tmp.list - } else { - contig.list <- c(contig.list, tmp.list) - } - } - } else { - cont.tmp <- contig[contig$barcode %in% rownames(meta), ] - for (i in seq_along(unique.groups)) { - sub.con <- cont.tmp[cont.tmp$barcode %in% rownames(subset(meta, group.by == unique.groups[i])),] - contig.list[[i]] <- sub.con - } - names(contig.list) <- unique.groups - } - contig.list -} diff --git a/R/startrac.R b/R/startrac.R index 03acf3df..b302d04b 100644 --- a/R/startrac.R +++ b/R/startrac.R @@ -17,11 +17,11 @@ #' rep(c("P", "T"), 3)) #' #' #Getting a sample of a Seurat object -#' screp_example <- get(data("screp_example")) -#' screp_example <- combineExpression(combined, screp_example) +#' scRep_example <- get(data("scRep_example ")) +#' scRep_example <- combineExpression(combined, scRep_example ) #' #' #Using occupiedscRepertoire() -#' StartracDiversity(screp_example, type = "Type", sample = "Patient", by = "overall") +#' StartracDiversity(scRep_example , type = "Type", sample = "Patient", by = "overall") #' } #' #' @param sc The seurat or SCE object to visualize after combineExpression(). diff --git a/R/utils.R b/R/utils.R index d5056256..0983f7cf 100644 --- a/R/utils.R +++ b/R/utils.R @@ -20,6 +20,12 @@ off.the.chain <- function(dat, chain, cloneCall) { return(dat) } +#This is the basic color palette for the package +colorblind_vector <- colorRampPalette(rev(c("#0D0887FF", "#47039FFF", "#7301A8FF", "#9C179EFF", + "#BD3786FF", "#D8576BFF","#ED7953FF","#FA9E3BFF", "#FDC926FF", "#F0F921FF"))) + + + #Remove list elements that contain all NA values checkBlanks <- function(df, cloneCall) { diff --git a/R/viz.R b/R/viz.R deleted file mode 100644 index 25024cec..00000000 --- a/R/viz.R +++ /dev/null @@ -1,775 +0,0 @@ -#' Quantify the unique clonotypes in the filtered contigs. -#' -#' This function takes the output from combineTCR(), combineBCR(), or -#' expression2List() and quantifies unique clonotypes. The unique clonotypes -#' can be either reported as a raw output or scaled to the total number of -#' clonotypes recovered using the scale parameter. Multiple sequencing -#' runs can be group together using the group parameter. If a matrix output -#' for the data is preferred, set exportTable = TRUE. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' quantContig(combined, cloneCall="strict", scale = TRUE) -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param order Maintain the order of the list when plotting -#' @param scale Converts the graphs into percentage of unique clonotypes. -#' @param exportTable Returns the data frame used for forming the graph -#' @import ggplot2 -#' @export -#' @return ggplot of the total or relative unique clonotypes -quantContig <- function(df, - cloneCall = "strict", - chain = "both", - scale=FALSE, - group.by = NULL, - split.by = NULL, - order = TRUE, - exportTable = FALSE) { - - if (length(group.by) > 1) { - stop("Only one item in the group.by variable can be listed.") - } - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - if (chain != "both") { - for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } - if (!is.null(group.by)) { - x <- group.by - labs <- group.by - Con.df <- data.frame(matrix(NA, length(df), 4)) - colnames(Con.df) <- c("contigs","values", "total", group.by) - for (i in seq_along(df)) { - Con.df[i,1] <- length(unique(df[[i]][,cloneCall])) - Con.df[i,2] <- names(df)[i] - Con.df[i,3] <- length(df[[i]][,cloneCall]) - location <- which(colnames(df[[i]]) == group.by) - Con.df[i,4] <- df[[i]][1,location] } - col <- length(unique(Con.df[,group.by])) - if (scale) { y <- "scaled" - Con.df$scaled <- Con.df$contigs/Con.df$total*100 - ylab <- "Percent of Unique Clonotype" - - } else { y <- "contigs" - x <- group.by - ylab <- "Unique Clonotypes"} - } else { - x <- "values" - labs <- "Samples" - Con.df <- data.frame(matrix(NA, length(df), 3)) - colnames(Con.df) <- c("contigs","values", "total") - for (i in seq_along(df)) { - Con.df[i,1] <- length(unique(df[[i]][,cloneCall])) - Con.df[i,2] <- names(df)[i] - Con.df[i,3] <- length(df[[i]][,cloneCall]) } - col <- length(unique(Con.df$values)) - if (scale == TRUE) { y <- "scaled" - Con.df$scaled <- Con.df$contigs/Con.df$total*100 - ylab <- "Percent of Unique Clonotype" - } else { y <- "contigs" - ylab <- "Unique Clonotypes" } } - - if (exportTable) { - if (length(df) > 1) { - return(Con.df) - } - - # if a single sample, remove the "values" column if NA - if (is.na(Con.df[[2]])) { - Con.df[[2]] <- NULL - } - return(Con.df) - } - - if(order & is.null(group.by)) { - Con.df[,x] <- factor(Con.df[,x], levels = Con.df[,x]) - } - plot <- ggplot(aes(x=Con.df[,x], y=Con.df[,y], - fill=as.factor(Con.df[,x])), data = Con.df) + - stat_summary(geom = "errorbar", fun.data = mean_se, - position = "dodge", width=.5) + labs(fill = labs) + - stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ - theme_classic() + xlab("Samples") + ylab(ylab) + - scale_fill_manual(values = colorblind_vector(col)) - - # if it is a single run, remove x axis labels if sample name missing - if ((length(df) == 1) && identical(names(df), NA_character_)) { - plot <- plot + - ggplot2::theme( - axis.title.x = element_blank(), - axis.text.x = element_blank(), - axis.ticks.x = element_blank() - ) - } - return(plot) -} - -#' Demonstrate the relative abundance of clonotypes by group or sample. -#' -#' This function takes the output of combineTCR(), combineBCR(), or -#' expression2List() and displays the number of clonotypes at specific -#' frequencies by sample or group. Visualization can either be a line -#' graph using calculated numbers or if scale = TRUE, the output will -#' be a density plot. Multiple sequencing runs can be group together -#' using the group parameter. If a matrix output for the data is -#' preferred, set exportTable = TRUE. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' abundanceContig(combined, cloneCall = "gene", scale = FALSE) -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column header for which you would like to analyze the data. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param order Maintain the order of the list when plotting -#' @param scale Converts the graphs into density plots in order to show -#' relative distributions. -#' @param exportTable Returns the data frame used for forming the graph -#' to the visualization. -#' @importFrom ggplot2 ggplot -#' @export -#' @return ggplot of the total or relative abundance of clonotypes -#' across quanta -abundanceContig <- function(df, - cloneCall = "strict", - chain = "both", - scale=FALSE, - group.by = NULL, - split.by = NULL, - order = TRUE, - exportTable = FALSE) { - df <- list.input.return(df,split.by) - Con.df <- NULL - xlab <- "Abundance" - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - names <- names(df) - if (!is.null(group.by)) { - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - data1 <- parseContigs(df, i, names, cloneCall) - label <- df[[i]][1,group.by] - data1[,paste(group.by)] <- label - Con.df<- rbind.data.frame(Con.df, data1) } - Con.df <- data.frame(Con.df) - col <- length(unique(Con.df[,group.by])) - fill <- group.by - if (scale == TRUE) { ylab <- "Density of Clonotypes" - plot <- ggplot(Con.df, aes(x=Abundance, fill=Con.df[,group.by])) + - geom_density(aes(y=after_stat(scaled)), alpha=0.5, - lwd=0.25, color="black", bw=0.5) + - scale_fill_manual(values = colorblind_vector(col)) + - labs(fill = fill) - } else { ylab <- "Number of Clonotypes" - plot <- ggplot(Con.df, aes(x=Abundance, group.by = values, - color = Con.df[,group.by])) + - geom_line(stat="count") + - scale_color_manual(values = colorblind_vector(col)) + - labs(color = fill)} - } else { - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - data1 <- parseContigs(df, i, names, cloneCall) - Con.df<- rbind.data.frame(Con.df, data1) } - Con.df <- data.frame(Con.df) - if(order) { - Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) - } - col <- length(unique(Con.df$values)) - fill <- "Samples" - if (scale == TRUE) { ylab <- "Density of Clonotypes" - plot <- ggplot(Con.df, aes(Abundance, fill=values)) + - geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, - color="black", bw=0.5) + - scale_fill_manual(values = colorblind_vector(col)) + - labs(fill = fill) - } else { ylab <- "Number of Clonotypes" - plot <- ggplot(Con.df, aes(x=Abundance, group = values, - color = values)) + - geom_line(stat="count") + - scale_color_manual(values = colorblind_vector(col)) + - labs(color = fill)} } - if (exportTable == TRUE) { return(Con.df) } - plot <- plot + scale_x_log10() + ylab(ylab) + xlab(xlab) + - theme_classic() -return(plot) -} - -#' Demonstrate the distribution of lengths filtered contigs. -#' -#' This function takes the output of combineTCR(), combineBCR(), or -#' expression2List() and displays either the nucleotide (nt) or amino -#' acid (aa) sequence length. The sequence length visualized can be -#' selected using the chains parameter, either the combined clonotype -#' (both chains) or across all single chains. Visualization can either -#' be a histogram or if scale = TRUE, the output will be a density plot. -#' Multiple sequencing runs can be group together using the -#' group parameter. If a matrix output for the data is preferred, set -#' exportTable = TRUE. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' lengthContig(combined, cloneCall="aa", chain = "both") -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), -#' CDR3 amino acid (aa). -#' @param group.by The group header for which you would like to analyze -#' the data. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param scale Converts the graphs into density plots in order to show -#' relative distributions. -#' @param order Maintain the order of the list when plotting -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param exportTable Returns the data frame used for forming the graph. -#' @importFrom stringr str_split -#' @importFrom ggplot2 ggplot -#' @export -#' @return ggplot of the discrete or relative length distributions of -#' clonotype sequences -lengthContig <- function(df, - cloneCall = "aa", - chain = "both", - group.by = NULL, - split.by = NULL, - order = TRUE, - scale = FALSE, - exportTable = FALSE) { - df <- list.input.return(df, split.by) - if(cloneCall == "nt") { cloneCall <- "CTnt" - ylab <- "CDR3 (NT)" - } else if (cloneCall == "aa") { cloneCall <- "CTaa" - ylab <- "CDR3 (AA)" - } else { stop("Please make a selection of the type of - CDR3 sequence to analyze by using `cloneCall`") } - AB <- grep("TRA", df[[1]]$TCR1) - IG <- grep("IGH", df[[1]]$TCR1) - if (length(AB) > 1 && length(IG) == 0) { - c1 <- "TRA" - c2 <- "TRB" - } else if (length(AB) > 1) { - c1 <- "IGH" - c2 <- "IGL" - } else { - c1 <- "TRG" - c2 <- "TRD" - } - xlab <- "Length" - Con.df <- NULL - Con.df <- lengthDF(df, cloneCall, chain, group.by, c1, c2) - if(is.null(group.by) & order) { - Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) - } - names <- names(df) - if (!is.null(group.by)) { - fill <- group.by - col <- length(unique(Con.df[,group.by])) - if (scale == TRUE) { yplus <- "Percent of " - plot <- ggplot(Con.df, aes(fill=Con.df[,group.by], - length,(after_stat(count))/sum(after_stat(count))*100)) + - geom_density(aes(y=after_stat(scaled)),alpha=.5,lwd=.25,color="black") - } else { yplus <- "Number of " - plot<-ggplot(Con.df,aes(as.factor(length),fill=Con.df[,group.by]))+ - geom_bar(position = position_dodge2(preserve = "single"), - color="black", lwd=0.25, width=0.9) + - scale_x_discrete(breaks = round(seq(min(Con.df$length), - max(Con.df$length), by = 5),10)) } - } else if (is.null(group.by)){ - fill <- "Samples" - col <- length(unique(Con.df$values)) - if (scale == TRUE) { yplus <- "Percent of " - plot <- ggplot(Con.df, aes(length, (after_stat(count))/sum(after_stat(count))*100, - fill=values)) + geom_density(aes(y=after_stat(scaled)), alpha=0.5, - lwd=0.25, color="black") - } else { yplus <- "Number of " - plot <- ggplot(Con.df, aes(as.factor(length), fill=values)) + - geom_bar(position = position_dodge2(preserve = "single"), - color="black", lwd=0.25) + - scale_x_discrete(breaks = round(seq(min(Con.df$length), - max(Con.df$length), by = 5),10))} } - plot <- plot + scale_fill_manual(values = colorblind_vector(col)) + - labs(fill = fill) + ylab(paste(yplus, ylab, sep="")) + - xlab(xlab) + theme_classic() - if (exportTable == TRUE) { return(Con.df) } - return(plot)} - -#' Demonstrate the difference in clonal proportion between clonotypes -#' -#' This function produces an alluvial or area graph of the proportion of -#' the indicated clonotypes for all or selected samples. Clonotypes can be -#' selected using the clonotypes parameter with the specific sequence of -#' interest or using the number parameter with the top n clonotypes by -#' proportion to be visualized. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' compareClonotypes(combined, numbers = 10, -#' samples = c("PX_P", "PX_T"), cloneCall="aa") -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param samples The specific samples to isolate for visualization. -#' @param clonotypes The specific sequences of interest. -#' @param numbers The top number clonotype sequences per group -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param graph The type of graph produced, either "alluvial" or "area". -#' @param exportTable Returns the data frame used for forming the graph. -#' @import ggplot2 -#' -#' @export -#' @return ggplot of the proportion of total sequencing read of -#' selecting clonotypes -compareClonotypes <- function(df, - cloneCall = "strict", - chain = "both", - samples = NULL, - clonotypes = NULL, - numbers = NULL, - split.by = NULL, - graph = "alluvial", - exportTable = FALSE){ - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - if (!is.null(numbers) & !is.null(clonotypes)) { - stop("Make sure your inputs are either numbers or clonotype sequences.") - } - Con.df <- NULL - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - tbl <- as.data.frame(table(df[[i]][,cloneCall])) - tbl[,2] <- tbl[,2]/sum(tbl[,2]) - colnames(tbl) <- c("Clonotypes", "Proportion") - tbl$Sample <- names(df[i]) - Con.df <- rbind.data.frame(Con.df, tbl) - } - if (!is.null(samples)) { - Con.df <- Con.df[Con.df$Sample %in% samples,] } - if (!is.null(clonotypes)) { - Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] } - if (!is.null(numbers)) { - top <- Con.df %>% - group_by(Con.df[,3]) %>% - slice_max(n = numbers, order_by = Proportion, with_ties = FALSE) - Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] } - if (nrow(Con.df) < length(unique(Con.df$Sample))) { - stop("Reasses the filtering strategies here, there is not - enough clonotypes to examine.") } - if (exportTable == TRUE) { return(Con.df)} - - plot <- ggplot(Con.df, aes(x = Sample, fill = Clonotypes, group = Clonotypes, - stratum = Clonotypes, alluvium = Clonotypes, - y = Proportion, label = Clonotypes)) + - theme_classic() + - theme(axis.title.x = element_blank()) - if (graph == "alluvial") { - plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") - } else if (graph == "area") { - plot <- plot + - geom_area(aes(group = Clonotypes), color = "black") } - return(plot) -} - -#' Scatter plot comparing the expansion of two samples -#' -#' This function produces a scatter plot directly comparing the specific clonotypes -#' between two samples. The clonotypes will be categorized by counts into singlets or multiplets, -#' either exclusive or shared between the selected samples. Visualization inspired -#' by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' scatterClonotype(combined, x.axis = "PY_P", y.axis = "PY_T", -#' graph = "proportion") -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param x.axis name of the list element to appear on the x.axis -#' @param y.axis name of the list element to appear on the y.axis -#' @param dot.size either total or the name of the list element to -#' use for size of dots -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param graph graph either proportion or raw clonotype count -#' @param exportTable Returns the data frame used for forming the graph. -#' @param seed the integer seed to set for the random variation of point coords. -#' -#' @import ggplot2 -#' -#' @export -#' @return ggplot of the relative clonotype numbers - -scatterClonotype <- function( - df, cloneCall ="strict", - x.axis = NULL, y.axis = NULL, - chain = "both", - dot.size = "total", - split.by = NULL, - graph = "proportion", - exportTable = FALSE, - seed = NULL -) { - if (!is.null(seed)) {set.seed(seed)} - - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) - if (chain != "both") { - for (i in axes) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } - x.df <- as.data.frame(table(df[[x.axis]][,cloneCall])) - colnames(x.df)[2] <- x.axis - y.df <- as.data.frame(table(df[[y.axis]][,cloneCall])) - colnames(y.df)[2] <- y.axis - combined.df <- merge(x.df, y.df, by = "Var1", all = TRUE) - if (dot.size != "total") { - if (dot.size %!in% colnames(combined.df)) { - size.df <- as.data.frame(table(df[[dot.size]][,cloneCall])) - colnames(size.df)[2] <- dot.size - combined.df <- merge(combined.df, size.df, by = "Var1", all = TRUE) } - combined.df[is.na(combined.df)] <- 0 - combined.df[,paste0("size", ".fraction")] <- combined.df[,dot.size]/sum(combined.df[,dot.size]) - labeling <- unique(c(x.axis, y.axis, dot.size)) - } else { - combined.df[is.na(combined.df)] <- 0 - labeling <- unique(c(x.axis, y.axis)) } - combined.df[,"class"] <- NA - combined.df[,"sum"] <- rowSums(combined.df[,labeling]) - for (i in seq_along(labeling)) { - if (length(labeling) > 2) { - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] == 1 & rowSums(combined.df[,labeling[which(labeling != labeling[i])]]) == 0, - paste0(labeling[i], ".singlet"), combined.df[,"class"]) - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] > 1 & rowSums(combined.df[,labeling[which(labeling != labeling[i])]]) == 0, - paste0(labeling[i], ".multiplet"), combined.df[,"class"]) - } else if (length(labeling) == 2) { - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] == 1 & combined.df[,labeling[which(labeling != labeling[i])]] == 0, - paste0(labeling[i], ".singlet"), combined.df[,"class"]) - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] > 1 & combined.df[,labeling[which(labeling != labeling[i])]] == 0, - paste0(labeling[i], ".multiplet"), combined.df[,"class"]) - }} - combined.df[,"class"] <- ifelse(combined.df[,y.axis] >= 1 & combined.df[,x.axis] >= 1, paste0("dual.expanded"), combined.df[,"class"]) - combined.df[,paste0(x.axis, ".fraction")] <- combined.df[,x.axis]/sum(combined.df[,x.axis]) - combined.df[,paste0(y.axis, ".fraction")] <- combined.df[,y.axis]/sum(combined.df[,y.axis]) - if (graph == "proportion") { - x <- combined.df[,paste0(x.axis, ".fraction")] - y <- combined.df[,paste0(y.axis, ".fraction")] - } else if (graph == "count") { - x <- combined.df[,x.axis] - y <- combined.df[,y.axis] } - if (dot.size != "total") { - size <- combined.df[,dot.size] - } else { size <- combined.df[,"sum"] } - if (exportTable == TRUE) { return(combined.df) } - plot <- ggplot(combined.df, aes(x=x, y = y, color = class)) + - theme_classic() + - scale_color_manual(values = colorblind_vector(length(unique(combined.df$class)))) + - xlab(x.axis) + ylab(y.axis) + labs(size = "Total n") - if (graph == "proportion") { - plot <- plot + geom_abline(slope = 1, intercept = 0, alpha = 0.4, lty=2) + - scale_y_sqrt() + scale_x_sqrt() - } else if (graph == "count") { - plot <- plot + ylim(0, max(x,y)) + xlim(0, max(x,y)) - } - plot <- plot + geom_jitter(aes(size = size)) - return(plot) -} - -#' Hierarchical clustering of clonotypes on clonotype size and -#' Jensen-Shannon divergence -#' -#' This function produces a hierarchical clustering of clonotypes by sample -#' using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold -#' model in -#' \href{https://bioconductor.org/packages/devel/bioc/html/powerTCR.html}{powerTCR R package}. -#' Please read and cite PMID: 30485278 if using the function for analyses. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' clonesizeDistribution(combined, cloneCall = "strict", method="ward.D2") -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param threshold Numerical vector containing the thresholds -#' the grid search was performed over. -#' @param method The clustering parameter for the dendrogram. -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param exportTable Returns the data frame used for forming the graph. -#' @importFrom dplyr bind_rows -#' @importFrom ggplot2 ggplot -#' @importFrom powerTCR fdiscgammagpd get_distances -#' @export -#' @return ggplot dendrogram of the clone size distribution - -clonesizeDistribution <- function( - df, - cloneCall ="strict", - chain = "both", - method = "ward.D2", - threshold = 1, - group.by = NULL, - split.by = NULL, - exportTable = FALSE -) { - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - if(!is.null(group.by)) { - df <- groupList(df, group.by) - } - data <- bind_rows(df) - unique_df <- unique(data[,cloneCall]) - Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) - Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) - colnames(Con.df)[1] <- "clonotype" - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - data <- df[[i]] - data <- data.frame(table(data[,cloneCall]), - stringsAsFactors = FALSE) - colnames(data) <- c(cloneCall, "Freq") - for (y in seq_along(unique_df)){ - clonotype.y <- Con.df$clonotype[y] - location.y <- which(clonotype.y == data[,cloneCall]) - Con.df[y,i+1] <- data[location.y[1],"Freq"] - } - } - colnames(Con.df)[2:(length(df)+1)] <- names(df) - Con.df[is.na(Con.df)] <- 0 - list <- list() - for (i in seq_along(df)) { - list[[i]] <- Con.df[,i+1] - list[[i]] <- suppressWarnings(fdiscgammagpd(list[[i]], useq = threshold)) - } - names(list) <- names(df) - grid <- 0:10000 - distances <- get_distances(list, grid, modelType="Spliced") - hclust <- hclust(as.dist(distances), method = method) - hcd <- as.dendrogram(hclust) - plot <- plot(hcd) - if (exportTable) { return(distances) } - return(plot) -} - -#This is the basic color palette for the package -colorblind_vector <- colorRampPalette(rev(c("#0D0887FF", "#47039FFF", "#7301A8FF", "#9C179EFF", - "#BD3786FF", "#D8576BFF","#ED7953FF","#FA9E3BFF", "#FDC926FF", "#F0F921FF"))) - -#Making lodes to function in alluvial plots -#' @importFrom ggalluvial to_lodes_form -makingLodes <- function(meta2, color, alpha, facet, set.axes) { - if (!is.null(color) & !is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2,key="x",value="stratum",id="alluvium", - axes=set.axes,diffuse=c(as.name(color),as.name(alpha),as.name(facet))) - } else if (!is.null(color) & !is.null(alpha) & is.null(facet)) { - lodes <- to_lodes_form(meta2,key="x",value="stratum",id="alluvium", - axes = set.axes, diffuse = c(as.name(color), as.name(alpha))) - } else if (!is.null(color) & is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2,key="x",value="stratum",id ="alluvium", - axes =set.axes, diffuse = c(as.name(color), as.name(facet))) - } else if (is.null(color) & is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id="alluvium",axes=set.axes,diffuse=c(as.name(alpha),as.name(facet))) - } else if (is.null(color) & is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes, diffuse = c(as.name(facet))) - } else if (!is.null(color) & is.null(alpha) & is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes, diffuse = c(as.name(color))) - } else if (is.null(color) & !is.null(alpha) & is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes, diffuse = c(as.name(alpha))) - } else { lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes)} - return(lodes) } - - -#' Visualizing the distribution of gene usage -#' -#' This function will allow for the visualizing the distribution -#' of the any VDJ and C gene of the TCR or BCR using heatmap or -#' bar chart. This function requires assumes two chains were used in -#' defining clonotype, if not, it will default to the only chain -#' present regardless of the chain parameter. -#' -#' @examples -#' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' -#' vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), -#' or combineExpression(). -#' @param gene Which part of the immune receptor to visualize - V, D, J, C -#' @param chain indicate the specific chain should be used - -#' e.g. "TRA", "TRG", "IGH", "IGL" (no both option here) -#' @param plot The type of plot to return - heatmap or bar -#' @param y.axis Variable to separate the y-axis, can be both categorical or other gene -#' gene segments such as V, D, J, or C. -#' @param order Categorical variable to organize the x-axis, either "gene" or "variance" -#' @param scale Converts the individual count of genes to proportion using the total -#' respective repertoire size -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param exportTable Returns the data frame used for forming the graph. -#' @import ggplot2 -#' @importFrom stringr str_split -#' @importFrom stats sd -#' @importFrom dplyr bind_rows -#' @export -#' @return ggplot bar diagram or heatmap of gene usage - -vizGenes <- function(df, - gene = "V", - chain = "TRA", - plot = "heatmap", - y.axis = "sample", - order = "gene", - scale = TRUE, - group.by = NULL, - split.by = NULL, - exportTable = FALSE) { - df <- list.input.return(df, split.by = split.by) - if(!is.null(group.by)) { - df <- groupList(df, group.by) - } - for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") - } - df <- bound.input.return(df) - if (y.axis %!in% colnames(df) | is.null(y.axis)) { - if (y.axis %!in% c("V", "D", "J", "C")) { - y.axis <- "element.names" - } else { - df <- select.gene(df, chain, y.axis) - colnames(df)[ncol(df)] <- y.axis - } - } - df <- select.gene(df, chain, gene) - df <- subset(df, !is.na(df[,ncol(df)])) #remove NA values - df <- subset(df, df[,ncol(df)] != "NA") #remove values that are character "NA" - df <- subset(df, df[,ncol(df)] != "") #remove rows with non genes - #df <- table(df[,ncol(df)], df[,y.axis]) - - if (!is.null(y.axis) && y.axis != "element.names") { - df <- df %>% - group_by(df[,ncol(df)], df[,y.axis], element.names) %>% - count() - } else { - df <- df %>% - group_by(df[,ncol(df)], element.names) %>% - count() - } - df <- df %>% - group_by(element.names) %>% - mutate(sum = sum(n)) - col.lab <- "Total n" - if (scale == TRUE) { - df[,"n"] <- df[,"n"]/df[,"sum"] - col.lab <- "Scaled Values" - } - colnames(df)[1:2] <- c("Var1", "Var2") - df <- df %>% - group_by(Var1, Var2) %>% - mutate(varcount = sum(n), - sd = sd(n, na.rm = TRUE), - mean = mean(n)) - if (order == "variance") { - varOrder <- order(df$varcount, decreasing = TRUE) - df$Var1 <- factor(df$Var1, levels = unique(df$Var1[varOrder])) - } - if (plot == "bar") { - df2 <- unique(df[,c("Var1", "Var2", "sd", "mean")]) - plot <- ggplot(df2, aes(x=Var1, y = mean)) + - geom_bar(stat = "identity") + - geom_errorbar(aes(ymin=mean, ymax=mean+sd), width=.2, - position=position_dodge(.9)) + - theme_classic() + - theme(axis.title.x = element_blank(), #remove titles - axis.title.y = element_blank(), - axis.ticks.x = element_blank(), #removes ticks - axis.text.x = element_text(angle = 90, - vjust = 0.5, hjust=1, size=rel(0.5))) + - facet_grid(Var2~.) - } else if (plot == "heatmap") { - - plot <- ggplot(df, aes(x=Var1, y = Var2)) + - geom_tile(aes(fill = n), color = "black") + - theme_classic() + - labs(fill = col.lab) + - theme(axis.title.x = element_blank(), #remove titles - axis.ticks.x = element_blank(), #removes ticks - axis.text.x = element_text(angle = 90, - vjust = 0.5, hjust=1, size=rel(0.5)), - axis.title.y = element_blank(), - axis.text.y = element_text(size=rel(0.5))) + - scale_fill_gradientn(colors = rev(colorblind_vector(5))) - } - if (exportTable == TRUE) { return(df) } - return(plot) -} diff --git a/R/vizGenes.R b/R/vizGenes.R new file mode 100644 index 00000000..951785d9 --- /dev/null +++ b/R/vizGenes.R @@ -0,0 +1,127 @@ +#' Visualizing the distribution of gene usage +#' +#' This function will allow for the visualizing the distribution +#' of the any VDJ and C gene of the TCR or BCR using heatmap or +#' bar chart. This function requires assumes two chains were used in +#' defining clonotype, if not, it will default to the only chain +#' present regardless of the chain parameter. +#' +#' @examples +#' #Making combined contig data +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' +#' vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), +#' or combineExpression(). +#' @param gene Which part of the immune receptor to visualize - V, D, J, C +#' @param chain indicate the specific chain should be used - +#' e.g. "TRA", "TRG", "IGH", "IGL" (no both option here) +#' @param plot The type of plot to return - heatmap or bar +#' @param y.axis Variable to separate the y-axis, can be both categorical or other gene +#' gene segments such as V, D, J, or C. +#' @param order Categorical variable to organize the x-axis, either "gene" or "variance" +#' @param scale Converts the individual count of genes to proportion using the total +#' respective repertoire size +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param exportTable Returns the data frame used for forming the graph. +#' @import ggplot2 +#' @importFrom stringr str_split +#' @importFrom stats sd +#' @importFrom dplyr bind_rows +#' @export +#' @return ggplot bar diagram or heatmap of gene usage + +vizGenes <- function(df, + gene = "V", + chain = "TRA", + plot = "heatmap", + y.axis = "sample", + order = "gene", + scale = TRUE, + group.by = NULL, + split.by = NULL, + exportTable = FALSE) { + df <- list.input.return(df, split.by = split.by) + if(!is.null(group.by)) { + df <- groupList(df, group.by) + } + for(i in seq_along(df)) { + df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") + } + df <- bound.input.return(df) + if (y.axis %!in% colnames(df) | is.null(y.axis)) { + if (y.axis %!in% c("V", "D", "J", "C")) { + y.axis <- "element.names" + } else { + df <- select.gene(df, chain, y.axis) + colnames(df)[ncol(df)] <- y.axis + } + } + df <- select.gene(df, chain, gene) + df <- subset(df, !is.na(df[,ncol(df)])) #remove NA values + df <- subset(df, df[,ncol(df)] != "NA") #remove values that are character "NA" + df <- subset(df, df[,ncol(df)] != "") #remove rows with non genes + #df <- table(df[,ncol(df)], df[,y.axis]) + + if (!is.null(y.axis) && y.axis != "element.names") { + df <- df %>% + group_by(df[,ncol(df)], df[,y.axis], element.names) %>% + count() + } else { + df <- df %>% + group_by(df[,ncol(df)], element.names) %>% + count() + } + df <- df %>% + group_by(element.names) %>% + mutate(sum = sum(n)) + col.lab <- "Total n" + if (scale == TRUE) { + df[,"n"] <- df[,"n"]/df[,"sum"] + col.lab <- "Scaled Values" + } + colnames(df)[1:2] <- c("Var1", "Var2") + df <- df %>% + group_by(Var1, Var2) %>% + mutate(varcount = sum(n), + sd = sd(n, na.rm = TRUE), + mean = mean(n)) + if (order == "variance") { + varOrder <- order(df$varcount, decreasing = TRUE) + df$Var1 <- factor(df$Var1, levels = unique(df$Var1[varOrder])) + } + if (plot == "bar") { + df2 <- unique(df[,c("Var1", "Var2", "sd", "mean")]) + plot <- ggplot(df2, aes(x=Var1, y = mean)) + + geom_bar(stat = "identity") + + geom_errorbar(aes(ymin=mean, ymax=mean+sd), width=.2, + position=position_dodge(.9)) + + theme_classic() + + theme(axis.title.x = element_blank(), #remove titles + axis.title.y = element_blank(), + axis.ticks.x = element_blank(), #removes ticks + axis.text.x = element_text(angle = 90, + vjust = 0.5, hjust=1, size=rel(0.5))) + + facet_grid(Var2~.) + } else if (plot == "heatmap") { + + plot <- ggplot(df, aes(x=Var1, y = Var2)) + + geom_tile(aes(fill = n), color = "black") + + theme_classic() + + labs(fill = col.lab) + + theme(axis.title.x = element_blank(), #remove titles + axis.ticks.x = element_blank(), #removes ticks + axis.text.x = element_text(angle = 90, + vjust = 0.5, hjust=1, size=rel(0.5)), + axis.title.y = element_blank(), + axis.text.y = element_text(size=rel(0.5))) + + scale_fill_gradientn(colors = rev(colorblind_vector(5))) + } + if (exportTable == TRUE) { return(df) } + 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b/tests/testthat/test-seuratFunctions.R index 4cb7625d..223073b3 100644 --- a/tests/testthat/test-seuratFunctions.R +++ b/tests/testthat/test-seuratFunctions.R @@ -1,11 +1,11 @@ # test script for seuratFunctions.R - testcases are NOT comprehensive! test_that("combineExpression works with seurat objects", { - data("combined_mini_contig_list", "screp_example") - combined_test <- combineExpression(combined_mini_contig_list, screp_example) + data("mini_contig_list", "scRep_example") + combined_test <- combineExpression(mini_contig_list, scRep_example) expect_length(combined_test@meta.data, 15) - expect_equal(combined_test@meta.data[, 1:8], screp_example@meta.data[, 1:8]) + expect_equal(combined_test@meta.data[, 1:8], scRep_example@meta.data[, 1:8]) expect_equal( combined_test@meta.data[, 9:15], getdata("seuratFunctions", "combineExpression_new_metadata") diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R index f70d0667..7bf8279a 100644 --- a/tests/testthat/test-seurat_command.R +++ b/tests/testthat/test-seurat_command.R @@ -1,7 +1,7 @@ test_that("make_screp_seurat_cmd works", { - data("screp_example", "combined_mini_contig_list") + data("scRep_example", "mini_contig_list") - test_obj <- combineExpression(combined_mini_contig_list, screp_example) + test_obj <- combineExpression(mini_contig_list, scRep_example) expect_true(!is.null(test_obj@commands[["combineExpression"]])) test_obj <- test_obj@commands[["combineExpression"]] @@ -11,7 +11,7 @@ test_that("make_screp_seurat_cmd works", { expect_identical(test_obj@assay.used, "integrated") expect_identical( test_obj@call.string, - "combineExpression(combined_mini_contig_list, screp_example)" + "combineExpression(mini_contig_list, scRep_example)" ) expect_equal( test_obj@params, diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 0337b346..10226a34 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -4,7 +4,7 @@ author: name: Nick Borcherding email: ncborch@gmail.com affiliation: Washington University in St. Louis, School of Medicine, St. Louis, MO, USA -date: "July 1st, 2023" +date: "September 6th, 2023" output: BiocStyle::html_document: toc_float: true @@ -141,8 +141,7 @@ As the output of CellRanger are quantifications of both the TCRA and TCRB chains ```{r} combined <- combineTCR(contig_list, - samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), - ID = c("P", "T", "P", "T", "P", "T")) + samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) ``` The output of `combineTCR()` will be a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It will also combine the multiple reads into clonotype calls by either the nucleotide sequence (CTnt), amino acid sequence (CTaa), the VDJC gene sequence (CTgene) or the combination of the nucleotide and gene sequence (CTstrict). The analogous function for B cells, `combineBCR()` functions similarly with 2 major caveats: 1) Each barcode can only have a maximum of 2 sequences, if greater exists, the 2 with the highest reads are selected. 2) The strict definition of clonotype (CTstrict) is based on the V gene and >85% normalized Levenshtein distance of the nucleotide sequence. The Levenshtein distance is calculated across all BCR sequences recovered, regardless of the run. @@ -512,17 +511,17 @@ scatterClonotype(combined, cloneCall ="gene", # Interacting with mRNA Expression -As mentioned previously, this data set is derived from work performed in the laboratory of [Weizhou Zhang]("https://pathology.ufl.edu/faculty/experimental-pathology/weizhou-zhang-ph-d/"). For the purposes of the vignette, we have randomly sampled 100 cells from the the fully integrated Seurat object to minimize the size of the package. A full version of the Seurat object is available via [GitHub](https://github.com/ncborcherding/scRepertoire) under the *Getting Data* header. We will use both Seurat and SingleCellExperiment (SCE) with scater to perform the further visualizations in tandem. +The data in the scRepertoire package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. For the purposes of the vignette, we have randomly sampled 500 cells from the the fully integrated Seurat object to minimize the size of the package. A full version of the Seurat object is available via [GitHub](https://github.com/ncborcherding/scRepertoire) under the *Getting Data* header. We will use both Seurat and SingleCellExperiment (SCE) with scater to perform the further visualizations in tandem. ```{r} library(Seurat) library(scater) -screp_example <- get(data("screp_example")) -sce <- suppressMessages(UpdateSeuratObject(screp_example)) -sce <- as.SingleCellExperiment(screp_example) +scRep_example <- get(data("scRep_example")) +sce <- suppressMessages(UpdateSeuratObject(scRep_example)) +sce <- as.SingleCellExperiment(scRep_example) #Seurat Format -DimPlot(screp_example) +DimPlot(scRep_example) ##Single Cell Experiment Format plotUMAP(sce, colour_by = "seurat_clusters") @@ -531,7 +530,7 @@ plotUMAP(sce, colour_by = "seurat_clusters") Here you can see we have 12 total clusters (C1-12), which we have labeled as such for simplicity. We can also get a little more granular information on the number of cells by using the *table()* function. ```{r} -table(screp_example$seurat_clusters) +table(scRep_example$seurat_clusters) ``` @@ -542,8 +541,8 @@ We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 a In order to categorize the frequency, we have the variable **proportion** which if TRUE allows for the relative proportion or when FALSE will use absolute frequency to define clonotype groups **cloneTypes** acts as a bin to place labels. As a default, **cloneTypes** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below you can see an example of using total frequency as expansion assignments. ```{r} -screp_example <- combineExpression(combined, - screp_example, +scRep_example <- combineExpression(combined, + scRep_example, cloneCall="gene", group.by = "sample", proportion = FALSE, @@ -581,10 +580,10 @@ colorblind_vector <- colorRampPalette(rev(c("#0D0887FF", "#47039FFF", "#7301A8FF", "#9C179EFF", "#BD3786FF", "#D8576BFF", "#ED7953FF","#FA9E3BFF", "#FDC926FF", "#F0F921FF"))) -DimPlot(screp_example, group.by = "Type") + NoLegend() + +DimPlot(scRep_example, group.by = "Type") + NoLegend() + scale_color_manual(values=colorblind_vector(2)) -DimPlot(screp_example, group.by = "cloneType") +DimPlot(scRep_example, group.by = "cloneType") ``` ## clonalOverlay @@ -592,7 +591,7 @@ DimPlot(screp_example, group.by = "cloneType") Using the dimensional reduction graphs as a reference, we can also generate an overlay of the position of clonally expanded cells using `clonalOverlay()`. Select the **reduction** for the visualization, default is "PCA" and the **freq.cutpoint** or lowest clonal frequency or proportion to generate the contour plot. We can modify the contours by selecting the number of **bins** or number of contours drawn. `clonalOverlay()` can be used to look across all cells or faceted by a meta data variable using **facet**. As we facet, the overall dimensional reduction will be maintained, while the contour plots will adjust based on the **facet** variable. Coloring of the dot plot is based on the active identity of the single-cell object. This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs). ```{r} -clonalOverlay(screp_example, +clonalOverlay(scRep_example, reduction = "umap", freq.cutpoint = 1, bins = 10, @@ -621,7 +620,7 @@ Filtering for clones can be accomplished using 3 methods: library(ggraph) #No Identity filter -clonalNetwork(screp_example, +clonalNetwork(scRep_example, reduction = "umap", identity = "seurat_clusters", filter.clones = NULL, @@ -629,7 +628,7 @@ clonalNetwork(screp_example, cloneCall = "aa") #Examining Cluster 2 only -clonalNetwork(screp_example, +clonalNetwork(scRep_example, reduction = "umap", identity = "ident", filter.identity = "C2", @@ -639,7 +638,7 @@ clonalNetwork(screp_example, We can also use **exportClones** to quickly get clones that are shared across multiple identity groups, along with the total number of clones in the data set. ```{r} -shared.clones <- clonalNetwork(screp_example, +shared.clones <- clonalNetwork(scRep_example, reduction = "umap", identity = "ident", cloneCall = "aa", @@ -652,12 +651,12 @@ head(shared.clones) We can also look at the clonotypes by calling specific sequences in the `highlightClonotypes()` below. In order to highlight the clonotypes, we first need to use the **cloneCall** the type of sequence we will be using and then the specific sequences themselves using **sequence**. Below you can see the steps to highlight the two most prominent sequences *"CAVNGGSQGNLIF_CSAEREDTDTQYF"* with a frequency = 482 and *"NA_CATSATLRVVAEKLFF"* with a frequency = 287. ```{r} -screp_example <- highlightClonotypes(screp_example, +scRep_example <- highlightClonotypes(scRep_example, cloneCall= "aa", sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF", "NA_CATSATLRVVAEKLFF")) -Seurat::DimPlot(screp_example, group.by = "highlight") +Seurat::DimPlot(scRep_example, group.by = "highlight") ``` ## occupiedscRepertoire @@ -668,10 +667,10 @@ We can also look at the count of cells by cluster assigned into specific frequen + **label** will still return absolute number of clonotypes ```{r} -occupiedscRepertoire(screp_example, +occupiedscRepertoire(scRep_example, x.axis = "seurat_clusters") -occupiedscRepertoire(screp_example, +occupiedscRepertoire(scRep_example, x.axis = "ident", proportion = TRUE, label = FALSE) @@ -682,14 +681,14 @@ occupiedscRepertoire(screp_example, After the metadata has been modified, we can look at clonotypes across multiple categories using the `alluvialClonotypes()` function. To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html), essentially we are able to use the plots to examine the interchange of categorical variables. Because this function will produce a graph with each clonotype arranged by called stratification, this will take some time depending on the size of your total cells. To expedite, we will actually subset the Seurat object before using `alluvialClonotypes()`. ```{r} -alluvialClonotypes(screp_example, +alluvialClonotypes(scRep_example, cloneCall = "aa", y.axes = c("Patient", "ident", "Type"), color = "CAVNGGSQGNLIF_CSAEREDTDTQYF") + scale_fill_manual(values = c("grey", colorblind_vector(5)[5])) -alluvialClonotypes(screp_example, +alluvialClonotypes(scRep_example, cloneCall = "gene", y.axes = c("Patient", "ident", "Type"), color = "seurat_clusters") @@ -703,12 +702,12 @@ Like alluvial graphs, we can also visualize the interconnection of clusters usin library(circlize) library(scales) -circles <- getCirclize(screp_example, +circles <- getCirclize(scRep_example, group.by = "seurat_clusters") #Just assigning the normal colors to each cluster -grid.cols <- hue_pal()(length(unique(screp_example$seurat_clusters))) -names(grid.cols) <- unique(screp_example$seurat_clusters) +grid.cols <- hue_pal()(length(unique(scRep_example$seurat_clusters))) +names(grid.cols) <- unique(scRep_example$seurat_clusters) #Graphing the chord diagram chordDiagram(circles, self.link = 1, grid.col = grid.cols) @@ -717,7 +716,7 @@ chordDiagram(circles, self.link = 1, grid.col = grid.cols) This can also be used if we wanted to explore just the tumor-specific T cells by just subsetting the single-cell object. ```{r} -subset <- subset(screp_example, Type == "T") +subset <- subset(scRep_example, Type == "T") circles <- getCirclize(subset, group.by = "ident") @@ -767,7 +766,7 @@ In order to use the ```StartracDiversity()``` function, you will need to include + tran - State Transition ```{r} -StartracDiversity(screp_example, +StartracDiversity(scRep_example, type = "Type", sample = "Patient", by = "overall") @@ -784,7 +783,7 @@ A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/3582969 * The compartment/variable used for the purpose of the comparison ```{r} -clonotypeBias(screp_example, +clonotypeBias(scRep_example, cloneCall = "aa", split.by = "Type", group.by = "seurat_clusters", @@ -798,7 +797,7 @@ clonotypeBias(screp_example, For users that would like greater ability to use the meta data in the Seurat objects to perform the analysis that scRepertoire provides, there is also the option of using the *expression2List()* function that will take the meta data and output the data as a list by cluster. ```{r} -combined2 <- expression2List(screp_example, split.by = "seurat_clusters") +combined2 <- expression2List(scRep_example, split.by = "seurat_clusters") combined3 <- expression2List(sce, split.by = "seurat_clusters") ``` From 72a046435e7c2a8ac9a39d50002d577f99431b75 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 13:06:31 -0500 Subject: [PATCH 020/142] Update combineExpression.R Remove extra barcode addition to meta data --- R/combineExpression.R | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/R/combineExpression.R b/R/combineExpression.R index ad19aa70..c58b40e9 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -140,7 +140,9 @@ combineExpression <- function( "CTaa", "CTstrict", "Frequency", "cloneType")]) dup <- PreMeta$barcode[which(duplicated(PreMeta$barcode))] PreMeta <- PreMeta[PreMeta$barcode %!in% dup,] - rownames(PreMeta) <- PreMeta$barcode + barcodes <- PreMeta$barcode + PreMeta <- PreMeta[,-1] + rownames(PreMeta) <- barcodes if (group.by != "none" && addLabel) { location <- which(colnames(PreMeta) == "Frequency") colnames(PreMeta)[location] <- paste0("Frequency.", group.by) @@ -176,7 +178,7 @@ combineExpression <- function( ) } sc -} # Qile: I think the barcode column added to the metadata is redundant? Since it matches the row names? +} From 4afc3214cf8978ac3f5a363ac29272e6ef0838a6 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 13:37:14 -0500 Subject: [PATCH 021/142] finished percentAA --- R/clonalHomeostasis.R | 6 +++--- R/highlightClonotypes.R | 5 +++-- R/occupiedscRepertoire.R | 5 +++-- R/percentAA.R | 43 ++++++++++++++++++++++++++++++---------- 4 files changed, 42 insertions(+), 17 deletions(-) diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 43f133d0..a8dd50c8 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -9,9 +9,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' clonalHomeostasis(combined, cloneCall = "gene") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/highlightClonotypes.R b/R/highlightClonotypes.R index d1d4106e..50267e5c 100644 --- a/R/highlightClonotypes.R +++ b/R/highlightClonotypes.R @@ -5,8 +5,9 @@ #' #' @examples #' #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example ")) diff --git a/R/occupiedscRepertoire.R b/R/occupiedscRepertoire.R index f881a7a2..89ab5336 100644 --- a/R/occupiedscRepertoire.R +++ b/R/occupiedscRepertoire.R @@ -9,8 +9,9 @@ #' #' @examples #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) diff --git a/R/percentAA.R b/R/percentAA.R index 8280f17e..6eed348a 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -1,4 +1,14 @@ - +#' Examining the relative amino acid composition by position +#' +#' This function the proportion of amino acids along the residues +#' of the cdr3 amino acid sequence. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' percentAA(combined, chain = "TRB", aa.length = 20) #' @param df The product of combineTCR(), combineBCR(), #' expression2List(), or combineExpression(). @@ -6,28 +16,28 @@ #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. -#' @param order Maintain the order of the list when plotting -#' @param scale Converts the graphs into percentage of unique clonotypes. +#' @param aa.length The maximum length of the cdr3 amino acid sequence #' @param exportTable Returns the data frame used for forming the graph #' @import ggplot2 #' @export -#' @return ggplot of the total or relative unique clonotypes +#' @return ggplot of stacked bar graphs of amino acid proportions percentAA <- function(df, chain = "TRB", group.by = NULL, split.by = NULL, + aa.length = 30, exportTable = FALSE) df <- list.input.return(df, split.by) df <- checkBlanks(df, "CTaa") for(i in seq_along(df)) { df[[i]] <- off.the.chain(df[[i]], chain, "CTaa") } - + res.list <- list() for (i in seq_along(df)) { strings <- df[[i]][,"CTaa"] strings <- do.call(c,str_split(strings, ";")) strings <- strings[strings != "NA"] - aa.length <- max(nchar(strings)) + strings <- strings[nchar(strings) < aa.length] strings <- .padded_strings(strings, aa.length) strings <- do.call(rbind, strings) @@ -40,11 +50,24 @@ percentAA <- function(df, res <- suppressWarnings(Reduce(function(...) merge(..., all = TRUE, by="x"), aa.output)) colnames(res) <- c("AA", paste0("pos.", seq_len(aa.length))) res[1:20,][is.na(res[1:20,])] <- 0 - ###### - #Need to come up with a summary function + melt.res <- suppressMessages(melt(res)) + melt.res$group <- names(df)[i] + res.list[[i]] <- melt.res } - #Need to come up with a visualization - + mat_melt <- do.call(rbind, res.list) + plot <- ggplot(mat_melt, aes(x=as.factor(variable), y = value, fill=AA)) + + geom_bar(stat = "identity", position="fill", lwd= 0.25, color = "black") + + scale_fill_manual(name = "Amino Acid", + values = rev(colorblind_vector(21))) + + xlab("Amino Acid Residues") + + ylab("Relative Percent") + + theme_classic() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) + if(length(res.list) > 1) { + plot <- plot + facet_grid(group~.) + } + if (exportTable == TRUE) { return(mat_melt) } + return(plot) } .padded_strings <- function(strings, max.length) { From eac53270af43faae767b05cdd0bd3e2de1a51fb9 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 13:44:16 -0500 Subject: [PATCH 022/142] update examples to new data --- R/abundanceContig.R | 6 +++--- R/alluvialClonotypes.R | 5 +++-- R/checkContig.R | 6 +++--- R/clonalNetwork.R | 5 +++-- R/clonalOverlay.R | 15 +++++++++------ R/clonalProportion.R | 6 +++--- R/clonesizeDistribution.R | 6 +++--- R/clusterTCR.R | 5 +++-- R/combineContigs.R | 6 +++--- R/combineExpression.R | 5 +++-- R/compareClonotypes.R | 6 +++--- R/diversity.R | 6 +++--- R/lengthContig.R | 6 +++--- R/overlap.R | 6 +++--- R/processing.R | 10 ++++++---- R/quantContig.R | 6 +++--- R/scatterClonotypes.R | 8 ++++---- R/startrac.R | 5 +++-- R/vizGenes.R | 6 +++--- 19 files changed, 67 insertions(+), 57 deletions(-) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index 440071bb..7ad618e9 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -10,9 +10,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' abundanceContig(combined, cloneCall = "gene", scale = FALSE) #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index c3d9943e..221a5434 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -37,8 +37,9 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' #' @examples #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) diff --git a/R/checkContig.R b/R/checkContig.R index 47589e1c..8c966a84 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -8,9 +8,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' checkContig(combined, chain = "TRA", examine = "chain", group.by = "sample") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index f7bf4cc6..a077be79 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -9,8 +9,9 @@ #' \dontrun{ #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index 9309d3b9..474bd90e 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -7,18 +7,21 @@ #' #' @examples #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #' #' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) +#' scRep_example <- combineExpression(combined, +#' scRep_example) #' #' #Using clonalOverlay() -#' clonalOverlay(sce, reduction = "umap", freq.cutpoint = 0.3, bins = 5) +#' clonalOverlay(scRep_example, +#' reduction = "umap", +#' freq.cutpoint = 0.3, +#' bins = 5) #' #' @param sc The seurat or SCE object to visualize after combineExpression(). #' @param reduction The dimensional reduction to visualize diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 409e2f82..30082a9a 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -9,9 +9,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' clonalProportion(combined, cloneCall = "gene") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/clonesizeDistribution.R b/R/clonesizeDistribution.R index 87a43dbf..895a0ea3 100644 --- a/R/clonesizeDistribution.R +++ b/R/clonesizeDistribution.R @@ -9,9 +9,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' clonesizeDistribution(combined, cloneCall = "strict", method="ward.D2") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/clusterTCR.R b/R/clusterTCR.R index fcf76aa2..b339c351 100644 --- a/R/clusterTCR.R +++ b/R/clusterTCR.R @@ -11,8 +11,9 @@ #' #' @examples # Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' sub_combined <- clusterTCR(combined[[2]], chain = "TRA", sequence = "aa") #' diff --git a/R/combineContigs.R b/R/combineContigs.R index 83837a9a..49e7d864 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -34,9 +34,9 @@ utils::globalVariables(c( #' columns after the existing raw clonotype data #' #' @examples -#' combineTCR(contig_list, -#' samples = rep(c("PX", "PY", "PZ"), each=2), -#' ID = rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' @param df List of filtered contig annotations from 10x Genomics. #' @param samples The labels of samples (required). diff --git a/R/combineExpression.R b/R/combineExpression.R index c58b40e9..9dfcb2e4 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -13,8 +13,9 @@ #' #' @examples #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) diff --git a/R/compareClonotypes.R b/R/compareClonotypes.R index 9a103788..60b15d11 100644 --- a/R/compareClonotypes.R +++ b/R/compareClonotypes.R @@ -8,9 +8,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' compareClonotypes(combined, numbers = 10, #' samples = c("PX_P", "PX_T"), cloneCall="aa") #' diff --git a/R/diversity.R b/R/diversity.R index dda19486..e701fa62 100644 --- a/R/diversity.R +++ b/R/diversity.R @@ -10,9 +10,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' clonalDiversity(combined, cloneCall = "gene") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/lengthContig.R b/R/lengthContig.R index b1ba15eb..7a3ccae9 100644 --- a/R/lengthContig.R +++ b/R/lengthContig.R @@ -12,9 +12,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' lengthContig(combined, cloneCall="aa", chain = "both") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/overlap.R b/R/overlap.R index 2471ee64..e8b04e70 100644 --- a/R/overlap.R +++ b/R/overlap.R @@ -9,9 +9,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' clonalOverlap(combined, cloneCall = "gene", method = "overlap") #' diff --git a/R/processing.R b/R/processing.R index fcfc0e76..ab59c17e 100644 --- a/R/processing.R +++ b/R/processing.R @@ -99,8 +99,9 @@ subsetContig <- function(df, name, variables = NULL) { #' #' @examples #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) @@ -145,8 +146,9 @@ expression2List <- function(sc, split.by) { #' #' @examples #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) diff --git a/R/quantContig.R b/R/quantContig.R index 43552a9e..44055811 100644 --- a/R/quantContig.R +++ b/R/quantContig.R @@ -9,9 +9,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' quantContig(combined, cloneCall="strict", scale = TRUE) #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/scatterClonotypes.R b/R/scatterClonotypes.R index 32618ac5..f85445b3 100644 --- a/R/scatterClonotypes.R +++ b/R/scatterClonotypes.R @@ -7,10 +7,10 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' scatterClonotype(combined, x.axis = "PY_P", y.axis = "PY_T", +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' scatterClonotype(combined, x.axis = "P17B", y.axis = "P17L", #' graph = "proportion") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). diff --git a/R/startrac.R b/R/startrac.R index b302d04b..5bcab143 100644 --- a/R/startrac.R +++ b/R/startrac.R @@ -13,8 +13,9 @@ #' @examples #' \dontrun{ #' #Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example ")) diff --git a/R/vizGenes.R b/R/vizGenes.R index 951785d9..c8282e93 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -8,9 +8,9 @@ #' #' @examples #' #Making combined contig data -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) #' From 3de9b4f313f5ee4189a0f646fc0437ceafe7b1e3 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 13:52:43 -0500 Subject: [PATCH 023/142] Update vignette.Rmd modify signposts to actual function calls --- vignettes/vignette.Rmd | 72 +++++++++++++++++------------------------- 1 file changed, 29 insertions(+), 43 deletions(-) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 10226a34..c60d5b81 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -104,7 +104,7 @@ for (i in seq_along(contig_list)) { } ``` -## Loading from other pipelines +## loadContigs New to this version of the package is support for BD Rhapsody, AIRR, [WAT3R](https://www.biorxiv.org/content/10.1101/2022.01.26.477886v1), and [TRUST4](https://www.nature.com/articles/s41592-021-01142-2) single-cell outputs. By indicating the directory of the outputs from these pipelines are located, ```loadContigs()``` will generate a contig list that is compatible with the rest of scRepertoire. Alternatively, ```loadContigs()``` will also accept a list of data frames that you have loaded individualy. In addition, although not necessary, ```loadContigs()``` can be used for 10X filtered_contig_annotation.csv files. @@ -123,7 +123,7 @@ contig.list <- loadContigs(dir = contig.output, format = "WAT3R") ``` -# Combining the Contigs +# combineTCR As the output of CellRanger are quantifications of both the TCRA and TCRB chains, the next step is to create a single list object with the TCR genes (comprised of the VDJC genes) and CDR3 sequences by cell barcode. This is performed using the `combineTCR()`, where the input is the stripped contig_list. There is also the relabeling of the barcodes by sample and ID information to prevent duplicates. @@ -148,7 +148,7 @@ The output of `combineTCR()` will be a list of contig data frames that will be r # Other Processing Functions -## Adding Additional Variables +## addVariable What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the batches in which the samples were processed and sequenced. @@ -160,14 +160,14 @@ example <- addVariable(combined, example[[1]][1:5,ncol(example[[1]])] # This is showing the first 5 values of the new column added ``` -## Subsetting Contigs +## subsetContig -Likewise we can remove specific list elements after `combineTCR()` using the `subsetContig()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and also the variable values to subset (**variables**). Below you can see us isolate just the 4 sequencing results from PX and PY. +Likewise we can remove specific list elements after `combineTCR()` using the `subsetContig()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and also the variable values to subset (**variables**). Below you can see us isolate just the 2 sequencing results from P18L and P18B. ```{r} subset <- subsetContig(combined, name = "sample", - variables = c("PX", "PY")) + variables = c("P18L", "P18B")) ``` *** @@ -182,17 +182,7 @@ subset <- subsetContig(combined, Important to note, that the clonotype is called using essentially the combination of genes or nt/aa CDR3 sequences for both loci. As of this implementation of scRepertoire, clonotype calling is not incorporating small variations within the CDR3 sequences. As such the **gene** approach will be the most sensitive, while the use of **nt** or **aa** moderately so, and the most specific for clonotypes being **strict**. Additionally, the clonotype call is trying to incorporate both loci, *i.e*, both **TCRA** and **TCRB** chains and if a single cell barcode has multiple sequences identified (*i.e.*, 2 TCRA chains expressed in one cell). Using the 10x approach, there is a subset of barcodes that only return one of the immune receptor chains, the unreturned chain is assigned an **NA** value. -## Quantify Clonotypes - -The first function to explore the clonotypes is `quantContig()` to return the total or relative numbers of unique clonotypes. - -**scale** -* TRUE - relative percent of unique clonotypes scaled by total size of the size of the clonotype repertoire -* FALSE - Report the total number of unique clonotypes - -Important to note, that the clonotype is called using essentially the combination of genes or nt/aa CDR3 sequences for both loci. As of this implementation of scRepertoire, clonotype calling is not incorporating small variations within the CDR3 sequences. As such the **gene** approach will be the most sensitive, while the use of **nt** or **aa** moderately so, and the most specific for clonotypes being **strict**. Additionally, the clonotype call is trying to incorporate both loci, *i.e*, both **TCRA** and **TCRB** chains and if a single cell barcode has multiple sequences identified (*i.e.*, 2 TCRA chains expressed in one cell). Using the 10x approach, there is a subset of barcodes that only return one of the immune receptor chains, the unreturned chain is assigned an **NA** value. - -## Quantify Clonotypes +## quantContig The first function to explore the clonotypes is `quantContig()` to return the total or relative numbers of unique clonotypes. @@ -230,7 +220,7 @@ quantContig(combined, scale = TRUE) ``` -## Checking NA values +## checkContig Depending on the experiment or sequencing run, a number of NAs can appear in either the sequence or VDJC gene usage. A quick way to check the percentage of NA values is to use ```checkContigs()``` @@ -253,7 +243,7 @@ checkContig(combined, group.by = "sample") ``` -## Clonotype Abundance +## abundanceContig We can also examine the relative distribution of clonotypes by abundance. Here `abundanceContig()` will produce a line graph with a total number of clonotypes by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the contig object using the **group.by** variable in the function. @@ -275,7 +265,7 @@ abundanceContig(combined, scale = TRUE) ``` -## Length of Clonotypes +## lengthContig We can look at the length distribution of the CDR3 sequences by calling the `lengtheContig()` function. Importantly, unlike the other basic visualizations, the **cloneCall** can only be "nt" or "aa". Due to the method of calling clonotypes as outlined above, the length should reveal a multimodal curve, this is a product of using the **NA** for the unreturned chain sequence and multiple chains within a single barcode. @@ -292,7 +282,7 @@ lengthContig(combined, chain = "TRA") ``` -## Compare Clonotypes +## compareClonotypes We can also look at clonotypes between samples and changes in dynamics by using the `compareClonotypes()` function. @@ -324,12 +314,12 @@ We can also look at clonotypes between samples and changes in dynamics by using ```{r} compareClonotypes(combined, numbers = 10, - samples = c("PX_P", "PX_T"), + samples = c("P17B", "P17L"), cloneCall="aa", graph = "alluvial") ``` -## Visualize Gene Usage +## vizGenes Last of the basic analysis visualizations is the relative usage of genes of the TCR or BCR, using `vizGenes()`. @@ -384,7 +374,8 @@ the default is to separate by the list element names. + "variance" to order by variance between the separate variable categories ```{r} -vizGenes(combined, gene = "V", +vizGenes(combined, + gene = "V", chain = "TRB", plot = "bar", scale = TRUE) @@ -417,7 +408,7 @@ vizGenes(combined[c(2,4,6)], After we have completed the basic processing and summary functions in scRepertoire, we can begin to explore the clonotypes of the single-cell data in more detail. -## Clonal Space Homeostasis +## clonalHomeostasis By examining the clonal space, we are effectively looking at the relative space occupied by clones at specific proportions. Another way to think about this would be thinking of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different number of clonal proportions. Clonal space homeostasis is asking what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cutpoints are set under the **cloneType** variable in the function and can be adjusted, at baseline the bins are as follows: @@ -434,7 +425,7 @@ clonalHomeostasis(combined, cloneCall = "aa") ``` -## Clonal Proportion +## clonalProportion Like clonal space homeostasis above, clonal proportion acts to place clones into separate bins. The key difference is instead of looking at the relative proportion of the clone to the total, the `clonalProportion()` function will rank the clones by total number and place them into bins. @@ -454,7 +445,7 @@ clonalProportion(combined, cloneCall = "nt") ``` -## Overlap Analysis +## clonalOverlap If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. Three methods currently can be performed in `clonalOverlap()` 1) overlap coefficient, 2) Morisita index, or 3) Jaccard index. The former is looking at the overlap of clonotypes scaled to the length of unique clonotypes in the smaller sample. The Morisita index is more complex, it is an ecological measure of the dispersion of individuals within a population, incorporating the size of the population. The Jaccard Similarity Index is very similar to the overlap coefficient - instead of using the length of the smaller sample, the denominator for the Jaccard Index is the union of the two comparisons, leading to a much smaller number. @@ -484,12 +475,12 @@ clonalDiversity(combined, n.boots = 100) ``` -## Scatter Compare +## scatterClonotype A number of users requested a visualization from the work of [Wu, et al 2020, PMID: 32103181](https://pubmed.ncbi.nlm.nih.gov/32103181/) that allows for the direct comparison of clonotypes between 2 samples. ```scatterClonotype()``` will organize two samples from the combineTCR/BCR product, count the relative clonotypes, and produce a scatter plot comparing the two samples. **x.axis** and **y.axis** -* Names of the list element to place on the x-axis and y-axis - so for example "PX_P" and "PX_T" +* Names of the list element to place on the x-axis and y-axis - so for example "P18L" and "P18B" **dot.size** * "total" to display the total number of clones between the the x- and y-axis @@ -501,8 +492,8 @@ A number of users requested a visualization from the work of [Wu, et al 2020, PM ```{r} scatterClonotype(combined, cloneCall ="gene", - x.axis = "PY_P", - y.axis = "PY_T", + x.axis = "P18B", + y.axis = "P18L", dot.size = "total", graph = "proportion") ``` @@ -527,12 +518,7 @@ DimPlot(scRep_example) plotUMAP(sce, colour_by = "seurat_clusters") ``` -Here you can see we have 12 total clusters (C1-12), which we have labeled as such for simplicity. We can also get a little more granular information on the number of cells by using the *table()* function. - -```{r} -table(scRep_example$seurat_clusters) -``` - +## combineExpression Next we can take the clonotypic information and attach it to our Seurat object using the *combineExpression()* function. **Importantly**, the major requirement for the attachment is matching contig cell barcodes and barcodes in the row names of the meta data of the Seurat or SCE object. If these do not match, the attachment will fail. Based on ease, we suggest you make the changes to the Seurat object row names. @@ -732,7 +718,7 @@ chordDiagram(circles, link.arr.type = "big.arrow") ``` -## Clustering Clonotypes +## clusterTCR The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes, by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function, but now can be applied to the T cell receptors. It will add to the end of each list element a cluster by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. @@ -772,7 +758,7 @@ StartracDiversity(scRep_example, by = "overall") ``` -## Clonotype Bias +## clonotypeBias A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/35829695/), clonal bias, like Startrac is a clonal metric that seeks to quantify how individual clones are skewed towards a specific cellular compartment or cluster. With the minimal example present in the in-built vignette, this will not be as enlightening as when we use a full single-cell object. @@ -801,25 +787,25 @@ combined2 <- expression2List(scRep_example, split.by = "seurat_clusters") combined3 <- expression2List(sce, split.by = "seurat_clusters") ``` -## 1. Clonal Diversity +## 1. clonalDiversity ```{r} clonalDiversity(combined2, cloneCall = "nt") clonalDiversity(combined3, cloneCall = "nt") ``` -## 2. Clonal Homeostasis +## 2. clonalHomeostasis ```{r} clonalHomeostasis(combined2, cloneCall = "nt") clonalHomeostasis(combined3, cloneCall = "nt") ``` -## 3. Clonal Proportion +## 3. clonalProportion ```{r} clonalProportion(combined2, cloneCall = "nt") clonalProportion(combined3, cloneCall = "nt") ``` -## 4. Clonal Overlap +## 4. clonalOverlap ```{r} clonalOverlap(combined2, cloneCall="aa", method="overlap") clonalOverlap(combined3, cloneCall="aa", method="overlap") From 7a47c59bd52fb8384b81d25e9be3f1ea591f131b Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 14:38:26 -0500 Subject: [PATCH 024/142] Update Diversity Remove vegan call and wrote custom functions, expand metrics --- DESCRIPTION | 1 - R/diversity.R | 96 ++++++++++++++++++++++++++++++++++++++++++++++----- R/utils.R | 12 +------ 3 files changed, 88 insertions(+), 21 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 89da8d48..034cd354 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -20,7 +20,6 @@ Imports: reshape2, ggalluvial, stringr, - vegan, powerTCR, SingleCellExperiment, SummarizedExperiment, diff --git a/R/diversity.R b/R/diversity.R index e701fa62..02d6dd03 100644 --- a/R/diversity.R +++ b/R/diversity.R @@ -1,12 +1,11 @@ #' Examine the clonal diversity of samples #' #' This function calculates traditional measures of diversity - Shannon, -#' inverse Simpson, Chao1 index, abundance-based coverage estimators -#' (ACE), and 1-Pielou's measure of species evenness by sample or group. -#' The function automatically down samples the -#' diversity metrics using 100 boot straps The group parameter can be -#' used to condense the individual samples. If a matrix output for -#' the data is preferred, set exportTable = TRUE. +#' inverse Simpson, normalized entropy, Gini-Simpson, Chao1 index, and +#' abundance-based coverage estimators (ACE) measure of species evenness by sample or group. +#' The function automatically down samples the diversity metrics using +#' 100 boot straps The group parameter can be used to condense the individual +#' samples. If a matrix output for the data is preferred, set exportTable = TRUE. #' #' @examples #' #Making combined contig data @@ -25,6 +24,8 @@ #' @param x.axis Additional variable in which to split the x.axis #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. +#' @param metrics The indices to use in diversity calculations - "shannon", "inv.simpson", +#' "norm.entropy", "gini.simpson", "chao1", "ACE" #' @param exportTable Exports a table of the data into the global environment #' in addition to the visualization #' @param n.boots number of bootstraps to downsample in order to get mean diversity @@ -44,6 +45,7 @@ clonalDiversity <- function(df, group.by = NULL, x.axis = NULL, split.by = NULL, + metrics = c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE"), exportTable = FALSE, n.boots = 100, return.boots = FALSE, @@ -73,7 +75,7 @@ clonalDiversity <- function(df, mat_a <- NULL sample <- c() if(skip.boots == TRUE) { - sample <- diversityCall(data) + sample <- .diversityCall(data) mat_a <- rbind(mat_a, sample) mat_a[is.na(mat_a)] <- 0 mat <- rbind(mat, mat_a) @@ -96,8 +98,8 @@ clonalDiversity <- function(df, } } } - colnames(mat) <- c("Shannon", "Inv.Simpson", "Chao", "ACE", "Inv.Pielou") - mat[,"Inv.Pielou"] <- 1 - mat[,"Inv.Pielou"] + colnames(mat) <- c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE") + mat <- mat[,colnames(mat) %in% metrics] if (!is.null(group.by)) { mat[,group.by] <- str_split(names(df), "[.]", simplify = TRUE)[,1] } else { @@ -141,3 +143,79 @@ clonalDiversity <- function(df, return(plot) } + + +.diversityCall <- function(data) { + shannon <- .shannon(data[,"Freq"]) + inv_simpson <- .invsimpson(data[,"Freq"]) + norm_entropy <- .normentropy(data[,"Freq"]) + gini_simpson <- .ginisimpson(data[,"Freq"]) + chao1 <- .chao1(data[,"Freq"]) + ACE <- .ACE(data[,"Freq"]) + out <- c(shannon, inv_simpson, norm_entropy, gini_simpson, chao1,ACE) + return(out) +} + + +.shannon <- function(p){ + p <- p[which(p > 0)] + p <- p / sum(p) + p <- p[which(p > 0)] + return(-sum(p * log(p))) +} +.normentropy <- function(p){ + p <- p[which(p > 0)] + p <- p / sum(p) + p <- p[which(p > 0)] + return(-sum(p * log(p)) / log(length(p))) +} +.invsimpson <- function(p){ + p <- p[which(p > 0)] + p <- p / sum(p) + p <- p[which(p > 0)] + return(1 / sum(p^2)) +} +.ginisimpson <- function(p){ + p <- p[which(p > 0)] + p <- p / sum(p) + p <- p[which(p > 0)] + return(1 - sum(p^2)) +} + +.chao1 <- function(p){ + n1 <- sum(p == 1) + n2 <- sum(p == 2) + S_obs <- length(p) + # Chao1 index calculation + if(n1 > 1 && n2 > 0) { + chao1 <- S_obs + (n1 * (n1 - 1)) / (2 * (n2 + 1)) + } else { + # In cases where n1 <= 1 or n2 == 0, Chao1 is undefined + chao1 <- NA + } + return(chao1) +} + +.ACE <- function(p) { + q = 10 + S_abund <- sum(p > q) + rare_data <- p[p <= q] + S_rare <- length(rare_data) + n_rare <- sum(rare_data) + + # Calculate C_ACE + C_ACE <- sum(p) / n_rare + + # Calculate gamma + gamma <- 0 + for(i in 1:q) { + f_i <- sum(rare_data == i) + gamma <- gamma + (1 - i / q)^f_i + } + + # Calculate ACE + ACE <- S_abund + (S_rare / C_ACE) + (1 - C_ACE) * gamma + return(ACE) +} + + diff --git a/R/utils.R b/R/utils.R index 0983f7cf..c412f014 100644 --- a/R/utils.R +++ b/R/utils.R @@ -216,17 +216,7 @@ filteringNA <- function(sc) { } } -#Calculating diversity using Vegan R package -#' @importFrom vegan diversity estimateR -diversityCall <- function(data) { - w <- diversity(data[,"Freq"], index = "shannon") - x <- diversity(data[,"Freq"], index = "invsimpson") - y <- estimateR(data[,"Freq"])[2] #Chao - z <- estimateR(data[,"Freq"])[4] #ACE - z2 <- diversity(data[,"Freq"], index = "shannon")/log(length(data[,"Freq"])) - out <- c(w,x,y,z, z2) - return(out) -} + #Organizing list of contigs for visualization parseContigs <- function(df, i, names, cloneCall) { From 45ae7f062aa5836624622be23893c4992c88905a Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 15:07:10 -0500 Subject: [PATCH 025/142] Update clonalOverlap Using switch function now for different indices and added cosine similarity --- R/{diversity.R => clonalDiversity.R} | 0 R/clonalOverlap.R | 197 +++++++++++++++++++++++++++ R/overlap.R | 75 ---------- R/utils.R | 94 ------------- 4 files changed, 197 insertions(+), 169 deletions(-) rename R/{diversity.R => clonalDiversity.R} (100%) create mode 100644 R/clonalOverlap.R delete mode 100644 R/overlap.R diff --git a/R/diversity.R b/R/clonalDiversity.R similarity index 100% rename from R/diversity.R rename to R/clonalDiversity.R diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R new file mode 100644 index 00000000..79ece535 --- /dev/null +++ b/R/clonalOverlap.R @@ -0,0 +1,197 @@ +#' Examining the clonal overlap between groups or samples +#' +#' This functions allows for the calculation and visualizations of the +#' overlap coefficient, morisita, or jaccard index for clonotypes +#' using the product of combineTCR(), combineBCR() or expression2list(). +#' The overlap coefficient is calculated using the intersection of clonotypes +#' divided by the length of the smallest component. +#' If a matrix output for the data is preferred, set exportTable = TRUE. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' +#' clonalOverlap(combined, cloneCall = "gene", method = "overlap") +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param method The method to calculate the overlap, either the "overlap" +#' coefficient, "morisita", "jaccard" indices, "cosine" similarity or "raw" +#' for the base numbers. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param exportTable Returns the data frame used for forming the graph +#' @importFrom stringr str_sort +#' @importFrom reshape2 melt +#' @export +#' @return ggplot of the clonotypic overlap between elements of a list +clonalOverlap <- function(df, + cloneCall = "strict", + method = c("overlap", "morisita", "jaccard", "raw"), + chain = "both", + split.by = NULL, + exportTable = FALSE){ + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + df <- df[order(names(df))] + values <- str_sort(as.character(unique(names(df))), numeric = TRUE) + df <- df[quiet(dput(values))] + num_samples <- length(df[]) + names_samples <- names(df) + coef_matrix <- data.frame(matrix(NA, num_samples, num_samples)) + colnames(coef_matrix) <- names_samples + rownames(coef_matrix) <- names_samples + length <- seq_len(num_samples) + if (chain != "both") { + for (i in seq_along(df)) { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + } + + coef_matrix <- switch(method, + "cosine" = .cosineIndex(df, length, cloneCall, coef_matrix), + "jaccard" = .jaccardIndex(df, length, cloneCall, coef_matrix), + "morisita" = .morisitaIndex(df, length, cloneCall, coef_matrix), + "overlap" = .overlapIndex(ddf, length, cloneCall, coef_matrix), + "raw" = .rawIndex(df, length, cloneCall, coef_matrix), + "Invalid method specified") + coef_matrix$names <- rownames(coef_matrix) + if (exportTable == TRUE) { return(coef_matrix) } + coef_matrix <- suppressMessages(melt(coef_matrix))[,-1] + col <- colorblind_vector(7) + coef_matrix$variable <- factor(coef_matrix$variable, levels = values) + coef_matrix$names <- factor(coef_matrix$names, levels = values) + plot <- ggplot(coef_matrix, aes(x=names, y=variable, fill=value)) + + geom_tile() + labs(fill = method) + + geom_text(aes(label = round(value, digits = 3))) + + scale_fill_gradientn(colors = rev(colorblind_vector(5)), na.value = "white") + + theme_classic() + theme(axis.title = element_blank()) + return(plot) +} + +#Calculate the Morisita Index for Overlap Analysis +#' @author Massimo Andreatta, Nick Borcherding +.morisitaIndex <- function(df, length, cloneCall, coef_matrix) { + for (i in seq_along(length)){ + df.i <- df[[i]] + df.i <- data.frame(table(df.i[,cloneCall])) + colnames(df.i) <- c(cloneCall, 'Count') + df.i[,2] <- as.numeric(df.i[,2]) + for (j in seq_along(length)){ + if (i >= j){ next } + else { df.j <- df[[j]] + df.j <- data.frame(table(df.j[,cloneCall])) + colnames(df.j) <- c(cloneCall, 'Count') + df.j[,2] <- as.numeric(df.j[,2]) + merged <- merge(df.i, df.j, by = cloneCall, all = TRUE) + merged[is.na(merged)] <- 0 + X <- sum(merged[,2]) + Y <- sum(merged[,3]) + sum.df.i <- sum(df.i[,2]^2) + sum.df.j <- sum(df.j[,2]^2) + + num <- 2 * sum(merged[, 2] * merged[, 3]) + den <- ((sum.df.i / (X^2) + sum.df.j / (Y^2)) * X * Y) + + coef.i.j <- num/den + coef_matrix[i,j] <- coef.i.j + } + } + } + return(coef_matrix) +} + + +#Calculate the Jaccard Similarity Index for Overlap Analysis +.jaccardIndex <- function(df, length, cloneCall, coef_matrix) { + for (i in seq_along(length)){ + df.i <- df[[i]] + df.i <- df.i[,c("barcode",cloneCall)] + df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + for (j in seq_along(length)){ + if (i >= j){ next } + + df.j <- df[[j]] + df.j <- df.j[,c("barcode",cloneCall)] + df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] + overlap <- length(intersect(df.i_unique[,cloneCall], + df.j_unique[,cloneCall])) + coef_matrix[i,j] <- + overlap/(sum(length(df.i_unique[,cloneCall]), + length(df.j_unique[,cloneCall]))-overlap) + } + } + return(coef_matrix) +} + +.rawIndex <- function(df, length, cloneCall, coef_matrix) { + for (i in seq_along(length)){ + df.i <- df[[i]] + df.i <- df.i[,c("barcode",cloneCall)] + df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + for (j in seq_along(length)){ + if (i >= j){ next } + df.j <- df[[j]] + df.j <- df.j[,c("barcode",cloneCall)] + df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] + overlap <- length(intersect(df.i_unique[,cloneCall], + df.j_unique[,cloneCall])) + coef_matrix[i,j] <- overlap + } + } + return(coef_matrix) +} + + +#Calculate the Overlap Coefficient for Overlap Analysis +#' @author Nick Bormann, Nick Borcherding +.overlapIndex <- function(df, length, cloneCall, coef_matrix) { + for (i in seq_along(length)){ + df.i <- df[[i]] + df.i <- df.i[,c("barcode",cloneCall)] + df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + for (j in seq_along(length)){ + if (i >= j){ next } + else { df.j <- df[[j]] + df.j <- df.j[,c("barcode",cloneCall)] + df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] + overlap <- length(intersect(df.i_unique[,cloneCall], + df.j_unique[,cloneCall])) + coef_matrix[i,j] <- + overlap/min(length(df.i_unique[,cloneCall]), + length(df.j_unique[,cloneCall])) } } } + return(coef_matrix) +} + +.cosineIndex <- function(df, length, cloneCall, coef_matrix) { + for (i in seq_along(length)){ + df.i <- df[[i]] + df.i <- df.i[,c("barcode",cloneCall)] + df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + for (j in seq_along(length)){ + if (i >= j){ next } + else { + df.j <- df[[j]] + df.j <- df.j[,c("barcode",cloneCall)] + df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] + all_species <- unique(c(df.j_unique[,cloneCall], df.j_unique[,cloneCall])) + vector_location1 <- as.integer(all_species %in% df.i_unique[,cloneCall]) + vector_location2 <- as.integer(all_species %in% df.j_unique[,cloneCall]) + + coef_matrix[i,j] <- + sum(vector_location1 * vector_location2) / (sqrt(sum(vector_location1^2)) * sqrt(sum(vector_location2^2))) + } + } + } + coef_matrix <- as.matrix(coef_matrix) + coef_matrix[is.nan(coef_matrix)] <- 0 + return(coef_matrix) +} + diff --git a/R/overlap.R b/R/overlap.R deleted file mode 100644 index e8b04e70..00000000 --- a/R/overlap.R +++ /dev/null @@ -1,75 +0,0 @@ -#' Examining the clonal overlap between groups or samples -#' -#' This functions allows for the calculation and visualizations of the -#' overlap coefficient, morisita, or jaccard index for clonotypes -#' using the product of combineTCR(), combineBCR() or expression2list(). -#' The overlap coefficient is calculated using the intersection of clonotypes -#' divided by the length of the smallest component. -#' If a matrix output for the data is preferred, set exportTable = TRUE. -#' -#' @examples -#' #Making combined contig data -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' -#' clonalOverlap(combined, cloneCall = "gene", method = "overlap") -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param method The method to calculate the overlap, either the "overlap" -#' coefficient, "morisita", "jaccard" indices, or "raw" for the base numbers. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param exportTable Returns the data frame used for forming the graph -#' @importFrom stringr str_sort -#' @importFrom reshape2 melt -#' @export -#' @return ggplot of the clonotypic overlap between elements of a list -clonalOverlap <- function(df, cloneCall = "strict", - method = c("overlap", "morisita", "jaccard", "raw"), - chain = "both", - split.by = NULL, - exportTable = FALSE){ - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - df <- df[order(names(df))] - values <- str_sort(as.character(unique(names(df))), numeric = TRUE) - df <- df[quiet(dput(values))] - num_samples <- length(df[]) - names_samples <- names(df) - coef_matrix <- data.frame(matrix(NA, num_samples, num_samples)) - colnames(coef_matrix) <- names_samples - rownames(coef_matrix) <- names_samples - length <- seq_len(num_samples) - if (chain != "both") { - for (i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } - if (method == "overlap") { - coef_matrix <- overlapIndex(df, length, cloneCall, coef_matrix) - } else if (method == "morisita") { - coef_matrix <- morisitaIndex(df, length, cloneCall, coef_matrix) - } else if (method == "jaccard") { - coef_matrix <- jaccardIndex(df, length, cloneCall, coef_matrix) - } else if (method == "raw") { - coef_matrix <- rawIndex(df, length, cloneCall, coef_matrix) - } - coef_matrix$names <- rownames(coef_matrix) - if (exportTable == TRUE) { return(coef_matrix) } - coef_matrix <- suppressMessages(melt(coef_matrix))[,-1] - col <- colorblind_vector(7) - coef_matrix$variable <- factor(coef_matrix$variable, levels = values) - coef_matrix$names <- factor(coef_matrix$names, levels = values) - plot <- ggplot(coef_matrix, aes(x=names, y=variable, fill=value)) + - geom_tile() + labs(fill = method) + - geom_text(aes(label = round(value, digits = 3))) + - scale_fill_gradientn(colors = rev(colorblind_vector(5)), na.value = "white") + - theme_classic() + theme(axis.title = element_blank()) - return(plot) } diff --git a/R/utils.R b/R/utils.R index c412f014..df30f92d 100644 --- a/R/utils.R +++ b/R/utils.R @@ -228,100 +228,6 @@ parseContigs <- function(df, i, names, cloneCall) { return(data1) } -#Calculate the Morisita Index for Overlap Analysis -#' @author Massimo Andreatta, Nick Borcherding -morisitaIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- data.frame(table(df.i[,cloneCall])) - colnames(df.i) <- c(cloneCall, 'Count') - df.i[,2] <- as.numeric(df.i[,2]) - for (j in seq_along(length)){ - if (i >= j){ next } - else { df.j <- df[[j]] - df.j <- data.frame(table(df.j[,cloneCall])) - colnames(df.j) <- c(cloneCall, 'Count') - df.j[,2] <- as.numeric(df.j[,2]) - merged <- merge(df.i, df.j, by = cloneCall, all = TRUE) - merged[is.na(merged)] <- 0 - X <- sum(merged[,2]) - Y <- sum(merged[,3]) - sum.df.i <- sum(df.i[,2]^2) - sum.df.j <- sum(df.j[,2]^2) - - num <- 2 * sum(merged[, 2] * merged[, 3]) - den <- ((sum.df.i / (X^2) + sum.df.j / (Y^2)) * X * Y) - - coef.i.j <- num/den - coef_matrix[i,j] <- coef.i.j - } - } - } - return(coef_matrix) -} - - -#Calculate the Jaccard Similarity Index for Overlap Analysis -jaccardIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] - for (j in seq_along(length)){ - if (i >= j){ next } - - df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) - coef_matrix[i,j] <- - overlap/(sum(length(df.i_unique[,cloneCall]), - length(df.j_unique[,cloneCall]))-overlap) - } - } - return(coef_matrix) -} - -rawIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] - for (j in seq_along(length)){ - if (i >= j){ next } - df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) - coef_matrix[i,j] <- overlap - } - } - return(coef_matrix) -} - - -#Calculate the Overlap Coefficient for Overlap Analysis -#' @author Nick Bormann, Nick Borcherding -overlapIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] - for (j in seq_along(length)){ - if (i >= j){ next } - else { df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) - coef_matrix[i,j] <- - overlap/min(length(df.i_unique[,cloneCall]), - length(df.j_unique[,cloneCall])) } } } - return(coef_matrix) -} - # This suppressing outputs for using dput() quiet <- function(x) { sink(tempfile()) From b06184d311a1cdc86e44c8584522e6cf079eacde Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 6 Sep 2023 15:35:10 -0500 Subject: [PATCH 026/142] update color handling No use any color built into grdevices hcl.pal() function --- R/abundanceContig.R | 12 +++++++----- R/checkContig.R | 6 ++++-- R/clonalDiversity.R | 4 +++- R/clonalHomeostasis.R | 6 ++++-- R/clonalNetwork.R | 4 +++- R/clonalOverlap.R | 7 ++++--- R/clonalProportion.R | 6 ++++-- R/lengthContig.R | 6 ++++-- R/occupiedscRepertoire.R | 6 ++++-- R/percentAA.R | 6 ++++-- R/quantContig.R | 8 +++++--- R/scatterClonotypes.R | 24 +++++++++++++----------- R/utils.R | 10 ++++++---- R/vizGenes.R | 6 ++++-- vignettes/vignette.Rmd | 8 +++----- 15 files changed, 72 insertions(+), 47 deletions(-) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index 7ad618e9..0ba1cfa2 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -29,6 +29,7 @@ #' relative distributions. #' @param exportTable Returns the data frame used for forming the graph #' to the visualization. +#' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom ggplot2 ggplot #' @export #' @return ggplot of the total or relative abundance of clonotypes @@ -40,7 +41,8 @@ abundanceContig <- function(df, group.by = NULL, split.by = NULL, order = TRUE, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { df <- list.input.return(df,split.by) Con.df <- NULL xlab <- "Abundance" @@ -63,13 +65,13 @@ abundanceContig <- function(df, plot <- ggplot(Con.df, aes(x=Abundance, fill=Con.df[,group.by])) + geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, color="black", bw=0.5) + - scale_fill_manual(values = colorblind_vector(col)) + + scale_fill_manual(values = .colorizer(palette,col)) + labs(fill = fill) } else { ylab <- "Number of Clonotypes" plot <- ggplot(Con.df, aes(x=Abundance, group.by = values, color = Con.df[,group.by])) + geom_line(stat="count") + - scale_color_manual(values = colorblind_vector(col)) + + scale_color_manual(values = .colorizer(palette,col)) + labs(color = fill)} } else { for (i in seq_along(df)) { @@ -88,13 +90,13 @@ abundanceContig <- function(df, plot <- ggplot(Con.df, aes(Abundance, fill=values)) + geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, color="black", bw=0.5) + - scale_fill_manual(values = colorblind_vector(col)) + + scale_fill_manual(values = .colorizer(col)) + labs(fill = fill) } else { ylab <- "Number of Clonotypes" plot <- ggplot(Con.df, aes(x=Abundance, group = values, color = values)) + geom_line(stat="count") + - scale_color_manual(values = colorblind_vector(col)) + + scale_color_manual(values = .colorizer(col)) + labs(color = fill)} } if (exportTable == TRUE) { return(Con.df) } plot <- plot + scale_x_log10() + ylab(ylab) + xlab(xlab) + diff --git a/R/checkContig.R b/R/checkContig.R index 8c966a84..fdef1a07 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -22,6 +22,7 @@ #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() #' @import dplyr #' @importFrom stringr str_split #' @export @@ -32,7 +33,8 @@ checkContig <- function(df, examine = "gene", group.by = NULL, split.by = NULL, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { x.axis <- c(split.by, group.by) #condensing for ploting. x.axis <- x.axis[1] @@ -116,7 +118,7 @@ checkContig <- function(df, labs(color = x.axis) + theme_classic() + ylab("Percent of NAs") + - scale_color_manual(values = colorblind_vector(col)) + + scale_color_manual(values = .colorizer(palette, col)) + theme(axis.title.x = element_blank()) return(plot) } diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 02d6dd03..c17dd18b 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -28,6 +28,7 @@ #' "norm.entropy", "gini.simpson", "chao1", "ACE" #' @param exportTable Exports a table of the data into the global environment #' in addition to the visualization +#' @param palette Colors to use in visualization - input any hcl.pals() #' @param n.boots number of bootstraps to downsample in order to get mean diversity #' @param return.boots export boot strapped values calculated - #' will automatically exportTable = TRUE @@ -47,6 +48,7 @@ clonalDiversity <- function(df, split.by = NULL, metrics = c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE"), exportTable = FALSE, + palette = "inferno", n.boots = 100, return.boots = FALSE, skip.boots = FALSE) { @@ -132,7 +134,7 @@ clonalDiversity <- function(df, geom_jitter(aes(color = melt[,group.by]), size = 3) + labs(color="Group") + ylab("Index Score") + - scale_color_manual(values = colorblind_vector(length(unique(melt[,group.by])))) + + scale_color_manual(values = .colorizer(palette,length(unique(melt[,group.by])))) + facet_wrap(~variable, scales = "free", ncol = 5) + theme_classic() + theme(axis.title.x = element_blank()) diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index a8dd50c8..1ff4fb9a 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -26,6 +26,7 @@ #' to group the new list. NULL will return clusters. #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization +#' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' @importFrom stringr str_split #' @importFrom reshape2 melt @@ -39,7 +40,8 @@ clonalHomeostasis <- function(df, chain = "both", group.by = NULL, split.by = NULL, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { cloneTypes <- c(None = 0, cloneTypes) df <- list.input.return(df, split.by = split.by) cloneCall <- theCall(cloneCall) @@ -74,7 +76,7 @@ clonalHomeostasis <- function(df, geom_bar(stat = "identity", position="fill", color = "black", lwd= 0.25) + scale_fill_manual(name = "Clonotype Group", - values = rev(colorblind_vector(col))) + + values = rev(.colorizer(palette,col))) + xlab("Samples") + ylab("Relative Abundance") + theme_classic() diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index a077be79..fd30ddb3 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -40,6 +40,7 @@ #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization +#' @param palette Colors to use in visualization - input any hcl.pals() #' @param exportClones Exports a table of clones that are shared #' across multiple identity groups and ordered by the total number #' of clone copies. @@ -63,6 +64,7 @@ clonalNetwork <- function(sc, cloneCall = "strict", chain = "both", exportTable = FALSE, + palette = "inferno", exportClones = FALSE) { cloneCall <- theCall(cloneCall) @@ -205,7 +207,7 @@ clonalNetwork <- function(sc, ylab(paste0(reduction, "_2")) + xlab(paste0(reduction, "_1")) + guides(color = "none") + - scale_edge_colour_gradientn(colors = rev(colorblind_vector(13)), trans = "log10") + + scale_edge_colour_gradientn(colors = rev(.colorizer(palette,13)), trans = "log10") + labs(size = "Unique Clones", edge_color = "Relative Proportion of \nClones of Starting Node") + theme( panel.background = element_blank(), diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 79ece535..59159ce7 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -27,6 +27,7 @@ #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom stringr str_sort #' @importFrom reshape2 melt #' @export @@ -36,7 +37,8 @@ clonalOverlap <- function(df, method = c("overlap", "morisita", "jaccard", "raw"), chain = "both", split.by = NULL, - exportTable = FALSE){ + exportTable = FALSE, + palette = "inferno"){ df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) @@ -65,13 +67,12 @@ clonalOverlap <- function(df, coef_matrix$names <- rownames(coef_matrix) if (exportTable == TRUE) { return(coef_matrix) } coef_matrix <- suppressMessages(melt(coef_matrix))[,-1] - col <- colorblind_vector(7) coef_matrix$variable <- factor(coef_matrix$variable, levels = values) coef_matrix$names <- factor(coef_matrix$names, levels = values) plot <- ggplot(coef_matrix, aes(x=names, y=variable, fill=value)) + geom_tile() + labs(fill = method) + geom_text(aes(label = round(value, digits = 3))) + - scale_fill_gradientn(colors = rev(colorblind_vector(5)), na.value = "white") + + scale_fill_gradientn(colors = rev(.colorizer(palette, 7)), na.value = "white") + theme_classic() + theme(axis.title = element_blank()) return(plot) } diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 30082a9a..b7f9b065 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -26,6 +26,7 @@ #' to group the new list. NULL will return clusters. #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization +#' @param palette Colors to use in visualization - input any hcl.pals() #' #' @import ggplot2 #' @importFrom stringr str_sort @@ -40,7 +41,8 @@ clonalProportion <- function(df, chain = "both", group.by = NULL, split.by = NULL, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { Con.df <- NULL df <- list.input.return(df, split.by = split.by) cloneCall <- theCall(cloneCall) @@ -76,7 +78,7 @@ clonalProportion <- function(df, geom_bar(stat = "identity", position="fill", color = "black", lwd= 0.25) + scale_fill_manual(name = "Clonal Indices", - values = colorblind_vector(col)) + + values = .colorizer(palette,col)) + xlab("Samples") + ylab("Occupied Repertoire Space") + theme_classic() diff --git a/R/lengthContig.R b/R/lengthContig.R index 7a3ccae9..5ba60afc 100644 --- a/R/lengthContig.R +++ b/R/lengthContig.R @@ -30,6 +30,7 @@ #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom stringr str_split #' @importFrom ggplot2 ggplot #' @export @@ -42,7 +43,8 @@ lengthContig <- function(df, split.by = NULL, order = TRUE, scale = FALSE, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { df <- list.input.return(df, split.by) if(cloneCall == "nt") { cloneCall <- "CTnt" ylab <- "CDR3 (NT)" @@ -95,7 +97,7 @@ lengthContig <- function(df, color="black", lwd=0.25) + scale_x_discrete(breaks = round(seq(min(Con.df$length), max(Con.df$length), by = 5),10))} } - plot <- plot + scale_fill_manual(values = colorblind_vector(col)) + + plot <- plot + scale_fill_manual(values = .colorizer(palette,col)) + labs(fill = fill) + ylab(paste(yplus, ylab, sep="")) + xlab(xlab) + theme_classic() if (exportTable == TRUE) { return(Con.df) } diff --git a/R/occupiedscRepertoire.R b/R/occupiedscRepertoire.R index 89ab5336..93190e5a 100644 --- a/R/occupiedscRepertoire.R +++ b/R/occupiedscRepertoire.R @@ -32,6 +32,7 @@ #' @param na.include Visualize NA values or not. #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization +#' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom dplyr %>% group_by mutate #' @importFrom reshape2 melt #' @import ggplot2 @@ -44,7 +45,8 @@ occupiedscRepertoire <- function(sc, facet.by = NULL, proportion = FALSE, na.include = FALSE, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { checkSingleObject(sc) meta <- grabMeta(sc) meta <- melt(table(meta[!is.na(meta$Frequency), @@ -78,7 +80,7 @@ occupiedscRepertoire <- function(sc, } plot <- plot + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + - scale_fill_manual(values = c(colorblind_vector(col))) + + scale_fill_manual(values = c(.colorizer(palette,col))) + ylab(lab) + theme_classic() + theme(axis.title.x = element_blank()) diff --git a/R/percentAA.R b/R/percentAA.R index 6eed348a..d9fa218e 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -18,6 +18,7 @@ #' to group the new list. NULL will return clusters. #' @param aa.length The maximum length of the cdr3 amino acid sequence #' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' @export #' @return ggplot of stacked bar graphs of amino acid proportions @@ -26,7 +27,8 @@ percentAA <- function(df, group.by = NULL, split.by = NULL, aa.length = 30, - exportTable = FALSE) + exportTable = FALSE, + palette = "inferno") df <- list.input.return(df, split.by) df <- checkBlanks(df, "CTaa") for(i in seq_along(df)) { @@ -58,7 +60,7 @@ percentAA <- function(df, plot <- ggplot(mat_melt, aes(x=as.factor(variable), y = value, fill=AA)) + geom_bar(stat = "identity", position="fill", lwd= 0.25, color = "black") + scale_fill_manual(name = "Amino Acid", - values = rev(colorblind_vector(21))) + + values = rev(.colorizer(palette,21))) + xlab("Amino Acid Residues") + ylab("Relative Percent") + theme_classic() + diff --git a/R/quantContig.R b/R/quantContig.R index 44055811..37e755b4 100644 --- a/R/quantContig.R +++ b/R/quantContig.R @@ -25,7 +25,8 @@ #' to group the new list. NULL will return clusters. #' @param order Maintain the order of the list when plotting #' @param scale Converts the graphs into percentage of unique clonotypes. -#' @param exportTable Returns the data frame used for forming the graph +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' @export #' @return ggplot of the total or relative unique clonotypes @@ -36,7 +37,8 @@ quantContig <- function(df, group.by = NULL, split.by = NULL, order = TRUE, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { if (length(group.by) > 1) { stop("Only one item in the group.by variable can be listed.") @@ -105,7 +107,7 @@ quantContig <- function(df, position = "dodge", width=.5) + labs(fill = labs) + stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ theme_classic() + xlab("Samples") + ylab(ylab) + - scale_fill_manual(values = colorblind_vector(col)) + scale_fill_manual(values = .colorizer(palette, col)) # if it is a single run, remove x axis labels if sample name missing if ((length(df) == 1) && identical(names(df), NA_character_)) { diff --git a/R/scatterClonotypes.R b/R/scatterClonotypes.R index f85445b3..9ba9466d 100644 --- a/R/scatterClonotypes.R +++ b/R/scatterClonotypes.R @@ -27,6 +27,7 @@ #' to group the new list. NULL will return clusters. #' @param graph graph either proportion or raw clonotype count #' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() #' @param seed the integer seed to set for the random variation of point coords. #' #' @import ggplot2 @@ -34,16 +35,17 @@ #' @export #' @return ggplot of the relative clonotype numbers -scatterClonotype <- function( - df, cloneCall ="strict", - x.axis = NULL, y.axis = NULL, - chain = "both", - dot.size = "total", - split.by = NULL, - graph = "proportion", - exportTable = FALSE, - seed = NULL -) { +scatterClonotype <- function(df, + cloneCall ="strict", + x.axis = NULL, + y.axis = NULL, + chain = "both", + dot.size = "total", + split.by = NULL, + graph = "proportion", + exportTable = FALSE, + palette = "inferno", + seed = NULL) { if (!is.null(seed)) {set.seed(seed)} df <- list.input.return(df, split.by) @@ -99,7 +101,7 @@ scatterClonotype <- function( if (exportTable == TRUE) { return(combined.df) } plot <- ggplot(combined.df, aes(x=x, y = y, color = class)) + theme_classic() + - scale_color_manual(values = colorblind_vector(length(unique(combined.df$class)))) + + scale_color_manual(values = .colorizer(palette,length(unique(combined.df$class)))) + xlab(x.axis) + ylab(y.axis) + labs(size = "Total n") if (graph == "proportion") { plot <- plot + geom_abline(slope = 1, intercept = 0, alpha = 0.4, lty=2) + diff --git a/R/utils.R b/R/utils.R index df30f92d..287328c2 100644 --- a/R/utils.R +++ b/R/utils.R @@ -20,10 +20,12 @@ off.the.chain <- function(dat, chain, cloneCall) { return(dat) } -#This is the basic color palette for the package -colorblind_vector <- colorRampPalette(rev(c("#0D0887FF", "#47039FFF", "#7301A8FF", "#9C179EFF", - "#BD3786FF", "#D8576BFF","#ED7953FF","#FA9E3BFF", "#FDC926FF", "#F0F921FF"))) - +.colorizer <- function(palette = "inferno", + n= NULL) { + + colors <- hcl.colors(n=n, palette = palette, fixup = TRUE) + return(colors) +} diff --git a/R/vizGenes.R b/R/vizGenes.R index c8282e93..f33341f5 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -29,6 +29,7 @@ #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' @importFrom stringr str_split #' @importFrom stats sd @@ -45,7 +46,8 @@ vizGenes <- function(df, scale = TRUE, group.by = NULL, split.by = NULL, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { df <- list.input.return(df, split.by = split.by) if(!is.null(group.by)) { df <- groupList(df, group.by) @@ -120,7 +122,7 @@ vizGenes <- function(df, vjust = 0.5, hjust=1, size=rel(0.5)), axis.title.y = element_blank(), axis.text.y = element_text(size=rel(0.5))) + - scale_fill_gradientn(colors = rev(colorblind_vector(5))) + scale_fill_gradientn(colors = rev(.colorizer(palette,5))) } if (exportTable == TRUE) { return(df) } return(plot) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index c60d5b81..9c5a391b 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -562,12 +562,10 @@ seurat <- combineExpression(list.receptors, We first want to look at the distribution of peripheral versus tumor T cells. We can use the same color scheme as the rest of the scRepertoire package by calling the object **colorblind_vector** using the following hex codes. ```{r} -colorblind_vector <- colorRampPalette(rev(c("#0D0887FF", "#47039FFF", - "#7301A8FF", "#9C179EFF", "#BD3786FF", "#D8576BFF", - "#ED7953FF","#FA9E3BFF", "#FDC926FF", "#F0F921FF"))) +colorblind_vector <- hcl.pal("inferno", 7) DimPlot(scRep_example, group.by = "Type") + NoLegend() + - scale_color_manual(values=colorblind_vector(2)) + scale_color_manual(values=colorblind_vector[c(1,7)]) DimPlot(scRep_example, group.by = "cloneType") ``` @@ -671,7 +669,7 @@ alluvialClonotypes(scRep_example, cloneCall = "aa", y.axes = c("Patient", "ident", "Type"), color = "CAVNGGSQGNLIF_CSAEREDTDTQYF") + - scale_fill_manual(values = c("grey", colorblind_vector(5)[5])) + scale_fill_manual(values = c("grey", colorblind_vector[7])) alluvialClonotypes(scRep_example, From d16eedce5bcc82fd65a87a5058c8f78d952d0412 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 7 Sep 2023 05:22:24 -0500 Subject: [PATCH 027/142] polishing manual and vignette --- DESCRIPTION | 2 +- NAMESPACE | 3 +- R/clonalDiversity.R | 2 +- R/clonalOverlap.R | 65 +++++++++-------- R/percentAA.R | 4 +- R/scatterClonotypes.R | 8 +- man/StartracDiversity.Rd | 11 +-- man/abundanceContig.Rd | 13 ++-- man/alluvialClonotypes.Rd | 11 +-- man/checkContig.Rd | 11 ++- man/clonalDiversity.Rd | 26 ++++--- man/clonalHomeostasis.Rd | 11 ++- man/clonalNetwork.Rd | 14 ++-- man/clonalOverlap.Rd | 16 ++-- man/clonalOverlay.Rd | 19 +++-- man/clonalProportion.Rd | 11 ++- man/clonesizeDistribution.Rd | 8 +- man/clonotypeBias.Rd | 4 +- man/clusterTCR.Rd | 5 +- man/combineExpression.Rd | 11 +-- man/combineTCR.Rd | 6 +- man/compareClonotypes.Rd | 8 +- man/createHTOContigList.Rd | 2 +- man/expression2List.Rd | 11 +-- man/getCirclize.Rd | 11 +-- man/highlightClonotypes.Rd | 13 ++-- man/lengthContig.Rd | 13 ++-- ...ini_contig_list.Rd => mini_contig_list.Rd} | 8 +- man/occupiedscRepertoire.Rd | 16 ++-- man/percentAA.Rd | 47 ++++++++++++ man/quantContig.Rd | 15 ++-- man/scRepertoire-package.Rd | 7 +- man/scatterClonotype.Rd | 13 ++-- man/screp_example.Rd | 4 +- man/vizGenes.Rd | 13 ++-- vignettes/vignette.Rmd | 73 +++++++++++-------- 36 files changed, 317 insertions(+), 198 deletions(-) rename man/{combined_mini_contig_list.Rd => mini_contig_list.Rd} (71%) create mode 100644 man/percentAA.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 034cd354..43d3e1a3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -3,7 +3,7 @@ Title: A toolkit for single-cell immune receptor profiling Version: 2.0.0 Authors@R: c( person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"), - person(given = "Qile", family = "Yang", role = c("aut", "cre"), email = "qile0317@gmail.com")) + person(given = "Qile", family = "Yang", role = c("aut"), email = "qile0317@gmail.com")) Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. License: GPL (>= 2) Encoding: UTF-8 diff --git a/NAMESPACE b/NAMESPACE index bfd24fb5..e4c44a4d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -25,6 +25,7 @@ export(highlightClonotypes) export(lengthContig) export(loadContigs) export(occupiedscRepertoire) +export(percentAA) export(quantContig) export(scatterClonotype) export(stripBarcode) @@ -96,6 +97,4 @@ importFrom(tidygraph,activate) importFrom(tidygraph,as_tbl_graph) importFrom(utils,combn) importFrom(utils,head) -importFrom(vegan,diversity) -importFrom(vegan,estimateR) useDynLib(scRepertoire, .registration = TRUE) diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index c17dd18b..7d730617 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -135,7 +135,7 @@ clonalDiversity <- function(df, labs(color="Group") + ylab("Index Score") + scale_color_manual(values = .colorizer(palette,length(unique(melt[,group.by])))) + - facet_wrap(~variable, scales = "free", ncol = 5) + + facet_wrap(~variable, scales = "free", ncol = length(metrics)) + theme_classic() + theme(axis.title.x = element_blank()) if (x.axis == "x.axis") { diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 59159ce7..dc4c9996 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -34,7 +34,7 @@ #' @return ggplot of the clonotypic overlap between elements of a list clonalOverlap <- function(df, cloneCall = "strict", - method = c("overlap", "morisita", "jaccard", "raw"), + method = NULL, chain = "both", split.by = NULL, exportTable = FALSE, @@ -61,12 +61,13 @@ clonalOverlap <- function(df, "cosine" = .cosineIndex(df, length, cloneCall, coef_matrix), "jaccard" = .jaccardIndex(df, length, cloneCall, coef_matrix), "morisita" = .morisitaIndex(df, length, cloneCall, coef_matrix), - "overlap" = .overlapIndex(ddf, length, cloneCall, coef_matrix), + "overlap" = .overlapIndex(df, length, cloneCall, coef_matrix), "raw" = .rawIndex(df, length, cloneCall, coef_matrix), "Invalid method specified") + coef_matrix <- as.data.frame(coef_matrix) coef_matrix$names <- rownames(coef_matrix) if (exportTable == TRUE) { return(coef_matrix) } - coef_matrix <- suppressMessages(melt(coef_matrix))[,-1] + coef_matrix <- suppressMessages(melt(coef_matrix)) coef_matrix$variable <- factor(coef_matrix$variable, levels = values) coef_matrix$names <- factor(coef_matrix$names, levels = values) plot <- ggplot(coef_matrix, aes(x=names, y=variable, fill=value)) + @@ -114,19 +115,19 @@ clonalOverlap <- function(df, .jaccardIndex <- function(df, length, cloneCall, coef_matrix) { for (i in seq_along(length)){ df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + df.i <- df.i[,cloneCall] + df.i_unique <- unique(df.i) for (j in seq_along(length)){ if (i >= j){ next } df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) + df.j <- df.j[,cloneCall] + df.j_unique <- unique(df.j) + overlap <- length(intersect(df.i_unique, + df.j_unique)) coef_matrix[i,j] <- - overlap/(sum(length(df.i_unique[,cloneCall]), - length(df.j_unique[,cloneCall]))-overlap) + overlap/(sum(length(df.i_unique), + length(df.j_unique))-overlap) } } return(coef_matrix) @@ -135,15 +136,15 @@ clonalOverlap <- function(df, .rawIndex <- function(df, length, cloneCall, coef_matrix) { for (i in seq_along(length)){ df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + df.i <- df.i[,cloneCall] + df.i_unique <- unique(df.i) for (j in seq_along(length)){ if (i >= j){ next } df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) + df.j <- df.j[,cloneCall] + df.j_unique <- unique(df.j) + overlap <- length(intersect(df.i_unique, + df.j_unique)) coef_matrix[i,j] <- overlap } } @@ -156,35 +157,35 @@ clonalOverlap <- function(df, .overlapIndex <- function(df, length, cloneCall, coef_matrix) { for (i in seq_along(length)){ df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + df.i <- df.i[,c(cloneCall)] + df.i_unique <- unique(df.i) for (j in seq_along(length)){ if (i >= j){ next } else { df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - overlap <- length(intersect(df.i_unique[,cloneCall], - df.j_unique[,cloneCall])) + df.j <- df.j[,c(cloneCall)] + df.j_unique <- unique(df.j) + overlap <- length(intersect(df.i_unique, + df.j_unique)) coef_matrix[i,j] <- - overlap/min(length(df.i_unique[,cloneCall]), - length(df.j_unique[,cloneCall])) } } } + overlap/min(length(df.i_unique), + length(df.j_unique)) } } } return(coef_matrix) } .cosineIndex <- function(df, length, cloneCall, coef_matrix) { for (i in seq_along(length)){ df.i <- df[[i]] - df.i <- df.i[,c("barcode",cloneCall)] - df.i_unique <- df.i[!duplicated(df.i[,cloneCall]),] + df.i <- df.i[,cloneCall] + df.i_unique <- unique(df.i) for (j in seq_along(length)){ if (i >= j){ next } else { df.j <- df[[j]] - df.j <- df.j[,c("barcode",cloneCall)] - df.j_unique <- df.j[!duplicated(df.j[,cloneCall]),] - all_species <- unique(c(df.j_unique[,cloneCall], df.j_unique[,cloneCall])) - vector_location1 <- as.integer(all_species %in% df.i_unique[,cloneCall]) - vector_location2 <- as.integer(all_species %in% df.j_unique[,cloneCall]) + df.j <- df.j[,cloneCall] + df.j_unique <- unique(df.j) + all_species <- unique(c(df.j_unique, df.j_unique)) + vector_location1 <- as.integer(all_species %in% df.i_unique) + vector_location2 <- as.integer(all_species %in% df.j_unique) coef_matrix[i,j] <- sum(vector_location1 * vector_location2) / (sqrt(sum(vector_location1^2)) * sqrt(sum(vector_location2^2))) diff --git a/R/percentAA.R b/R/percentAA.R index d9fa218e..287bcaca 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -26,9 +26,9 @@ percentAA <- function(df, chain = "TRB", group.by = NULL, split.by = NULL, - aa.length = 30, + aa.length = 20, exportTable = FALSE, - palette = "inferno") + palette = "inferno") { df <- list.input.return(df, split.by) df <- checkBlanks(df, "CTaa") for(i in seq_along(df)) { diff --git a/R/scatterClonotypes.R b/R/scatterClonotypes.R index 9ba9466d..dd7a12cb 100644 --- a/R/scatterClonotypes.R +++ b/R/scatterClonotypes.R @@ -99,9 +99,9 @@ scatterClonotype <- function(df, size <- combined.df[,dot.size] } else { size <- combined.df[,"sum"] } if (exportTable == TRUE) { return(combined.df) } - plot <- ggplot(combined.df, aes(x=x, y = y, color = class)) + + plot <- ggplot(combined.df, aes(x=x, y = y, fill = class)) + theme_classic() + - scale_color_manual(values = .colorizer(palette,length(unique(combined.df$class)))) + + scale_fill_manual(values = .colorizer(palette,length(unique(combined.df$class)))) + xlab(x.axis) + ylab(y.axis) + labs(size = "Total n") if (graph == "proportion") { plot <- plot + geom_abline(slope = 1, intercept = 0, alpha = 0.4, lty=2) + @@ -109,6 +109,6 @@ scatterClonotype <- function(df, } else if (graph == "count") { plot <- plot + ylim(0, max(x,y)) + xlim(0, max(x,y)) } - plot <- plot + geom_jitter(aes(size = size)) + plot <- plot + geom_jitter(aes(size = size,), shape = 21, color = "black", stroke = 0.25) return(plot) -} \ No newline at end of file +} diff --git a/man/StartracDiversity.Rd b/man/StartracDiversity.Rd index bd99ff0e..0c1e66e9 100644 --- a/man/StartracDiversity.Rd +++ b/man/StartracDiversity.Rd @@ -44,15 +44,16 @@ read and cite the linked manuscript. \examples{ \dontrun{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -screp_example <- combineExpression(combined, screp_example) +scRep_example <- get(data("scRep_example ")) +scRep_example <- combineExpression(combined, scRep_example ) #Using occupiedscRepertoire() -StartracDiversity(screp_example, type = "Type", sample = "Patient", by = "overall") +StartracDiversity(scRep_example , type = "Type", sample = "Patient", by = "overall") } } diff --git a/man/abundanceContig.Rd b/man/abundanceContig.Rd index 7041f31e..63d0dc35 100644 --- a/man/abundanceContig.Rd +++ b/man/abundanceContig.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/abundanceContig.R \name{abundanceContig} \alias{abundanceContig} \title{Demonstrate the relative abundance of clonotypes by group or sample.} @@ -12,7 +12,8 @@ abundanceContig( group.by = NULL, split.by = NULL, order = TRUE, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -37,6 +38,8 @@ to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph to the visualization.} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the total or relative abundance of clonotypes @@ -53,9 +56,9 @@ preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) abundanceContig(combined, cloneCall = "gene", scale = FALSE) } diff --git a/man/alluvialClonotypes.Rd b/man/alluvialClonotypes.Rd index 21907a9c..3cf15957 100644 --- a/man/alluvialClonotypes.Rd +++ b/man/alluvialClonotypes.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/seuratFunctions.R +% Please edit documentation in R/alluvialClonotypes.R \name{alluvialClonotypes} \alias{alluvialClonotypes} \title{Exploring interaction of clonotypes by seurat or SCE dynamics} @@ -49,12 +49,13 @@ the end of the graph. } \examples{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) +scRep_example <- get(data("scRep_example")) +sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) sce <- Seurat::as.SingleCellExperiment(sce) #Using combineExpresion() diff --git a/man/checkContig.Rd b/man/checkContig.Rd index 61e3d081..0e2454d3 100644 --- a/man/checkContig.Rd +++ b/man/checkContig.Rd @@ -10,7 +10,8 @@ checkContig( examine = "gene", group.by = NULL, split.by = NULL, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -28,6 +29,8 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the percentage of NA values @@ -41,9 +44,9 @@ and percentage of NAs are preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) checkContig(combined, chain = "TRA", examine = "chain", group.by = "sample") } diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index a434a1a7..5d9c6441 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/diversity.R +% Please edit documentation in R/clonalDiversity.R \name{clonalDiversity} \alias{clonalDiversity} \title{Examine the clonal diversity of samples} @@ -11,7 +11,9 @@ clonalDiversity( group.by = NULL, x.axis = NULL, split.by = NULL, + metrics = c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE"), exportTable = FALSE, + palette = "inferno", n.boots = 100, return.boots = FALSE, skip.boots = FALSE @@ -34,9 +36,14 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{split.by}{If using a single-cell object, the column header to group the new list. NULL will return clusters.} +\item{metrics}{The indices to use in diversity calculations - "shannon", "inv.simpson", +"norm.entropy", "gini.simpson", "chao1", "ACE"} + \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} +\item{palette}{Colors to use in visualization - input any hcl.pals()} + \item{n.boots}{number of bootstraps to downsample in order to get mean diversity} \item{return.boots}{export boot strapped values calculated - @@ -49,18 +56,17 @@ ggplot of the diversity of clonotype sequences across list } \description{ This function calculates traditional measures of diversity - Shannon, -inverse Simpson, Chao1 index, abundance-based coverage estimators -(ACE), and 1-Pielou's measure of species evenness by sample or group. -The function automatically down samples the -diversity metrics using 100 boot straps The group parameter can be -used to condense the individual samples. If a matrix output for -the data is preferred, set exportTable = TRUE. +inverse Simpson, normalized entropy, Gini-Simpson, Chao1 index, and +abundance-based coverage estimators (ACE) measure of species evenness by sample or group. +The function automatically down samples the diversity metrics using +100 boot straps The group parameter can be used to condense the individual +samples. If a matrix output for the data is preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) clonalDiversity(combined, cloneCall = "gene") } diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index 899b8a77..b10e81f4 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -12,7 +12,8 @@ clonalHomeostasis( chain = "both", group.by = NULL, split.by = NULL, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -34,6 +35,8 @@ to group the new list. NULL will return clusters.} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the space occupied by the specific proportion of clonotypes @@ -48,9 +51,9 @@ output for the data is preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) clonalHomeostasis(combined, cloneCall = "gene") } diff --git a/man/clonalNetwork.Rd b/man/clonalNetwork.Rd index 34e6546a..646c781c 100644 --- a/man/clonalNetwork.Rd +++ b/man/clonalNetwork.Rd @@ -15,6 +15,7 @@ clonalNetwork( cloneCall = "strict", chain = "both", exportTable = FALSE, + palette = "inferno", exportClones = FALSE ) } @@ -45,6 +46,8 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} +\item{palette}{Colors to use in visualization - input any hcl.pals()} + \item{exportClones}{Exports a table of clones that are shared across multiple identity groups and ordered by the total number of clone copies.} @@ -61,17 +64,18 @@ plot. \examples{ \dontrun{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) +scRep_example <- get(data("scRep_example")) #Using combineExpresion() -screp_example <- combineExpression(combined, screp_example) +scRep_example <- combineExpression(combined, scRep_example) #Using clonalNetwork() -clonalNetwork(screp_example, reduction = "umap", +clonalNetwork(scRep_example, reduction = "umap", identity = "cluster") } diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 3706e489..866f00ad 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/overlap.R +% Please edit documentation in R/clonalOverlap.R \name{clonalOverlap} \alias{clonalOverlap} \title{Examining the clonal overlap between groups or samples} @@ -10,7 +10,8 @@ clonalOverlap( method = c("overlap", "morisita", "jaccard", "raw"), chain = "both", split.by = NULL, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -21,7 +22,8 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{method}{The method to calculate the overlap, either the "overlap" -coefficient, "morisita", "jaccard" indices, or "raw" for the base numbers.} +coefficient, "morisita", "jaccard" indices, "cosine" similarity or "raw" +for the base numbers.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} @@ -30,6 +32,8 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the clonotypic overlap between elements of a list @@ -44,9 +48,9 @@ If a matrix output for the data is preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) clonalOverlap(combined, cloneCall = "gene", method = "overlap") diff --git a/man/clonalOverlay.Rd b/man/clonalOverlay.Rd index 213f0648..7cb0509d 100644 --- a/man/clonalOverlay.Rd +++ b/man/clonalOverlay.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/seuratFunctions.R +% Please edit documentation in R/clonalOverlay.R \name{clonalOverlay} \alias{clonalOverlay} \title{Visualize distribution of clonal frequency overlaid on dimensional reduction plots} @@ -35,18 +35,21 @@ Credit to the idea goes to Drs Andreatta and Carmona and their work with } \examples{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) - +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) +scRep_example <- get(data("scRep_example")) #Using combineExpresion() -sce <- combineExpression(combined, sce) +scRep_example <- combineExpression(combined, + scRep_example) #Using clonalOverlay() -clonalOverlay(sce, reduction = "umap", freq.cutpoint = 0.3, bins = 5) +clonalOverlay(scRep_example, + reduction = "umap", + freq.cutpoint = 0.3, + bins = 5) } \author{ diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index c4dbec3b..aec03963 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -11,7 +11,8 @@ clonalProportion( chain = "both", group.by = NULL, split.by = NULL, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -33,6 +34,8 @@ to group the new list. NULL will return clusters.} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the space occupied by the specific rank of clonotypes @@ -47,9 +50,9 @@ preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) clonalProportion(combined, cloneCall = "gene") } diff --git a/man/clonesizeDistribution.Rd b/man/clonesizeDistribution.Rd index 2e747dd9..8eee4f84 100644 --- a/man/clonesizeDistribution.Rd +++ b/man/clonesizeDistribution.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/clonesizeDistribution.R \name{clonesizeDistribution} \alias{clonesizeDistribution} \title{Hierarchical clustering of clonotypes on clonotype size and @@ -50,9 +50,9 @@ Please read and cite PMID: 30485278 if using the function for analyses. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) clonesizeDistribution(combined, cloneCall = "strict", method="ward.D2") } diff --git a/man/clonotypeBias.Rd b/man/clonotypeBias.Rd index fcc04e0f..d4200b34 100644 --- a/man/clonotypeBias.Rd +++ b/man/clonotypeBias.Rd @@ -52,8 +52,8 @@ combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), rep(c("P", "T"), 3)) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) +scRep_example <- get(data("scRep_example")) +sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #Using combineExpresion() sce <- combineExpression(combined, sce) diff --git a/man/clusterTCR.Rd b/man/clusterTCR.Rd index b4355368..79248016 100644 --- a/man/clusterTCR.Rd +++ b/man/clusterTCR.Rd @@ -38,8 +38,9 @@ also underlies the combineBCR() function and therefore not needed for B cells. This may take some time to calculate the distances and cluster. } \examples{ -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) sub_combined <- clusterTCR(combined[[2]], chain = "TRA", sequence = "aa") diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index d43f56e3..4c758b8c 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/seuratFunctions.R +% Please edit documentation in R/combineExpression.R \name{combineExpression} \alias{combineExpression} \title{Adding clonotype information to a Seurat or SCE object} @@ -64,12 +64,13 @@ process. } \examples{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) +scRep_example <- get(data("scRep_example")) +sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) sce <- Seurat::as.SingleCellExperiment(sce) #Using combineExpresion() diff --git a/man/combineTCR.Rd b/man/combineTCR.Rd index f6ca727a..110cc075 100644 --- a/man/combineTCR.Rd +++ b/man/combineTCR.Rd @@ -46,8 +46,8 @@ just a single data.frame, the function will add the consolidated information columns after the existing raw clonotype data } \examples{ -combineTCR(contig_list, - samples = rep(c("PX", "PY", "PZ"), each=2), - ID = rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) } diff --git a/man/compareClonotypes.Rd b/man/compareClonotypes.Rd index 72663677..5d6f567d 100644 --- a/man/compareClonotypes.Rd +++ b/man/compareClonotypes.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/compareClonotypes.R \name{compareClonotypes} \alias{compareClonotypes} \title{Demonstrate the difference in clonal proportion between clonotypes} @@ -52,9 +52,9 @@ proportion to be visualized. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) compareClonotypes(combined, numbers = 10, samples = c("PX_P", "PX_T"), cloneCall="aa") diff --git a/man/createHTOContigList.Rd b/man/createHTOContigList.Rd index 5b0b3328..9648a29c 100644 --- a/man/createHTOContigList.Rd +++ b/man/createHTOContigList.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/seuratFunctions.R +% Please edit documentation in R/createHTOContigList.R \name{createHTOContigList} \alias{createHTOContigList} \title{Generate a contig list from a multiplexed experiment} diff --git a/man/expression2List.Rd b/man/expression2List.Rd index c56cb23d..5551bcb8 100644 --- a/man/expression2List.Rd +++ b/man/expression2List.Rd @@ -24,14 +24,15 @@ parameters or SCE or Seurat objects must appear in the meta data. } \examples{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -screp_example <- combineExpression(combined, screp_example) +scRep_example <- get(data("scRep_example")) +scRep_example <- combineExpression(combined, scRep_example) #Using expression2List -newList <- expression2List(screp_example, split.by = "seurat_clusters") +newList <- expression2List(scRep_example, split.by = "seurat_clusters") } diff --git a/man/getCirclize.Rd b/man/getCirclize.Rd index d366e8cc..e6721484 100644 --- a/man/getCirclize.Rd +++ b/man/getCirclize.Rd @@ -32,15 +32,16 @@ clonotype unqiue and shared across the multiple group.by variable. } \examples{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -screp_example <- combineExpression(combined, screp_example) +scRep_example <- get(data("scRep_example")) +scRep_example <- combineExpression(combined, scRep_example) #Getting data frame output for Circilize -circles <- getCirclize(screp_example, group.by = "seurat_clusters") +circles <- getCirclize(scRep_example, group.by = "seurat_clusters") } diff --git a/man/highlightClonotypes.Rd b/man/highlightClonotypes.Rd index 2b363f65..09f10de1 100644 --- a/man/highlightClonotypes.Rd +++ b/man/highlightClonotypes.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/seuratFunctions.R +% Please edit documentation in R/highlightClonotypes.R \name{highlightClonotypes} \alias{highlightClonotypes} \title{Highlighting specific clonotypes in Seurat} @@ -28,17 +28,18 @@ reduction in seurat object. } \examples{ #' #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) +scRep_example <- get(data("scRep_example ")) #Using combineExpresion() -screp_example <- combineExpression(combined, screp_example ) +scRep_example <- combineExpression(combined, scRep_example ) #Using highlightClonotype() -screp_example <- highlightClonotypes(screp_example, cloneCall= "aa", +scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF")) } diff --git a/man/lengthContig.Rd b/man/lengthContig.Rd index 3e7074ae..a77963b3 100644 --- a/man/lengthContig.Rd +++ b/man/lengthContig.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/lengthContig.R \name{lengthContig} \alias{lengthContig} \title{Demonstrate the distribution of lengths filtered contigs.} @@ -12,7 +12,8 @@ lengthContig( split.by = NULL, order = TRUE, scale = FALSE, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -36,6 +37,8 @@ to group the new list. NULL will return clusters.} relative distributions.} \item{exportTable}{Returns the data frame used for forming the graph.} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the discrete or relative length distributions of @@ -54,9 +57,9 @@ exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) lengthContig(combined, cloneCall="aa", chain = "both") } diff --git a/man/combined_mini_contig_list.Rd b/man/mini_contig_list.Rd similarity index 71% rename from man/combined_mini_contig_list.Rd rename to man/mini_contig_list.Rd index 4f01dff9..ad1bb2c8 100644 --- a/man/combined_mini_contig_list.Rd +++ b/man/mini_contig_list.Rd @@ -1,19 +1,19 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} -\name{combined_mini_contig_list} -\alias{combined_mini_contig_list} +\name{mini_contig_list} +\alias{mini_contig_list} \title{Processed subset of `contig_list`} \format{ An R `list` of `data.frame` objects } \usage{ -data("combined_mini_contig_list") +data("mini_contig_list") } \description{ A list of 6 dataframes of T cell contigs outputted from the `filtered_contig_annotation` files, but subsetted to about 92 valid T cells -which correspond to the same barcodes found in `screp_example` +which correspond to the same barcodes found in `scRep_example` } \seealso{ \code{\link{contig_list}} diff --git a/man/occupiedscRepertoire.Rd b/man/occupiedscRepertoire.Rd index 5974d7ec..dd1b3e6b 100644 --- a/man/occupiedscRepertoire.Rd +++ b/man/occupiedscRepertoire.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/seuratFunctions.R +% Please edit documentation in R/occupiedscRepertoire.R \name{occupiedscRepertoire} \alias{occupiedscRepertoire} \title{Visualize the number of single cells with clonotype frequencies by cluster} @@ -11,7 +11,8 @@ occupiedscRepertoire( facet.by = NULL, proportion = FALSE, na.include = FALSE, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -29,6 +30,8 @@ occupiedscRepertoire( \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ Stacked bar plot of counts of cells by clonotype frequency group @@ -43,12 +46,13 @@ the idea goes to Drs. Carmona and Andreatta and their work with } \examples{ #Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -screp_example <- get(data("screp_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(screp_example)) +scRep_example <- get(data("scRep_example")) +sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #Using combineExpresion() sce <- combineExpression(combined, sce) diff --git a/man/percentAA.Rd b/man/percentAA.Rd new file mode 100644 index 00000000..91ddf10d --- /dev/null +++ b/man/percentAA.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/percentAA.R +\name{percentAA} +\alias{percentAA} +\title{Examining the relative amino acid composition by position} +\usage{ +percentAA( + df, + chain = "TRB", + group.by = NULL, + split.by = NULL, + aa.length = 20, + exportTable = FALSE, + palette = "inferno" +) +} +\arguments{ +\item{df}{The product of combineTCR(), combineBCR(), +expression2List(), or combineExpression().} + +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} + +\item{group.by}{The column header used for grouping.} + +\item{split.by}{If using a single-cell object, the column header +to group the new list. NULL will return clusters.} + +\item{aa.length}{The maximum length of the cdr3 amino acid sequence} + +\item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} +} +\value{ +ggplot of stacked bar graphs of amino acid proportions +} +\description{ +This function the proportion of amino acids along the residues +of the cdr3 amino acid sequence. +} +\examples{ +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +percentAA(combined, chain = "TRB", aa.length = 20) +} diff --git a/man/quantContig.Rd b/man/quantContig.Rd index f8650a7a..d8694c48 100644 --- a/man/quantContig.Rd +++ b/man/quantContig.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/quantContig.R \name{quantContig} \alias{quantContig} \title{Quantify the unique clonotypes in the filtered contigs.} @@ -12,7 +12,8 @@ quantContig( group.by = NULL, split.by = NULL, order = TRUE, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -34,7 +35,9 @@ to group the new list. NULL will return clusters.} \item{order}{Maintain the order of the list when plotting} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the total or relative unique clonotypes @@ -49,9 +52,9 @@ for the data is preferred, set exportTable = TRUE. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) quantContig(combined, cloneCall="strict", scale = TRUE) } diff --git a/man/scRepertoire-package.Rd b/man/scRepertoire-package.Rd index 8e5ddd84..01804a81 100644 --- a/man/scRepertoire-package.Rd +++ b/man/scRepertoire-package.Rd @@ -6,10 +6,15 @@ \alias{scRepertoire-package} \title{scRepertoire: A toolkit for single-cell immune receptor profiling} \description{ -scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. It also allows for general analysis of single-cell clonotype information without the use of expression information. The package functions as a wrapper for Startrac and powerTCR R packages. +scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. } \author{ \strong{Maintainer}: Nick Borcherding \email{ncborch@gmail.com} +Authors: +\itemize{ + \item Qile Yang \email{qile0317@gmail.com} +} + } \keyword{internal} diff --git a/man/scatterClonotype.Rd b/man/scatterClonotype.Rd index 4208a509..88a61de6 100644 --- a/man/scatterClonotype.Rd +++ b/man/scatterClonotype.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/scatterClonotypes.R \name{scatterClonotype} \alias{scatterClonotype} \title{Scatter plot comparing the expansion of two samples} @@ -14,6 +14,7 @@ scatterClonotype( split.by = NULL, graph = "proportion", exportTable = FALSE, + palette = "inferno", seed = NULL ) } @@ -41,6 +42,8 @@ to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph.} +\item{palette}{Colors to use in visualization - input any hcl.pals()} + \item{seed}{the integer seed to set for the random variation of point coords.} } \value{ @@ -54,10 +57,10 @@ by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 202 } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) -scatterClonotype(combined, x.axis = "PY_P", y.axis = "PY_T", +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +scatterClonotype(combined, x.axis = "P17B", y.axis = "P17L", graph = "proportion") } diff --git a/man/screp_example.Rd b/man/screp_example.Rd index f2a058a0..2e3d9d99 100644 --- a/man/screp_example.Rd +++ b/man/screp_example.Rd @@ -1,8 +1,8 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/data.R \docType{data} -\name{screp_example} -\alias{screp_example} +\name{scRep_example} +\alias{scRep_example} \title{A seurat object of 100 single T cells derived from 3 clear cell renal carcinoma patients.} \description{ diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index 24f359c4..220a9e6a 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/viz.R +% Please edit documentation in R/vizGenes.R \name{vizGenes} \alias{vizGenes} \title{Visualizing the distribution of gene usage} @@ -14,7 +14,8 @@ vizGenes( scale = TRUE, group.by = NULL, split.by = NULL, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -42,6 +43,8 @@ respective repertoire size} to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph.} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot bar diagram or heatmap of gene usage @@ -55,9 +58,9 @@ present regardless of the chain parameter. } \examples{ #Making combined contig data -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 9c5a391b..faf57fe2 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -153,11 +153,11 @@ The output of `combineTCR()` will be a list of contig data frames that will be r What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the batches in which the samples were processed and sequenced. ```{r} -example <- addVariable(combined, +combined <- addVariable(combined, name = "batch", - variables = c("b1", "b1", "b2", "b2", "b2", "b2")) + variables = c("b1", "b1", "b1", "b2", "b2", "b2", "b2", "b2")) -example[[1]][1:5,ncol(example[[1]])] # This is showing the first 5 values of the new column added +combined[[1]][1:5,ncol(combined[[1]])] # This is showing the first 5 values of the new column added ``` ## subsetContig @@ -211,12 +211,12 @@ quantContig_output <- quantContig(combined, quantContig_output ``` -The other option here is to be able to define the visualization by data classes. Here we used the *combineTCR()* to define the **ID** variable as part of the naming structure. We can the **group.by** to specifically use a column in the data set to organize the visualization. +The other option here is to be able to define the visualization by data classes. Here we used the *combineTCR()* to define the **batch** variable as part of the naming structure. We can the **group.by** to specifically use a column in the data set to organize the visualization. ```{r} quantContig(combined, cloneCall="gene", - group.by = "ID", + group.by = "batch", scale = TRUE) ``` @@ -235,7 +235,7 @@ Depending on the experiment or sequencing run, a number of NAs can appear in eit checkContig(combined, chain = "TRA", examine = "gene", - group.by = "ID") + group.by = "batch") checkContig(combined, chain = "TRB", @@ -251,10 +251,6 @@ We can also examine the relative distribution of clonotypes by abundance. Here ` abundanceContig(combined, cloneCall = "gene", scale = FALSE) -abundanceContig(combined, - cloneCall = "gene", - group.by = "ID", - scale = FALSE) ``` As you can see the peripheral blood sample derived from patient 1 is a relative extreme outlier. Another method to examine the relative abundance is to look at the density by using the **scale** call in the function. @@ -279,7 +275,8 @@ lengthContig(combined, chain = "both") lengthContig(combined, cloneCall="aa", - chain = "TRA") + chain = "TRA", + scale = TRUE) ``` ## compareClonotypes @@ -321,7 +318,7 @@ compareClonotypes(combined, ## vizGenes -Last of the basic analysis visualizations is the relative usage of genes of the TCR or BCR, using `vizGenes()`. +A visualizations of the relative usage of genes of the TCR or BCR, using `vizGenes()`. **gene** + "V" @@ -385,7 +382,7 @@ We can also use `vizGenes()` to look at the usage of genes in a single chain. So ```{r} #Peripheral Blood -vizGenes(combined[c(1,3,5)], +vizGenes(combined[c(1,3,5,7)], gene = "V", chain = "TRB", y.axis = "J", @@ -393,8 +390,8 @@ vizGenes(combined[c(1,3,5)], scale = TRUE, order = "variance") -#Tumor Infiltrating -vizGenes(combined[c(2,4,6)], +#Lung +vizGenes(combined[c(2,4,6,8)], gene = "V", chain = "TRB", y.axis = "J", @@ -403,6 +400,17 @@ vizGenes(combined[c(2,4,6)], order = "variance") ``` +## percentAA + +Quantify the proportion of amino acids along the cdr3 sequence with `percentAA()`. By default the function will pad the sequences with NAs up to the maximum of **aa.length**. Sequences longer than **aa.length** will be removed before visualization. + +```{r} +percentAA(combined, + chain = "TRB", + aa.length = 20) +``` + + *** # More Advanced Clonal Analysis @@ -471,7 +479,6 @@ Diversity can also be measured for samples or by other variables. Diversity is c clonalDiversity(combined, cloneCall = "gene", group.by = "sample", - x.axis = "ID", n.boots = 100) ``` @@ -491,7 +498,8 @@ A number of users requested a visualization from the work of [Wu, et al 2020, PM * "count" for the total count of clonotypes by sample ```{r} -scatterClonotype(combined, cloneCall ="gene", +scatterClonotype(combined, + cloneCall ="gene", x.axis = "P18B", y.axis = "P18L", dot.size = "total", @@ -562,12 +570,19 @@ seurat <- combineExpression(list.receptors, We first want to look at the distribution of peripheral versus tumor T cells. We can use the same color scheme as the rest of the scRepertoire package by calling the object **colorblind_vector** using the following hex codes. ```{r} -colorblind_vector <- hcl.pal("inferno", 7) +colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) + +#Adding blood or lung designation as Type +scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) + +#Adding patient information +scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) DimPlot(scRep_example, group.by = "Type") + NoLegend() + - scale_color_manual(values=colorblind_vector[c(1,7)]) + scale_color_manual(values=colorblind_vector[c(1,6)]) -DimPlot(scRep_example, group.by = "cloneType") +DimPlot(scRep_example, group.by = "cloneType") + + scale_color_manual(values=colorblind_vector[c(2,4,6)]) ``` ## clonalOverlay @@ -637,8 +652,8 @@ We can also look at the clonotypes by calling specific sequences in the `highlig ```{r} scRep_example <- highlightClonotypes(scRep_example, cloneCall= "aa", - sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF", - "NA_CATSATLRVVAEKLFF")) + sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", + "NA_CASSVRRERANTGELFF")) Seurat::DimPlot(scRep_example, group.by = "highlight") ``` @@ -668,14 +683,14 @@ After the metadata has been modified, we can look at clonotypes across multiple alluvialClonotypes(scRep_example, cloneCall = "aa", y.axes = c("Patient", "ident", "Type"), - color = "CAVNGGSQGNLIF_CSAEREDTDTQYF") + + color = "CVVSDNTGGFKTIF_CASSVRRERANTGELFF") + scale_fill_manual(values = c("grey", colorblind_vector[7])) alluvialClonotypes(scRep_example, cloneCall = "gene", y.axes = c("Patient", "ident", "Type"), - color = "seurat_clusters") + color = "ident") ``` ## getCirclize @@ -687,7 +702,7 @@ library(circlize) library(scales) circles <- getCirclize(scRep_example, - group.by = "seurat_clusters") + group.by = "ident") #Just assigning the normal colors to each cluster grid.cols <- hue_pal()(length(unique(scRep_example$seurat_clusters))) @@ -697,10 +712,10 @@ names(grid.cols) <- unique(scRep_example$seurat_clusters) chordDiagram(circles, self.link = 1, grid.col = grid.cols) ``` -This can also be used if we wanted to explore just the tumor-specific T cells by just subsetting the single-cell object. +This can also be used if we wanted to explore just the lung-specific T cells by just subsetting the single-cell object. ```{r} -subset <- subset(scRep_example, Type == "T") +subset <- subset(scRep_example, Type == "L") circles <- getCirclize(subset, group.by = "ident") @@ -769,7 +784,7 @@ A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/3582969 ```{r} clonotypeBias(scRep_example, cloneCall = "aa", - split.by = "Type", + split.by = "Patient", group.by = "seurat_clusters", n.boots = 20, min.expand =1) @@ -805,7 +820,7 @@ clonalProportion(combined3, cloneCall = "nt") ## 4. clonalOverlap ```{r} -clonalOverlap(combined2, cloneCall="aa", method="overlap") +clonalOverlap(combined2, cloneCall="aa", method="cosine") clonalOverlap(combined3, cloneCall="aa", method="overlap") ``` From be94b48356f0aaf14c9c647ae04ca3ac4b73de54 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 7 Sep 2023 05:46:48 -0500 Subject: [PATCH 028/142] running check() --- NAMESPACE | 1 + R/abundanceContig.R | 7 +++--- R/clonalDiversity.R | 2 +- R/global.R | 1 + R/processing.R | 10 ++++---- R/utils.R | 2 +- man/addVariable.Rd | 10 ++++---- man/clonalOverlap.Rd | 2 +- vignettes/vignette.Rmd | 54 ++++++++++++++++++++++++++---------------- 9 files changed, 55 insertions(+), 34 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index e4c44a4d..c3276f88 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -63,6 +63,7 @@ importFrom(ggraph,geom_node_point) importFrom(ggraph,ggraph) importFrom(ggraph,guide_edge_colourbar) importFrom(ggraph,scale_edge_colour_gradientn) +importFrom(grDevices,hcl.colors) importFrom(igraph,V) importFrom(igraph,`V<-`) importFrom(igraph,components) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index 0ba1cfa2..8aff3405 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -79,7 +79,8 @@ abundanceContig <- function(df, df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) } data1 <- parseContigs(df, i, names, cloneCall) - Con.df<- rbind.data.frame(Con.df, data1) } + Con.df<- rbind.data.frame(Con.df, data1) + } Con.df <- data.frame(Con.df) if(order) { Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) @@ -90,13 +91,13 @@ abundanceContig <- function(df, plot <- ggplot(Con.df, aes(Abundance, fill=values)) + geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, color="black", bw=0.5) + - scale_fill_manual(values = .colorizer(col)) + + scale_fill_manual(values = .colorizer(palette,col)) + labs(fill = fill) } else { ylab <- "Number of Clonotypes" plot <- ggplot(Con.df, aes(x=Abundance, group = values, color = values)) + geom_line(stat="count") + - scale_color_manual(values = .colorizer(col)) + + scale_color_manual(values = .colorizer(palette,col)) + labs(color = fill)} } if (exportTable == TRUE) { return(Con.df) } plot <- plot + scale_x_log10() + ylab(ylab) + xlab(xlab) + diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 7d730617..9ca7599b 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -85,7 +85,7 @@ clonalDiversity <- function(df, } else { for (j in seq(seq_len(n.boots))) { x <- sample_n(data, min) - sample <- diversityCall(x) + sample <- .diversityCall(x) mat_a <- rbind(mat_a, sample) } mat_a[is.na(mat_a)] <- 0 diff --git a/R/global.R b/R/global.R index 72cfd1c0..2738bbc5 100644 --- a/R/global.R +++ b/R/global.R @@ -73,5 +73,6 @@ utils::globalVariables ("total.cells") utils::globalVariables ("clone") utils::globalVariables ("v.gene") + utils::globalVariables ("AA") invisible () } diff --git a/R/processing.R b/R/processing.R index ab59c17e..316bbbf5 100644 --- a/R/processing.R +++ b/R/processing.R @@ -32,10 +32,12 @@ stripBarcode <- function(contigs, column = 1, connector = "_", num_connects = 3) #' the combined object. #' #' @examples -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' combined <- addVariable(combined, name = "batch", variables = c(1,1,1,1,2,2)) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' combined <- addVariable(combined, +#' name = "Type", +#' variables = rep(c("B", "L"), 4)) #' #' @param df The product of combineTCR() combineBCR() or expression2List(). #' @param name The column header to add. diff --git a/R/utils.R b/R/utils.R index 287328c2..e14a2db3 100644 --- a/R/utils.R +++ b/R/utils.R @@ -19,7 +19,7 @@ off.the.chain <- function(dat, chain, cloneCall) { dat[,cloneCall] <- str_split(dat[,cloneCall], "_", simplify = TRUE)[,x] return(dat) } - +#' @importFrom grDevices hcl.colors .colorizer <- function(palette = "inferno", n= NULL) { diff --git a/man/addVariable.Rd b/man/addVariable.Rd index bb34090e..4069ecdb 100644 --- a/man/addVariable.Rd +++ b/man/addVariable.Rd @@ -24,9 +24,11 @@ The length of the variable paramater needs to match the length of the combined object. } \examples{ -x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) -combined <- addVariable(combined, name = "batch", variables = c(1,1,1,1,2,2)) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +combined <- addVariable(combined, + name = "Type", + variables = rep(c("B", "L"), 4)) } diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 866f00ad..4a587233 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -7,7 +7,7 @@ clonalOverlap( df, cloneCall = "strict", - method = c("overlap", "morisita", "jaccard", "raw"), + method = NULL, chain = "both", split.by = NULL, exportTable = FALSE, diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index faf57fe2..84643174 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -150,12 +150,12 @@ The output of `combineTCR()` will be a list of contig data frames that will be r ## addVariable -What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the batches in which the samples were processed and sequenced. +What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the Typees in which the samples were processed and sequenced. ```{r} combined <- addVariable(combined, - name = "batch", - variables = c("b1", "b1", "b1", "b2", "b2", "b2", "b2", "b2")) + name = "Type", + variables = rep(c("B", "L"), 4)) combined[[1]][1:5,ncol(combined[[1]])] # This is showing the first 5 values of the new column added ``` @@ -199,6 +199,13 @@ quantContig(combined, cloneCall="strict", chain = "both", scale = TRUE) + +#Adjust colors with the palette parameter +quantContig(combined, + cloneCall="strict", + chain = "both", + scale = TRUE, + palette = "Zissou 1") ``` Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph, instead of the visual output. @@ -211,12 +218,12 @@ quantContig_output <- quantContig(combined, quantContig_output ``` -The other option here is to be able to define the visualization by data classes. Here we used the *combineTCR()* to define the **batch** variable as part of the naming structure. We can the **group.by** to specifically use a column in the data set to organize the visualization. +Another option here is to be able to define the visualization by data classes. Here we used the *combineTCR()* to define the **Type** variable as part of the naming structure. We can the **group.by** to specifically use a column in the data set to organize the visualization. ```{r} quantContig(combined, cloneCall="gene", - group.by = "batch", + group.by = "Type", scale = TRUE) ``` @@ -235,7 +242,7 @@ Depending on the experiment or sequencing run, a number of NAs can appear in eit checkContig(combined, chain = "TRA", examine = "gene", - group.by = "batch") + group.by = "Type") checkContig(combined, chain = "TRB", @@ -257,7 +264,7 @@ As you can see the peripheral blood sample derived from patient 1 is a relative ```{r} abundanceContig(combined, - group.by = "ID", + group.by = "Type", scale = TRUE) ``` @@ -273,6 +280,7 @@ We can look at the length distribution of the CDR3 sequences by calling the `len lengthContig(combined, cloneCall="aa", chain = "both") + lengthContig(combined, cloneCall="aa", chain = "TRA", @@ -428,8 +436,11 @@ By examining the clonal space, we are effectively looking at the relative space * Hyperexpanded = 1 ```{r} -clonalHomeostasis(combined, cloneCall = "gene") -clonalHomeostasis(combined, cloneCall = "aa") +clonalHomeostasis(combined, + cloneCall = "gene") + +clonalHomeostasis(combined, + cloneCall = "aa") ``` @@ -448,8 +459,11 @@ The **split** represents ranking of clonotypes by copy or frequency of occurrenc * 100000 ```{r} -clonalProportion(combined, cloneCall = "gene") -clonalProportion(combined, cloneCall = "nt") +clonalProportion(combined, + cloneCall = "gene") + +clonalProportion(combined, + cloneCall = "nt") ``` @@ -535,19 +549,18 @@ We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 a In order to categorize the frequency, we have the variable **proportion** which if TRUE allows for the relative proportion or when FALSE will use absolute frequency to define clonotype groups **cloneTypes** acts as a bin to place labels. As a default, **cloneTypes** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below you can see an example of using total frequency as expansion assignments. ```{r} +sce <- combineExpression(combined, + sce, + cloneCall="gene", + group.by = "sample", + proportion = TRUE) + scRep_example <- combineExpression(combined, scRep_example, cloneCall="gene", group.by = "sample", proportion = FALSE, cloneTypes=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500)) - - -sce <- combineExpression(combined, - sce, - cloneCall="gene", - group.by = "sample", - proportion = TRUE) ``` ## Combining both TCR and BCR @@ -578,8 +591,9 @@ scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) #Adding patient information scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) -DimPlot(scRep_example, group.by = "Type") + NoLegend() + - scale_color_manual(values=colorblind_vector[c(1,6)]) +DimPlot(scRep_example, group.by = "Type") + + NoLegend() + + scale_color_manual(values=colorblind_vector[c(1,6)]) DimPlot(scRep_example, group.by = "cloneType") + scale_color_manual(values=colorblind_vector[c(2,4,6)]) From 98ca89d03b980b6c78c6f04edb7a798d6306f4db Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 8 Sep 2023 04:41:45 -0500 Subject: [PATCH 029/142] Adding percentGenes --- R/percentAA.R | 1 + R/percentGenes.R | 91 ++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 92 insertions(+) create mode 100644 R/percentGenes.R diff --git a/R/percentAA.R b/R/percentAA.R index 287bcaca..7a83f5fe 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -20,6 +20,7 @@ #' @param exportTable Returns the data frame used for forming the graph #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 +#' @importFrom reshape2 melt #' @export #' @return ggplot of stacked bar graphs of amino acid proportions percentAA <- function(df, diff --git a/R/percentGenes.R b/R/percentGenes.R new file mode 100644 index 00000000..6f3ad28e --- /dev/null +++ b/R/percentGenes.R @@ -0,0 +1,91 @@ +#' Examining the V or J gene usage across groupings +#' +#' This function the proportion V or J genes used by +#' grouping variables +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' percentGenes(combined, chain = "TRB", gene = "Vgene) + +#' @param df The product of combineTCR(), combineBCR(), +#' expression2List(), or combineExpression(). +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" +#' @param gene "V", "D" or "J" +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() +#' @import ggplot2 +#' @importFrom stringr str_split str_sort +#' @importFrom reshape2 melt +#' @export +#' @return ggplot of percentage of indicated genes as a heatmap +#' +percentGenes <- function(df, + chain = "TRB", + gene = "Vgene", + group.by = NULL, + split.by = NULL, + exportTable = FALSE, + palette = "inferno") { + + df <- list.input.return(df, split.by) + df <- checkBlanks(df, "CTgene") + for(i in seq_along(df)) { + df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") + } + + #Parsing gene input + if (gene %in% c("Vgene", "V", "v", "v.gene")) { + gene.loci <- paste0(chain, "V") + } else if(gene %in% c("Jgene", "j", "J", "j.gene")) { + gene.loci <- paste0(chain, "J") + } else if(gene %in% c("Dgene", "d", "D", "D.gene")) { + if(chain %in% c("TRB", "TRD", "IGH")) { + gene.loci <- paste0(chain, "D") + } else { + stop(paste0("There is not the D locus for ", gene)) + } + } + + #Getting indicated genes across list + gene_counts <- lapply(df, function(x) { + tmp <- unlist(str_split(x[,"CTgene"], ";")) + tmp <- str_split(tmp, "[.]", simplify = TRUE) + + tmp <- tmp[grep(gene.loci, tmp)] + }) + #Need total unique genes + gene.dictionary <- unique(unlist(gene_counts)) + + #Summarizing the gene usage across the list + summary <- lapply(gene_counts, function(x) { + percentages <- unlist(prop.table(table(x))) + genes.to.add <- gene.dictionary [which(gene.dictionary %!in% names(percentages))] + if(length(genes.to.add) >= 1) { + percentages.to.add <- rep(0, length(genes.to.add)) + names(percentages.to.add) <- genes.to.add + percentages <- c(percentages, percentages.to.add) + } + percentages[match(str_sort(names(percentages), numeric = TRUE), names(percentages))] + + + }) + summary <- do.call(rbind,summary) + if (exportTable == TRUE) { + return(summary) + } + #Melting matrix and Visualizing + mat_melt <- melt(summary) + plot <- ggplot(mat_melt, aes(y=Var1, x = Var2, fill=round(value*100,2))) + + geom_tile(lwd= 0.25, color = "black") + + scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + + theme_classic() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), + axis.title = element_blank()) + return(plot) +} From ee3ecdba0d7e5fe612dfa4311af785248ae2a13b Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 8 Sep 2023 05:03:13 -0500 Subject: [PATCH 030/142] Create percentVJ.R --- R/percentVJ.R | 84 +++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 84 insertions(+) create mode 100644 R/percentVJ.R diff --git a/R/percentVJ.R b/R/percentVJ.R new file mode 100644 index 00000000..8cacc390 --- /dev/null +++ b/R/percentVJ.R @@ -0,0 +1,84 @@ +#' Examining the V or J gene usage across groupings +#' +#' This function the proportion V or J genes used by +#' grouping variables +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' percentGenes(combined, chain = "TRB", gene = "Vgene) + +#' @param df The product of combineTCR(), combineBCR(), +#' expression2List(), or combineExpression(). +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" +#' @param gene "V", "D" or "J" +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() +#' @import ggplot2 +#' @importFrom stringr str_split str_sort +#' @importFrom reshape2 melt +#' @export +#' @return ggplot of percentage of indicated genes as a heatmap +#' +percentGenes <- function(df, + chain = "TRB", + group.by = NULL, + split.by = NULL, + exportTable = FALSE, + palette = "inferno") { + + df <- list.input.return(df, split.by) + df <- checkBlanks(df, "CTgene") + for(i in seq_along(df)) { + df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") + } + + if(chain %in% c("TRA", "TRG", "IGL")) { + positions <- c(1,2) + } else { + positions <- c(1,3) + } + + + #Getting indicated genes across list + gene_counts <- lapply(df, function(x) { + tmp <- unlist(str_split(x[,"CTgene"], ";")) + tmp <- str_split(tmp, "[.]", simplify = TRUE) + strings <- paste0(tmp[,positions[1]], ";", tmp[,positions[2]]) + strings <- strings[-which(strings == "NA;")] + }) + #Need total unique genes + gene.dictionary <- unique(unlist(gene_counts)) + + #Summarizing the gene usage across the list + summary <- lapply(gene_counts, function(x) { + percentages <- unlist(prop.table(table(x))) + genes.to.add <- gene.dictionary [which(gene.dictionary %!in% names(percentages))] + if(length(genes.to.add) >= 1) { + percentages.to.add <- rep(0, length(genes.to.add)) + names(percentages.to.add) <- genes.to.add + percentages <- c(percentages, percentages.to.add) + } + percentages <- percentages[match(str_sort(names(percentages), numeric = TRUE), names(percentages))] + + + }) + summary <- do.call(rbind,summary) + if (exportTable == TRUE) { + return(summary) + } + #Melting matrix and Visualizing + mat_melt <- melt(summary) + plot <- ggplot(mat_melt, aes(y=Var1, x = Var2, fill=round(value*100,2))) + + geom_tile(lwd= 0.25, color = "black") + + scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + + theme_classic() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), + axis.title = element_blank()) + return(plot) +} From d43d909571256005e43745bfdfda2077a18a7008 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 8 Sep 2023 07:50:11 -0500 Subject: [PATCH 031/142] Add visualizations to percentVJ --- R/percentVJ.R | 30 +++++++++++++++++++++++------- 1 file changed, 23 insertions(+), 7 deletions(-) diff --git a/R/percentVJ.R b/R/percentVJ.R index 8cacc390..9adee2b4 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -23,9 +23,9 @@ #' @importFrom stringr str_split str_sort #' @importFrom reshape2 melt #' @export -#' @return ggplot of percentage of indicated genes as a heatmap +#' @return ggplot of percentage of V and J gene pairings as a heatmap #' -percentGenes <- function(df, +percentVJ <- function(df, chain = "TRB", group.by = NULL, split.by = NULL, @@ -54,6 +54,9 @@ percentGenes <- function(df, }) #Need total unique genes gene.dictionary <- unique(unlist(gene_counts)) + coordinates <- strsplit(gene.dictionary, ";") + V_values <- unique(sapply(coordinates, function(coord) coord[1])) + J_values <- unique(sapply(coordinates, function(coord) coord[2])) #Summarizing the gene usage across the list summary <- lapply(gene_counts, function(x) { @@ -65,19 +68,32 @@ percentGenes <- function(df, percentages <- c(percentages, percentages.to.add) } percentages <- percentages[match(str_sort(names(percentages), numeric = TRUE), names(percentages))] - - }) - summary <- do.call(rbind,summary) if (exportTable == TRUE) { - return(summary) + summary.matrix <- do.call(rbind,summary) + return(summary.matrix) } + mat <- lapply(summary, function(x) { + + # Create an empty matrix + result_matrix <- matrix(0, nrow = length(V_values), ncol = length(unique(J_values))) + rownames(result_matrix) <- V_values + colnames(result_matrix) <- J_values + + for (i in seq_len(length(x))) { + coordinates <- unlist(strsplit(names(x)[i], ";")) + result_matrix[coordinates[1], coordinates[2]] <- x[i] + } + result_matrix + + }) #Melting matrix and Visualizing - mat_melt <- melt(summary) + mat_melt <- melt(mat) plot <- ggplot(mat_melt, aes(y=Var1, x = Var2, fill=round(value*100,2))) + geom_tile(lwd= 0.25, color = "black") + scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + theme_classic() + + facet_wrap(~L1) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), axis.title = element_blank()) return(plot) From fa375a8dd05f2bec7e1c30933a154b1debf617ac Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 8 Sep 2023 19:58:41 -0500 Subject: [PATCH 032/142] Add default color scheme to compareClonotypes() --- R/compareClonotypes.R | 23 ++++++++++++++++------- 1 file changed, 16 insertions(+), 7 deletions(-) diff --git a/R/compareClonotypes.R b/R/compareClonotypes.R index 60b15d11..cb540a51 100644 --- a/R/compareClonotypes.R +++ b/R/compareClonotypes.R @@ -11,8 +11,10 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' compareClonotypes(combined, numbers = 10, -#' samples = c("PX_P", "PX_T"), cloneCall="aa") +#' compareClonotypes(combined, +#' numbers = 5, +#' samples = c("PX_P", "PX_T"), +#' cloneCall="aa") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). #' @param cloneCall How to call the clonotype - VDJC gene (gene), @@ -27,6 +29,7 @@ #' to group the new list. NULL will return clusters. #' @param graph The type of graph produced, either "alluvial" or "area". #' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' #' @export @@ -40,7 +43,8 @@ compareClonotypes <- function(df, numbers = NULL, split.by = NULL, graph = "alluvial", - exportTable = FALSE){ + exportTable = FALSE, + palette = "inferno"){ df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) @@ -59,14 +63,17 @@ compareClonotypes <- function(df, Con.df <- rbind.data.frame(Con.df, tbl) } if (!is.null(samples)) { - Con.df <- Con.df[Con.df$Sample %in% samples,] } + Con.df <- Con.df[Con.df$Sample %in% samples,] + } if (!is.null(clonotypes)) { - Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] } + Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] + } if (!is.null(numbers)) { top <- Con.df %>% group_by(Con.df[,3]) %>% slice_max(n = numbers, order_by = Proportion, with_ties = FALSE) - Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] } + Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] + } if (nrow(Con.df) < length(unique(Con.df$Sample))) { stop("Reasses the filtering strategies here, there is not enough clonotypes to examine.") } @@ -81,6 +88,8 @@ compareClonotypes <- function(df, plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") } else if (graph == "area") { plot <- plot + - geom_area(aes(group = Clonotypes), color = "black") } + geom_area(aes(group = Clonotypes), color = "black") + } + plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"Clonotypes"])))) return(plot) } \ No newline at end of file From 9f179b7c03572bb656555b7e88fc886c2541224d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 8 Sep 2023 20:24:11 -0500 Subject: [PATCH 033/142] Update clonalOverlap.R Now automatically change text color by value and add a border around non NA values --- R/clonalOverlap.R | 21 ++++++++++++++++----- 1 file changed, 16 insertions(+), 5 deletions(-) diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index dc4c9996..23f4d034 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -66,15 +66,26 @@ clonalOverlap <- function(df, "Invalid method specified") coef_matrix <- as.data.frame(coef_matrix) coef_matrix$names <- rownames(coef_matrix) - if (exportTable == TRUE) { return(coef_matrix) } + if (exportTable == TRUE) { + return(coef_matrix) + } coef_matrix <- suppressMessages(melt(coef_matrix)) coef_matrix$variable <- factor(coef_matrix$variable, levels = values) coef_matrix$names <- factor(coef_matrix$names, levels = values) + + tertile_values <- quantile(na.omit(coef_matrix[,"value"]), probs = c(1/3,2/3)) + plot <- ggplot(coef_matrix, aes(x=names, y=variable, fill=value)) + - geom_tile() + labs(fill = method) + - geom_text(aes(label = round(value, digits = 3))) + - scale_fill_gradientn(colors = rev(.colorizer(palette, 7)), na.value = "white") + - theme_classic() + theme(axis.title = element_blank()) + geom_tile() + + geom_tile(data = coef_matrix[!is.na(coef_matrix[,"value"]),], fill = NA, lwd= 0.25, color = "black") + + labs(fill = method) + + geom_text(aes(label = round(value, digits = 3), + color = ifelse(value <= as.vector(tertile_values[1]), + "white", "black"))) + + scale_fill_gradientn(colors = .colorizer(palette, 7), na.value = "white") + + scale_color_identity() + + theme_classic() + + theme(axis.title = element_blank()) return(plot) } From 6b4dbefbecce68e83af50f2f3e721b478ae1c4fa Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 9 Sep 2023 13:38:15 -0500 Subject: [PATCH 034/142] updating manual --- DESCRIPTION | 5 +++-- NAMESPACE | 3 +++ R/alluvialClonotypes.R | 9 ++++---- R/clonalOverlap.R | 1 + R/clonalRarefaction.R | 6 +++++ R/percentGenes.R | 2 +- R/percentVJ.R | 3 +-- man/alluvialClonotypes.Rd | 9 ++++---- man/compareClonotypes.Rd | 11 ++++++--- man/percentGenes.Rd | 47 +++++++++++++++++++++++++++++++++++++++ man/percentVJ.Rd | 44 ++++++++++++++++++++++++++++++++++++ vignettes/vignette.Rmd | 15 +++++-------- 12 files changed, 128 insertions(+), 27 deletions(-) create mode 100644 R/clonalRarefaction.R create mode 100644 man/percentGenes.Rd create mode 100644 man/percentVJ.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 43d3e1a3..acb277d3 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -12,8 +12,7 @@ RoxygenNote: 7.2.3 biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Depends: ggplot2, - R (>= 4.0), - Seurat + R (>= 4.0) Imports: stringdist, dplyr, @@ -32,8 +31,10 @@ Imports: igraph, ggraph, tidygraph, + Seurat, SeuratObject, stats, + BiocManager, Rcpp Suggests: knitr, diff --git a/NAMESPACE b/NAMESPACE index c3276f88..7774de1d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -26,6 +26,8 @@ export(lengthContig) export(loadContigs) export(occupiedscRepertoire) export(percentAA) +export(percentGenes) +export(percentVJ) export(quantContig) export(scatterClonotype) export(stripBarcode) @@ -86,6 +88,7 @@ importFrom(reshape2,dcast) importFrom(reshape2,melt) importFrom(rlang,"%||%") importFrom(stats,na.omit) +importFrom(stats,quantile) importFrom(stats,sd) importFrom(stringdist,stringdist) importFrom(stringdist,stringdistmatrix) diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index 221a5434..eb6f8727 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -43,15 +43,14 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) -#' sce <- Seurat::as.SingleCellExperiment(sce) #' #' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) +#' scRep_example <- combineExpression(combined, scRep_example) +#' scRep_example$Patient <- substring(scRep_example$orig.ident, 1,3) #' #' #Using alluvialClonotypes() -#' alluvialClonotypes(sce, cloneCall = "gene", -#' y.axes = c("Patient", "ident"), color = "ident") +#' alluvialClonotypes(scRep_example, cloneCall = "gene", +#' y.axes = c("Patient", "ident"), color = "ident") #' #' @param sc The seurat or SCE object to visualize after combineExpression(). #' For SCE objects, the cluster variable must be in the meta data under diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 23f4d034..1e8a0884 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -30,6 +30,7 @@ #' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom stringr str_sort #' @importFrom reshape2 melt +#' @importFrom stats quantile #' @export #' @return ggplot of the clonotypic overlap between elements of a list clonalOverlap <- function(df, diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R new file mode 100644 index 00000000..3fe2a130 --- /dev/null +++ b/R/clonalRarefaction.R @@ -0,0 +1,6 @@ +clonalRarefaction <- function(cloneCall = "strict", + method = NULL, + chain = "both", + split.by = NULL) { + +} \ No newline at end of file diff --git a/R/percentGenes.R b/R/percentGenes.R index 6f3ad28e..be2b3fec 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -8,7 +8,7 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' percentGenes(combined, chain = "TRB", gene = "Vgene) +#' percentGenes(combined, chain = "TRB", gene = "Vgene") #' @param df The product of combineTCR(), combineBCR(), #' expression2List(), or combineExpression(). diff --git a/R/percentVJ.R b/R/percentVJ.R index 9adee2b4..90225213 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -8,12 +8,11 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' percentGenes(combined, chain = "TRB", gene = "Vgene) +#' percentVJ(combined, chain = "TRB") #' @param df The product of combineTCR(), combineBCR(), #' expression2List(), or combineExpression(). #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" -#' @param gene "V", "D" or "J" #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. diff --git a/man/alluvialClonotypes.Rd b/man/alluvialClonotypes.Rd index 3cf15957..30dd09c6 100644 --- a/man/alluvialClonotypes.Rd +++ b/man/alluvialClonotypes.Rd @@ -55,14 +55,13 @@ combined <- combineTCR(contig_list, #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) -sce <- Seurat::as.SingleCellExperiment(sce) #Using combineExpresion() -sce <- combineExpression(combined, sce) +scRep_example <- combineExpression(combined, scRep_example) +scRep_example$Patient <- substring(scRep_example$orig.ident, 1,3) #Using alluvialClonotypes() -alluvialClonotypes(sce, cloneCall = "gene", -y.axes = c("Patient", "ident"), color = "ident") +alluvialClonotypes(scRep_example, cloneCall = "gene", + y.axes = c("Patient", "ident"), color = "ident") } diff --git a/man/compareClonotypes.Rd b/man/compareClonotypes.Rd index 5d6f567d..50b416e5 100644 --- a/man/compareClonotypes.Rd +++ b/man/compareClonotypes.Rd @@ -13,7 +13,8 @@ compareClonotypes( numbers = NULL, split.by = NULL, graph = "alluvial", - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -38,6 +39,8 @@ to group the new list. NULL will return clusters.} \item{graph}{The type of graph produced, either "alluvial" or "area".} \item{exportTable}{Returns the data frame used for forming the graph.} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot of the proportion of total sequencing read of @@ -55,7 +58,9 @@ proportion to be visualized. combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -compareClonotypes(combined, numbers = 10, -samples = c("PX_P", "PX_T"), cloneCall="aa") +compareClonotypes(combined, + numbers = 5, + samples = c("PX_P", "PX_T"), + cloneCall="aa") } diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd new file mode 100644 index 00000000..fb729b69 --- /dev/null +++ b/man/percentGenes.Rd @@ -0,0 +1,47 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/percentGenes.R +\name{percentGenes} +\alias{percentGenes} +\title{Examining the V or J gene usage across groupings} +\usage{ +percentGenes( + df, + chain = "TRB", + gene = "Vgene", + group.by = NULL, + split.by = NULL, + exportTable = FALSE, + palette = "inferno" +) +} +\arguments{ +\item{df}{The product of combineTCR(), combineBCR(), +expression2List(), or combineExpression().} + +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} + +\item{gene}{"V", "D" or "J"} + +\item{group.by}{The column header used for grouping.} + +\item{split.by}{If using a single-cell object, the column header +to group the new list. NULL will return clusters.} + +\item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} +} +\value{ +ggplot of percentage of indicated genes as a heatmap +} +\description{ +This function the proportion V or J genes used by +grouping variables +} +\examples{ +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +percentGenes(combined, chain = "TRB", gene = "Vgene") +} diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd new file mode 100644 index 00000000..8806b755 --- /dev/null +++ b/man/percentVJ.Rd @@ -0,0 +1,44 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/percentVJ.R +\name{percentVJ} +\alias{percentVJ} +\title{Examining the V or J gene usage across groupings} +\usage{ +percentVJ( + df, + chain = "TRB", + group.by = NULL, + split.by = NULL, + exportTable = FALSE, + palette = "inferno" +) +} +\arguments{ +\item{df}{The product of combineTCR(), combineBCR(), +expression2List(), or combineExpression().} + +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} + +\item{group.by}{The column header used for grouping.} + +\item{split.by}{If using a single-cell object, the column header +to group the new list. NULL will return clusters.} + +\item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} +} +\value{ +ggplot of percentage of V and J gene pairings as a heatmap +} +\description{ +This function the proportion V or J genes used by +grouping variables +} +\examples{ +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +percentVJ(combined, chain = "TRB") +} diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 84643174..9cdeff7b 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -22,7 +22,7 @@ knitr::opts_chunk$set(error = FALSE, message = FALSE, warning = FALSE) if (!require("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } -suppressPackageStartupMessages(invisible(base::library(BiocManager))) +suppressPackageStartupMessages(invisible(library(BiocManager))) quiet_bioc_load <- function(...) { pkgs <- list(...) @@ -527,14 +527,11 @@ scatterClonotype(combined, The data in the scRepertoire package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. For the purposes of the vignette, we have randomly sampled 500 cells from the the fully integrated Seurat object to minimize the size of the package. A full version of the Seurat object is available via [GitHub](https://github.com/ncborcherding/scRepertoire) under the *Getting Data* header. We will use both Seurat and SingleCellExperiment (SCE) with scater to perform the further visualizations in tandem. ```{r} -library(Seurat) -library(scater) scRep_example <- get(data("scRep_example")) -sce <- suppressMessages(UpdateSeuratObject(scRep_example)) -sce <- as.SingleCellExperiment(scRep_example) +sce <- Seurat::as.SingleCellExperiment(scRep_example) #Seurat Format -DimPlot(scRep_example) +Seurat::DimPlot(scRep_example) ##Single Cell Experiment Format plotUMAP(sce, colour_by = "seurat_clusters") @@ -591,11 +588,11 @@ scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) #Adding patient information scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) -DimPlot(scRep_example, group.by = "Type") + - NoLegend() + +Seurat::DimPlot(scRep_example, group.by = "Type") + + Seurat::NoLegend() + scale_color_manual(values=colorblind_vector[c(1,6)]) -DimPlot(scRep_example, group.by = "cloneType") + +Seurat::DimPlot(scRep_example, group.by = "cloneType") + scale_color_manual(values=colorblind_vector[c(2,4,6)]) ``` From 2ec54dbfab4b8ce1464f26d023ed651b33fdf071 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 9 Sep 2023 20:00:51 -0500 Subject: [PATCH 035/142] update name space and working alluvialClonotype visualization --- DESCRIPTION | 4 +- NAMESPACE | 1 - NEWS | 15 +++ R/addVariable.R | 30 +++++ R/alluvialClonotypes.R | 63 +++++++---- R/expression2List.R | 46 ++++++++ R/getCircilize.R | 78 +++++++++++++ R/highlightClonotypes.R | 4 +- R/processing.R | 220 ------------------------------------- R/subsetContig.R | 37 +++++++ man/addVariable.Rd | 2 +- man/alluvialClonotypes.Rd | 13 ++- man/expression2List.Rd | 2 +- man/getCirclize.Rd | 2 +- man/highlightClonotypes.Rd | 4 +- man/stripBarcode.Rd | 27 ----- man/subsetContig.Rd | 2 +- 17 files changed, 268 insertions(+), 282 deletions(-) create mode 100644 R/addVariable.R create mode 100644 R/expression2List.R create mode 100644 R/getCircilize.R delete mode 100644 R/processing.R create mode 100644 R/subsetContig.R delete mode 100644 man/stripBarcode.Rd diff --git a/DESCRIPTION b/DESCRIPTION index acb277d3..1262cdae 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -31,10 +31,8 @@ Imports: igraph, ggraph, tidygraph, - Seurat, SeuratObject, stats, - BiocManager, Rcpp Suggests: knitr, @@ -43,6 +41,8 @@ Suggests: circlize, scales, scater, + BiocManager, + Seurat, spelling, testthat (>= 3.0.0), vdiffr diff --git a/NAMESPACE b/NAMESPACE index 7774de1d..a9d65928 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -30,7 +30,6 @@ export(percentGenes) export(percentVJ) export(quantContig) export(scatterClonotype) -export(stripBarcode) export(subsetContig) export(vizGenes) exportClasses(StartracOut) diff --git a/NEWS b/NEWS index 1019f450..8d9a282b 100644 --- a/NEWS +++ b/NEWS @@ -1,3 +1,18 @@ +CHANGES IN VERSION 2.0.0 +------------------------ + +SIGNIFICANT USER-VISIBLE CHANGES + +* Added percentAA() +* Added percentGenes() +* Added percentVJ() + + +DEPRECATED AND DEFUNCT + +* Deprecate stripBarcodes() + + CHANGES IN VERSION 1.11.0 ------------------------ * Rebasing for the purposes of bioconductor version diff --git a/R/addVariable.R b/R/addVariable.R new file mode 100644 index 00000000..456d5bc2 --- /dev/null +++ b/R/addVariable.R @@ -0,0 +1,30 @@ +#' Adding variables after the combination of contigs. +#' +#' This function adds variables to the product of combineTCR() combineBCR() or +#' expression2List() to be used in later visualizations. For each element, +#' the function will add a column (labled by name) with the variable. +#' The length of the variable paramater needs to match the length of +#' the combined object. +#' +#' @examples +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' combined <- addVariable(combined, +#' name = "Type", +#' variables = rep(c("B", "L"), 4)) +#' +#' @param df The product of combineTCR() combineBCR() or expression2List(). +#' @param name The column header to add. +#' @param variables The exact values to add to each element of the list. +#' @export +#' @return list of contigs with a new column (name). +addVariable <- function(df, name = NULL, variables = NULL) { + if (length(df) != length(variables)) { + stop("Make sure the variables match the length of the contig list") + } + for (i in seq_along(df)) { + df[[i]][,name] <- variables[i] + } + return(df) +} \ No newline at end of file diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index eb6f8727..58d794a2 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -49,8 +49,10 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' scRep_example$Patient <- substring(scRep_example$orig.ident, 1,3) #' #' #Using alluvialClonotypes() -#' alluvialClonotypes(scRep_example, cloneCall = "gene", -#' y.axes = c("Patient", "ident"), color = "ident") +#' alluvialClonotypes(scRep_example, +#' cloneCall = "gene", +#' y.axes = c("Patient", "ident"), +#' color = "ident") #' #' @param sc The seurat or SCE object to visualize after combineExpression(). #' For SCE objects, the cluster variable must be in the meta data under @@ -65,8 +67,9 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' @param color The column header or clonotype(s) to be highlighted. #' @param facet The column label to separate. #' @param alpha The column header to have gradated opacity. -#' @import ggplot2 +#' @param palette Colors to use in visualization - input any hcl.pals() #' +#' @import ggplot2 #' @importFrom ggalluvial StatStratum geom_flow geom_stratum to_lodes_form geom_alluvium #' @importFrom dplyr %>% mutate #' @@ -74,16 +77,18 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' @return Alluvial ggplot comparing clonotype distribution across #' selected parameters. alluvialClonotypes <- function(sc, - cloneCall = c("gene", "nt", "aa", "strict"), + cloneCall = "strict", chain = "both", y.axes = NULL, color = NULL, alpha = NULL, - facet = NULL) { + facet = NULL, + palette = "inferno") { checkSingleObject(sc) cloneCall <- theCall(cloneCall) if (length(y.axes) == 0) { - stop("Make sure you have selected the variable(s) to visualize") } + stop("Make sure you have selected the variable(s) to visualize") + } meta <- grabMeta(sc) if (chain != "both") { meta <- off.the.chain(meta, chain, cloneCall) @@ -103,24 +108,42 @@ alluvialClonotypes <- function(sc, lodes <- makingLodes(meta2, color, alpha, facet, set.axes) plot <- ggplot(data = lodes, aes(x = x, stratum = stratum, - alluvium = alluvium, label = stratum)) + - geom_stratum() + theme_classic() + - theme(axis.title.x = element_blank(), axis.ticks.x = element_blank()) + alluvium = alluvium, + label = stratum)) + + geom_stratum(width = 0.2) + if (is.null(color) & is.null(alpha)) { - plot <- plot + geom_alluvium() + plot <- plot + geom_alluvium(width=0.2) } else if (!is.null(color) & is.null(alpha)) { - plot <- plot+geom_flow(aes(fill = lodes[,color]), - stat = "alluvium", lode.guidance = "forward") + labs(fill = color) + plot <- plot + geom_flow(aes(fill = lodes[,color]), + stat = "alluvium", + lode.guidance = "forward") + + labs(fill = color, + width = 0.2) } else if (is.null(color) & !is.null(alpha)) { - plot <- plot + geom_flow(aes(alpha = lodes[,alpha]), stat = "alluvium", - lode.guidance = "forward") + labs(alpha = alpha) - }else { - plot <- plot+geom_flow(aes(alpha=lodes[,alpha], fill=lodes[,color]), - stat = "alluvium", lode.guidance = "forward") + + plot <- plot + geom_flow(aes(alpha = lodes[,alpha]), + stat = "alluvium", + lode.guidance = "forward") + + labs(alpha = alpha, + width = 0.2) + } else { + plot <- plot+geom_flow(aes(alpha=lodes[,alpha], + fill=lodes[,color]), + stat = "alluvium", + lode.guidance = "forward", + width = 0.2) + labs(fill = color, alpha = alpha) } if (length(facet) == 1 & length(facet) < 2) { plot <- plot + facet_wrap(.~lodes[,facet], scales="free_y") - } else if (length(facet) == 0) { plot <- plot } - plot <- plot + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) + } + + plot <- plot + + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) + + scale_fill_manual(values = .colorizer(palette, length(levels(lodes[,color])))) + + scale_x_discrete(expand = c(0.025,0.025)) + + theme_classic() + + theme(axis.title.x = element_blank(), + axis.ticks.x = element_blank(), + line = element_blank()) return(plot) -} \ No newline at end of file +} diff --git a/R/expression2List.R b/R/expression2List.R new file mode 100644 index 00000000..e96c5bcc --- /dev/null +++ b/R/expression2List.R @@ -0,0 +1,46 @@ +#' Allows users to take the meta data in Seurat/SCE and place it into a list +#' that will work with all the functions +#' +#' Allows users to perform more fundamental measures of clonotype analysis +#' using the meta data from the Seurat or SCE object. For Seurat objects the +#' active identity is automatically added as "cluster". Remaining grouping +#' parameters or SCE or Seurat objects must appear in the meta data. +#' +#' @examples +#' #Getting the combined contigs +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example")) +#' scRep_example <- combineExpression(combined, scRep_example) +#' +#' #Using expression2List +#' newList <- expression2List(scRep_example, split.by = "seurat_clusters") +#' +#' @param sc object after combineExpression(). +#' @param split.by The column header to group the new list. NULL will return clusters. +#' @importFrom stringr str_sort +#' @export +#' @return list derived from the meta data of single-cell object with +#' elements divided by the group parameter +expression2List <- function(sc, split.by) { + if (!inherits(x=sc, what ="Seurat") & + !inherits(x=sc, what ="SummarizedExperiment")) { + stop("Use a Seurat or SCE object to convert into a list") + } + meta <- grabMeta(sc) + if(is.null(split.by)){ + split.by <- "cluster" + } + unique <- str_sort(as.character(unique(meta[,split.by])), numeric = TRUE) + df <- NULL + for (i in seq_along(unique)) { + subset <- subset(meta, meta[,split.by] == unique[i]) + subset <- subset(subset, !is.na(cloneType)) + df[[i]] <- subset + } + names(df) <- unique + return(df) +} \ No newline at end of file diff --git a/R/getCircilize.R b/R/getCircilize.R new file mode 100644 index 00000000..a030740e --- /dev/null +++ b/R/getCircilize.R @@ -0,0 +1,78 @@ +#' Generate data frame to be used with circlize R package to visualize +#' clonotypes as a chord diagram. +#' +#' This function will take the meta data from the product of +#' combineExpression()and generate a relational data frame to +#' be used for a chord diagram. Each cord will represent the number of +#' clonotype unqiue and shared across the multiple group.by variable. +#' +#' @examples +#' #Getting the combined contigs +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' +#' #Getting a sample of a Seurat object +#' scRep_example <- get(data("scRep_example")) +#' scRep_example <- combineExpression(combined, scRep_example) +#' +#' #Getting data frame output for Circilize +#' circles <- getCirclize(scRep_example, group.by = "seurat_clusters") +#' +#' +#' @param sc object after combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param group.by The group header for which you would like to analyze +#' the data. +#' @param proportion Binary will calculate relationship unique +#' clonotypes (proportion = FALSE) or a ratio of the group.by +#' variable (proportion = TRUE) +#' +#' @importFrom reshape2 dcast +#' @export +#' @return data frame of shared clonotypes between groups +#' @author Dillon Corvino, Nick Borcherding +getCirclize <- function(sc, cloneCall = "strict", + group.by = NULL, proportion = FALSE) { + meta <- grabMeta(sc) + cloneCall <- theCall(cloneCall) + test <- meta[, c(cloneCall, group.by)] + test <- test[!is.na(test[,cloneCall]),] + dTest <- suppressMessages(dcast(test, test[,cloneCall] ~ test[,group.by])) + dTest <- dTest[apply(dTest[,-1], 1, function(x) !all(x==0)),] + dTest2 <- dTest[-1] + dTest2[dTest2 >= 1] <- 1 + total <- nrow(dTest) + + list <- list() + for (i in seq_len(nrow(dTest2))) { + list[[i]] <- which(dTest2[i,] > 0) + } + matrix_out <- matrix(ncol = ncol(dTest2), nrow = ncol(dTest2), 0) + for (j in seq_along(list)) { + matrix_out[list[[j]],list[[j]]] <- matrix_out[list[[j]],list[[j]]] + 1 + if (length(list[[j]]) > 1) { + #length <- length(list[[j]]) + diag(matrix_out[list[[j]],list[[j]]]) <- diag(matrix_out[list[[j]],list[[j]]]) - 1 + } + } + + matrix_out[lower.tri(matrix_out)] <- NA + + colnames(matrix_out) <- colnames(dTest2) + rownames(matrix_out) <- colnames(dTest2) + + output <- data.frame(from = rep(rownames(matrix_out), + times = ncol(matrix_out)), + to = rep(colnames(matrix_out), each = nrow(matrix_out)), + value = as.vector(matrix_out), + stringsAsFactors = FALSE) + output <- na.omit(output) + + if (proportion == TRUE) { + output$value <- output$value/total + } + return(output) +} \ No newline at end of file diff --git a/R/highlightClonotypes.R b/R/highlightClonotypes.R index 50267e5c..d6b90ffa 100644 --- a/R/highlightClonotypes.R +++ b/R/highlightClonotypes.R @@ -10,10 +10,10 @@ #' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object -#' scRep_example <- get(data("scRep_example ")) +#' scRep_example <- get(data("scRep_example")) #' #' #Using combineExpresion() -#' scRep_example <- combineExpression(combined, scRep_example ) +#' scRep_example <- combineExpression(combined, scRep_example) #' #' #Using highlightClonotype() #' scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", diff --git a/R/processing.R b/R/processing.R deleted file mode 100644 index 316bbbf5..00000000 --- a/R/processing.R +++ /dev/null @@ -1,220 +0,0 @@ -#' Removing any additional prefixes to the barcodes of filtered contigs. -#' -#' @param contigs The raw loaded filtered_contig_annotation.csv -#' @param column The column in which the barcodes are listed -#' @param connector The type of character in which is attaching the defualt -#' barcode with any other characters -#' @param num_connects The number of strings combined with the connectors -#' @examples -#' stripBarcode(contig_list[[1]], column = 1, connector = "_", num_connects = 1) -#' @export -#' @return list with the suffixes of the barcodes removed. -stripBarcode <- function(contigs, column = 1, connector = "_", num_connects = 3) -{ - count <- as.data.frame( - t(data.frame( - strsplit(contigs[,column], paste("['", connector, "']", sep="")), - stringsAsFactors = FALSE - )), - stringsAsFactors = FALSE - )[num_connects] - - contigs[,column] <- count - return(contigs) -} - -#' Adding variables after the combination of contigs. -#' -#' This function adds variables to the product of combineTCR() combineBCR() or -#' expression2List() to be used in later visualizations. For each element, -#' the function will add a column (labled by name) with the variable. -#' The length of the variable paramater needs to match the length of -#' the combined object. -#' -#' @examples -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' combined <- addVariable(combined, -#' name = "Type", -#' variables = rep(c("B", "L"), 4)) -#' -#' @param df The product of combineTCR() combineBCR() or expression2List(). -#' @param name The column header to add. -#' @param variables The exact values to add to each element of the list. -#' @export -#' @return list of contigs with a new column (name). -addVariable <- function(df, name = NULL, variables = NULL) { - if (length(df) != length(variables)) { - stop("Make sure the variables match the length of the contig list") - } - for (i in seq_along(df)) { - df[[i]][,name] <- variables[i] - } - return(df) -} - -#' Subset the product of combineTCR() combineBCR() or expression2List() -#' -#' This function allows for the subsetting of the product of combineTCR() -#' combineBCR() or expression2List() by the name of the individual list -#' element. In general the names of are samples + _ + ID, allowing -#' for users to subset the product of combineTCR(), combineBCR(), -#' or expression2List() across a string or individual name. -#' -#' @examples -#' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' subset <- subsetContig(combined, name = "sample", variables = c("PX")) -#' -#' @param df The product of combineTCR(), combineBCR(), or expression2List(). -#' @param name The column header you'd like to use to subset. -#' @param variables The values to subset by, must be in the names(df). - -#' @export -#' @return list of contigs that have been filtered for the name parameter -subsetContig <- function(df, name, variables = NULL) { - names2 <- NULL - df2 <- list() - for (i in seq_along(df)) { - if (df[[i]][1,name] %in% variables) { - df2 <- append(df2, list(df[[i]])) - n2 <- names(df)[i] - names2 <- c(names2, n2) - } - else { - next() - } - } - names(df2) <- names2 - return(df2) -} - -#' Allows users to take the meta data in Seurat/SCE and place it into a list -#' that will work with all the functions -#' -#' Allows users to perform more fundamental measures of clonotype analysis -#' using the meta data from the Seurat or SCE object. For Seurat objects the -#' active identity is automatically added as "cluster". Remaining grouping -#' parameters or SCE or Seurat objects must appear in the meta data. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' -#' #Getting a sample of a Seurat object -#' scRep_example <- get(data("scRep_example")) -#' scRep_example <- combineExpression(combined, scRep_example) -#' -#' #Using expression2List -#' newList <- expression2List(scRep_example, split.by = "seurat_clusters") -#' -#' @param sc object after combineExpression(). -#' @param split.by The column header to group the new list. NULL will return clusters. -#' @importFrom stringr str_sort -#' @export -#' @return list derived from the meta data of single-cell object with -#' elements divided by the group parameter -expression2List <- function(sc, split.by) { - if (!inherits(x=sc, what ="Seurat") & - !inherits(x=sc, what ="SummarizedExperiment")) { - stop("Use a Seurat or SCE object to convert into a list") - } - meta <- grabMeta(sc) - if(is.null(split.by)){ - split.by <- "cluster" - } - unique <- str_sort(as.character(unique(meta[,split.by])), numeric = TRUE) - df <- NULL - for (i in seq_along(unique)) { - subset <- subset(meta, meta[,split.by] == unique[i]) - subset <- subset(subset, !is.na(cloneType)) - df[[i]] <- subset - } - names(df) <- unique - return(df) -} - -#' Generate data frame to be used with circlize R package to visualize -#' clonotypes as a chord diagram. -#' -#' This function will take the meta data from the product of -#' combineExpression()and generate a relational data frame to -#' be used for a chord diagram. Each cord will represent the number of -#' clonotype unqiue and shared across the multiple group.by variable. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' -#' #Getting a sample of a Seurat object -#' scRep_example <- get(data("scRep_example")) -#' scRep_example <- combineExpression(combined, scRep_example) -#' -#' #Getting data frame output for Circilize -#' circles <- getCirclize(scRep_example, group.by = "seurat_clusters") -#' -#' -#' @param sc object after combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param group.by The group header for which you would like to analyze -#' the data. -#' @param proportion Binary will calculate relationship unique -#' clonotypes (proportion = FALSE) or a ratio of the group.by -#' variable (proportion = TRUE) -#' -#' @importFrom reshape2 dcast -#' @export -#' @return data frame of shared clonotypes between groups -#' @author Dillon Corvino, Nick Borcherding -getCirclize <- function(sc, cloneCall = "strict", - group.by = NULL, proportion = FALSE) { - meta <- grabMeta(sc) - cloneCall <- theCall(cloneCall) - test <- meta[, c(cloneCall, group.by)] - test <- test[!is.na(test[,cloneCall]),] - dTest <- suppressMessages(dcast(test, test[,cloneCall] ~ test[,group.by])) - dTest <- dTest[apply(dTest[,-1], 1, function(x) !all(x==0)),] - dTest2 <- dTest[-1] - dTest2[dTest2 >= 1] <- 1 - total <- nrow(dTest) - - list <- list() - for (i in seq_len(nrow(dTest2))) { - list[[i]] <- which(dTest2[i,] > 0) - } - matrix_out <- matrix(ncol = ncol(dTest2), nrow = ncol(dTest2), 0) - for (j in seq_along(list)) { - matrix_out[list[[j]],list[[j]]] <- matrix_out[list[[j]],list[[j]]] + 1 - if (length(list[[j]]) > 1) { - #length <- length(list[[j]]) - diag(matrix_out[list[[j]],list[[j]]]) <- diag(matrix_out[list[[j]],list[[j]]]) - 1 - } - } - - matrix_out[lower.tri(matrix_out)] <- NA - - colnames(matrix_out) <- colnames(dTest2) - rownames(matrix_out) <- colnames(dTest2) - - output <- data.frame(from = rep(rownames(matrix_out), - times = ncol(matrix_out)), - to = rep(colnames(matrix_out), each = nrow(matrix_out)), - value = as.vector(matrix_out), - stringsAsFactors = FALSE) - output <- na.omit(output) - - if (proportion == TRUE) { - output$value <- output$value/total - } - return(output) -} - - diff --git a/R/subsetContig.R b/R/subsetContig.R new file mode 100644 index 00000000..0f43d987 --- /dev/null +++ b/R/subsetContig.R @@ -0,0 +1,37 @@ + +#' Subset the product of combineTCR() combineBCR() or expression2List() +#' +#' This function allows for the subsetting of the product of combineTCR() +#' combineBCR() or expression2List() by the name of the individual list +#' element. In general the names of are samples + _ + ID, allowing +#' for users to subset the product of combineTCR(), combineBCR(), +#' or expression2List() across a string or individual name. +#' +#' @examples +#' x <- contig_list +#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), +#' rep(c("P", "T"), 3)) +#' subset <- subsetContig(combined, name = "sample", variables = c("PX")) +#' +#' @param df The product of combineTCR(), combineBCR(), or expression2List(). +#' @param name The column header you'd like to use to subset. +#' @param variables The values to subset by, must be in the names(df). + +#' @export +#' @return list of contigs that have been filtered for the name parameter +subsetContig <- function(df, name, variables = NULL) { + names2 <- NULL + df2 <- list() + for (i in seq_along(df)) { + if (df[[i]][1,name] %in% variables) { + df2 <- append(df2, list(df[[i]])) + n2 <- names(df)[i] + names2 <- c(names2, n2) + } + else { + next() + } + } + names(df2) <- names2 + return(df2) +} \ No newline at end of file diff --git a/man/addVariable.Rd b/man/addVariable.Rd index 4069ecdb..111e4289 100644 --- a/man/addVariable.Rd +++ b/man/addVariable.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/processing.R +% Please edit documentation in R/addVariable.R \name{addVariable} \alias{addVariable} \title{Adding variables after the combination of contigs.} diff --git a/man/alluvialClonotypes.Rd b/man/alluvialClonotypes.Rd index 30dd09c6..44aa95a1 100644 --- a/man/alluvialClonotypes.Rd +++ b/man/alluvialClonotypes.Rd @@ -6,12 +6,13 @@ \usage{ alluvialClonotypes( sc, - cloneCall = c("gene", "nt", "aa", "strict"), + cloneCall = "strict", chain = "both", y.axes = NULL, color = NULL, alpha = NULL, - facet = NULL + facet = NULL, + palette = "inferno" ) } \arguments{ @@ -34,6 +35,8 @@ visualizations.} \item{alpha}{The column header to have gradated opacity.} \item{facet}{The column label to separate.} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ Alluvial ggplot comparing clonotype distribution across @@ -61,7 +64,9 @@ scRep_example <- combineExpression(combined, scRep_example) scRep_example$Patient <- substring(scRep_example$orig.ident, 1,3) #Using alluvialClonotypes() -alluvialClonotypes(scRep_example, cloneCall = "gene", - y.axes = c("Patient", "ident"), color = "ident") +alluvialClonotypes(scRep_example, + cloneCall = "gene", + y.axes = c("Patient", "ident"), + color = "ident") } diff --git a/man/expression2List.Rd b/man/expression2List.Rd index 5551bcb8..89da8356 100644 --- a/man/expression2List.Rd +++ b/man/expression2List.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/processing.R +% Please edit documentation in R/expression2List.R \name{expression2List} \alias{expression2List} \title{Allows users to take the meta data in Seurat/SCE and place it into a list diff --git a/man/getCirclize.Rd b/man/getCirclize.Rd index e6721484..cd8e6460 100644 --- a/man/getCirclize.Rd +++ b/man/getCirclize.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/processing.R +% Please edit documentation in R/getCircilize.R \name{getCirclize} \alias{getCirclize} \title{Generate data frame to be used with circlize R package to visualize diff --git a/man/highlightClonotypes.Rd b/man/highlightClonotypes.Rd index 09f10de1..6336929b 100644 --- a/man/highlightClonotypes.Rd +++ b/man/highlightClonotypes.Rd @@ -33,10 +33,10 @@ combined <- combineTCR(contig_list, "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -scRep_example <- get(data("scRep_example ")) +scRep_example <- get(data("scRep_example")) #Using combineExpresion() -scRep_example <- combineExpression(combined, scRep_example ) +scRep_example <- combineExpression(combined, scRep_example) #Using highlightClonotype() scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", diff --git a/man/stripBarcode.Rd b/man/stripBarcode.Rd deleted file mode 100644 index 8b428984..00000000 --- a/man/stripBarcode.Rd +++ /dev/null @@ -1,27 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/processing.R -\name{stripBarcode} -\alias{stripBarcode} -\title{Removing any additional prefixes to the barcodes of filtered contigs.} -\usage{ -stripBarcode(contigs, column = 1, connector = "_", num_connects = 3) -} -\arguments{ -\item{contigs}{The raw loaded filtered_contig_annotation.csv} - -\item{column}{The column in which the barcodes are listed} - -\item{connector}{The type of character in which is attaching the defualt -barcode with any other characters} - -\item{num_connects}{The number of strings combined with the connectors} -} -\value{ -list with the suffixes of the barcodes removed. -} -\description{ -Removing any additional prefixes to the barcodes of filtered contigs. -} -\examples{ -stripBarcode(contig_list[[1]], column = 1, connector = "_", num_connects = 1) -} diff --git a/man/subsetContig.Rd b/man/subsetContig.Rd index fe274911..005bd16c 100644 --- a/man/subsetContig.Rd +++ b/man/subsetContig.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/processing.R +% Please edit documentation in R/subsetContig.R \name{subsetContig} \alias{subsetContig} \title{Subset the product of combineTCR() combineBCR() or expression2List()} From 4cf7fa0c483c125a58ad816a93ad5c17a5126ceb Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 9 Sep 2023 20:35:20 -0500 Subject: [PATCH 036/142] Update clonalOverlap.R DRYing code out - still need to test overlaps --- R/clonalOverlap.R | 191 ++++++++++++++++++---------------------------- 1 file changed, 75 insertions(+), 116 deletions(-) diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 1e8a0884..1792b0d2 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -13,7 +13,9 @@ #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' -#' clonalOverlap(combined, cloneCall = "gene", method = "overlap") +#' clonalOverlap(combined, +#' cloneCall = "gene", +#' method = "jaccard") #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). #' @param cloneCall How to call the clonotype - VDJC gene (gene), @@ -40,6 +42,11 @@ clonalOverlap <- function(df, split.by = NULL, exportTable = FALSE, palette = "inferno"){ + if(method == "morisita") { + return_type = "freq" + } else { + return_type = "unique" + } df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) @@ -48,25 +55,32 @@ clonalOverlap <- function(df, df <- df[quiet(dput(values))] num_samples <- length(df[]) names_samples <- names(df) - coef_matrix <- data.frame(matrix(NA, num_samples, num_samples)) - colnames(coef_matrix) <- names_samples - rownames(coef_matrix) <- names_samples length <- seq_len(num_samples) + if (chain != "both") { for (i in seq_along(df)) { df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) } } - coef_matrix <- switch(method, - "cosine" = .cosineIndex(df, length, cloneCall, coef_matrix), - "jaccard" = .jaccardIndex(df, length, cloneCall, coef_matrix), - "morisita" = .morisitaIndex(df, length, cloneCall, coef_matrix), - "overlap" = .overlapIndex(df, length, cloneCall, coef_matrix), - "raw" = .rawIndex(df, length, cloneCall, coef_matrix), - "Invalid method specified") - coef_matrix <- as.data.frame(coef_matrix) + #Selecting Index Function + indexFunc <- switch(method, + "morisita" = .morisitaCalc, + "jaccard" = .jaccardCalc, + "raw" = .rawCalc, + "overlap" = .overlapCalc, + "cosine" = .cosineCalc, + stop("Invalid method provided")) + + #Calculating Index + coef_matrix <- data.frame(matrix(NA, num_samples, num_samples)) + coef_matrix <- .calculateIndex(df, length, cloneCall, coef_matrix, indexFunc, return_type) + + #Data manipulation + colnames(coef_matrix) <- names_samples + rownames(coef_matrix) <- names_samples coef_matrix$names <- rownames(coef_matrix) + if (exportTable == TRUE) { return(coef_matrix) } @@ -90,122 +104,67 @@ clonalOverlap <- function(df, return(plot) } -#Calculate the Morisita Index for Overlap Analysis -#' @author Massimo Andreatta, Nick Borcherding -.morisitaIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- data.frame(table(df.i[,cloneCall])) - colnames(df.i) <- c(cloneCall, 'Count') - df.i[,2] <- as.numeric(df.i[,2]) - for (j in seq_along(length)){ - if (i >= j){ next } - else { df.j <- df[[j]] - df.j <- data.frame(table(df.j[,cloneCall])) - colnames(df.j) <- c(cloneCall, 'Count') - df.j[,2] <- as.numeric(df.j[,2]) - merged <- merge(df.i, df.j, by = cloneCall, all = TRUE) - merged[is.na(merged)] <- 0 - X <- sum(merged[,2]) - Y <- sum(merged[,3]) - sum.df.i <- sum(df.i[,2]^2) - sum.df.j <- sum(df.j[,2]^2) - - num <- 2 * sum(merged[, 2] * merged[, 3]) - den <- ((sum.df.i / (X^2) + sum.df.j / (Y^2)) * X * Y) - - coef.i.j <- num/den - coef_matrix[i,j] <- coef.i.j - } - } +# Helper function to prepare data +.prepareDataFrame <- function(df, cloneCall, return_type = "unique") { + if (return_type == "unique") { + return(unique(df[, cloneCall])) + } else if (return_type == "freq") { + temp_df <- data.frame(table(df[, cloneCall])) + colnames(temp_df) <- c(cloneCall, 'Count') + temp_df[, 2] <- as.numeric(temp_df[, 2]) + return(temp_df) } - return(coef_matrix) } - -#Calculate the Jaccard Similarity Index for Overlap Analysis -.jaccardIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,cloneCall] - df.i_unique <- unique(df.i) - for (j in seq_along(length)){ - if (i >= j){ next } - - df.j <- df[[j]] - df.j <- df.j[,cloneCall] - df.j_unique <- unique(df.j) - overlap <- length(intersect(df.i_unique, - df.j_unique)) - coef_matrix[i,j] <- - overlap/(sum(length(df.i_unique), - length(df.j_unique))-overlap) +# Helper function for common loop and conditional structure +.calculateIndex <- function(df, length, cloneCall, coef_matrix, indexFunc, return_type = "unique") { + for (i in seq_along(length)) { + df_i <- .prepareDataFrame(df[[i]], cloneCall, return_type) + for (j in seq_along(length)) { + if (i >= j) { next } + df_j <- .prepareDataFrame(df[[j]], cloneCall, return_type) + coef_matrix[i, j] <- indexFunc(df_i, df_j) } } return(coef_matrix) } -.rawIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,cloneCall] - df.i_unique <- unique(df.i) - for (j in seq_along(length)){ - if (i >= j){ next } - df.j <- df[[j]] - df.j <- df.j[,cloneCall] - df.j_unique <- unique(df.j) - overlap <- length(intersect(df.i_unique, - df.j_unique)) - coef_matrix[i,j] <- overlap - } - } - return(coef_matrix) +# Morisita Index calculation function +.morisitaCalc <- function(df_i, df_j) { + merged <- merge(df_i, df_j, by = names(df_i)[1], all = TRUE) + merged[is.na(merged)] <- 0 + + X <- sum(merged[, 2]) + Y <- sum(merged[, 3]) + + num <- 2 * sum(merged[, 2] * merged[, 3]) + den <- ((sum(df_i[, 2]^2) / (X^2)) + (sum(df_j[, 2]^2) / (Y^2))) * X * Y + + return(num / den) } +# Jaccard Index calculation function +.jaccardCalc <- function(df_i, df_j) { + overlap <- length(intersect(df_i, df_j)) + return(overlap / (length(df_i) + length(df_j) - overlap)) +} -#Calculate the Overlap Coefficient for Overlap Analysis -#' @author Nick Bormann, Nick Borcherding -.overlapIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,c(cloneCall)] - df.i_unique <- unique(df.i) - for (j in seq_along(length)){ - if (i >= j){ next } - else { df.j <- df[[j]] - df.j <- df.j[,c(cloneCall)] - df.j_unique <- unique(df.j) - overlap <- length(intersect(df.i_unique, - df.j_unique)) - coef_matrix[i,j] <- - overlap/min(length(df.i_unique), - length(df.j_unique)) } } } - return(coef_matrix) +# Raw Index calculation function +.rawCalc <- function(df_i, df_j) { + return(length(intersect(df_i, df_j))) } -.cosineIndex <- function(df, length, cloneCall, coef_matrix) { - for (i in seq_along(length)){ - df.i <- df[[i]] - df.i <- df.i[,cloneCall] - df.i_unique <- unique(df.i) - for (j in seq_along(length)){ - if (i >= j){ next } - else { - df.j <- df[[j]] - df.j <- df.j[,cloneCall] - df.j_unique <- unique(df.j) - all_species <- unique(c(df.j_unique, df.j_unique)) - vector_location1 <- as.integer(all_species %in% df.i_unique) - vector_location2 <- as.integer(all_species %in% df.j_unique) - - coef_matrix[i,j] <- - sum(vector_location1 * vector_location2) / (sqrt(sum(vector_location1^2)) * sqrt(sum(vector_location2^2))) - } - } - } - coef_matrix <- as.matrix(coef_matrix) - coef_matrix[is.nan(coef_matrix)] <- 0 - return(coef_matrix) +# Overlap Index calculation function +.overlapCalc <- function(df_i, df_j) { + overlap <- length(intersect(df_i, df_j)) + return(overlap / min(length(df_i), length(df_j))) } +.cosineCalc <- function(df_i, df_j) { + all_species <- unique(c(df_i, df_j)) + vector_location1 <- as.integer(all_species %in% df_i) + vector_location2 <- as.integer(all_species %in% df_j) + + return(sum(vector_location1 * vector_location2) / + (sqrt(sum(vector_location1^2)) * sqrt(sum(vector_location2^2)))) +} From b038bc9fa2141ef92b9ad172451c6f43ecd52908 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 10 Sep 2023 06:38:23 -0500 Subject: [PATCH 037/142] Update clonalOverlap.R --- R/clonalOverlap.R | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 1792b0d2..c104aaa3 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -23,14 +23,15 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param method The method to calculate the overlap, either the "overlap" -#' coefficient, "morisita", "jaccard" indices, "cosine" similarity or "raw" +#' @param method The method to calculate the overlap, +#' "overlap" +# "morisita", "jaccard" indices, "cosine" similarity or "raw" #' for the base numbers. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param exportTable Returns the data frame used for forming the graph #' @param palette Colors to use in visualization - input any hcl.pals() -#' @importFrom stringr str_sort +#' @importFrom stringr str_sort str_to_title #' @importFrom reshape2 melt #' @importFrom stats quantile #' @export @@ -39,7 +40,8 @@ clonalOverlap <- function(df, cloneCall = "strict", method = NULL, chain = "both", - split.by = NULL, + split.by = NULL, + text.color = NULL, exportTable = FALSE, palette = "inferno"){ if(method == "morisita") { @@ -79,23 +81,20 @@ clonalOverlap <- function(df, #Data manipulation colnames(coef_matrix) <- names_samples rownames(coef_matrix) <- names_samples - coef_matrix$names <- rownames(coef_matrix) - + if (exportTable == TRUE) { return(coef_matrix) } - coef_matrix <- suppressMessages(melt(coef_matrix)) - coef_matrix$variable <- factor(coef_matrix$variable, levels = values) - coef_matrix$names <- factor(coef_matrix$names, levels = values) + mat_melt <- suppressMessages(melt(as.matrix(coef_matrix))) - tertile_values <- quantile(na.omit(coef_matrix[,"value"]), probs = c(1/3,2/3)) + mean_value <- mean(na.omit(mat_melt[,"value"])) - plot <- ggplot(coef_matrix, aes(x=names, y=variable, fill=value)) + + plot <- ggplot(mat_melt, aes(x=Var1, y=Var2, fill=value)) + geom_tile() + - geom_tile(data = coef_matrix[!is.na(coef_matrix[,"value"]),], fill = NA, lwd= 0.25, color = "black") + - labs(fill = method) + + geom_tile(data = mat_melt[!is.na(mat_melt[,"value"]),], fill = NA, lwd= 0.25, color = "black") + + labs(fill = str_to_title(method)) + geom_text(aes(label = round(value, digits = 3), - color = ifelse(value <= as.vector(tertile_values[1]), + color = ifelse(value <= mean_value, "white", "black"))) + scale_fill_gradientn(colors = .colorizer(palette, 7), na.value = "white") + scale_color_identity() + @@ -160,6 +159,7 @@ clonalOverlap <- function(df, return(overlap / min(length(df_i), length(df_j))) } +# Overlap Index calculation function .cosineCalc <- function(df_i, df_j) { all_species <- unique(c(df_i, df_j)) vector_location1 <- as.integer(all_species %in% df_i) From 3772e02cba7c2cf4cd7c4d97af99bb53e99c3462 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 11 Sep 2023 04:11:26 -0500 Subject: [PATCH 038/142] update clonalDiversity --- R/clonalDiversity.R | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 9ca7599b..2421f1c3 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -34,7 +34,7 @@ #' will automatically exportTable = TRUE #' @param skip.boots remove downsampling and boot strapping from the calculation #' @importFrom stringr str_sort str_split -#' @importFrom reshape2 melt +#' @importFrom reshape2 mat_melt #' @importFrom dplyr sample_n #' @import ggplot2 #' @export @@ -119,22 +119,22 @@ clonalDiversity <- function(df, } rownames(mat) <- names(df) - melt <- suppressMessages(melt(mat, id.vars = c(group.by, x.axis))) - values <- str_sort(as.character(unique(melt[,group.by])), + mat_melt <- suppressMessages(melt(mat, id.vars = c(group.by, x.axis))) + values <- str_sort(as.character(unique(mat_melt[,group.by])), numeric = TRUE) values <- quiet(dput(values)) - melt[,group.by] <- factor(melt[,group.by], levels = values) + mat_melt[,group.by] <- factor(mat_melt[,group.by], levels = values) if (x.axis == "x.axis") { - plot <- ggplot(melt, aes(x=1, y=as.numeric(value))) + plot <- ggplot(mat_melt, aes(x=1, y=as.numeric(value))) } else { - plot <- ggplot(melt, aes(x=melt[,x.axis], y=as.numeric(value))) + plot <- ggplot(mat_melt, aes(x=mat_melt[,x.axis], y=as.numeric(value))) } plot <- plot + geom_boxplot(outlier.alpha = 0) + - geom_jitter(aes(color = melt[,group.by]), size = 3) + - labs(color="Group") + + geom_jitter(aes(fill = mat_melt[,group.by]), size = 3, shape = 21, stroke = 0.25, color = "black") + + labs(fill = "Group") + ylab("Index Score") + - scale_color_manual(values = .colorizer(palette,length(unique(melt[,group.by])))) + + scale_fill_manual(values = .colorizer(palette,length(unique(mat_melt[,group.by])))) + facet_wrap(~variable, scales = "free", ncol = length(metrics)) + theme_classic() + theme(axis.title.x = element_blank()) From 68b23f633ce224671a7f80f9f60a8d852fb7c24d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 12 Sep 2023 12:42:04 -0500 Subject: [PATCH 039/142] update startrac --- R/startrac.R | 619 ++++++++++----------------------------------------- 1 file changed, 123 insertions(+), 496 deletions(-) diff --git a/R/startrac.R b/R/startrac.R index 5bcab143..38411223 100644 --- a/R/startrac.R +++ b/R/startrac.R @@ -1,7 +1,6 @@ - -#' Diversity indices for single-cell RNA-seq +#' Startrac-based diversity indices for single-cell RNA-seq #' -#' @description This function utilizes the Startrac R package derived from +#' @description This function utilizes the Startrac approach derived from #' \href{https://pubmed.ncbi.nlm.nih.gov/30479382/}{PMID: 30479382} #' Required to run the function, the "type" variable needs to include the #' difference in where the cells were derived. The output of this function @@ -11,7 +10,6 @@ #' read and cite the linked manuscript. #' #' @examples -#' \dontrun{ #' #Getting the combined contigs #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", @@ -20,55 +18,107 @@ #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example ")) #' scRep_example <- combineExpression(combined, scRep_example ) +#' scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) +#' scRep_example$Type <- substring(scRep_example$orig.ident,4,4) #' #' #Using occupiedscRepertoire() #' StartracDiversity(scRep_example , type = "Type", sample = "Patient", by = "overall") -#' } +#' #' #' @param sc The seurat or SCE object to visualize after combineExpression(). #' For SCE objects, the cluster variable must be in the meta data under #' "cluster". -#' @param type The column header in the meta data that gives the where the -#' cells were derived from, not the patient sample IDs -#' @param sample The column header corresponding to individual samples -#' or patients. -#' @param by Method to subset the indices by either overall -#' (across all samples) or by specific group +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param type The variable in the meta data that provides tissue type +#' @param group.by The variable in the meta data to group by, often samples +#' @param metric Calculate the "standard" values across the group.by variable +#' or "pairwise" values #' @param exportTable Returns the data frame used for forming the graph #' @importFrom reshape2 melt #' @import ggplot2 #' @export #' @return ggplot object of Startrac diversity metrics StartracDiversity <- function(sc, - type = "Type", - sample = NULL, - by = "overall", - exportTable = FALSE) { - meta <- grabMeta(sc) - colnames(meta)[ncol(meta)] <- "majorCluster" - meta$clone.status <- ifelse(meta$Frequency > 1, "Yes", "No") + cloneCall = "strict", + chain = "both", + type = "Type", + group.by = NULL, + metric = "standard", + exportTable = FALSE) { + + cloneCall <- theCall(cloneCall) + df <- grabMeta(sc) + barcodes <- rownames(df) + colnames(df)[ncol(df)] <- "majorCluster" + + if (is.null(group.by)) { + if("orig.ident" %!in% colnames(df)) { + stop("Please select a group.by variable") + } + group.by <- "orig.ident" + } + group.levels = unique(dat[,group.by]) + + cloneCall <- theCall(cloneCall) + if (chain != "both") { + df <- off.the.chain(df, chain, cloneCall) + } + + df <- df %>% + group_by(df[,group.by], df[,cloneCall]) %>% + dplyr::mutate(n = n()) %>% + as.data.frame() + + rownames(df) <- barcodes + remove.pos <- which(df[,cloneCall] %in% c("", NA)) + if (length(remove.pos) > 0) { + df <- df[-remove.pos,] + } + df[,"clone.status"] <- ifelse(df[,"n"] > 1, "Yes", "No") + if (is.null(sample)) { stop("Must Add the sample information in order to make the StarTrac calculations") } else { - processed <- data.frame(rownames(meta), meta$CTstrict, meta$clone.status, - meta[,sample], meta[,"majorCluster"], - meta[,type], stringsAsFactors = FALSE) + processed <- data.frame(rownames(df), df[,cloneCall], df$clone.status, + df[,group.by], df[,"majorCluster"], + df[,type], stringsAsFactors = FALSE) colnames(processed) <- c("Cell_Name", "clone.id", "clone.status", "patient", "majorCluster", "loc") } + processed[processed == "NA"] <- NA processed <- na.omit(processed) - indices <- suppressWarnings(Startrac.run(processed, proj = "total", verbose = FALSE)) - indices <- data.frame(indices@cluster.data) - if (by == "overall") { - indices <- subset(indices, aid != "total") + indexFunc <- switch(metric, + "standard" = .calIndex, + "pairwise" = .pIndex, + stop("Invalid metric provided")) + + mat.list <- list() + for(i in seq_along(group.levels)) { + mat.list[[i]] <- as.data.frame(indexFunc(processed[processed[,4] == group.levels[i],])) + } + names(mat.list) <- group.levels + if(metric == "standard") { + mat <- bind_rows(mat.list, .id = "group") + } else if (metric == "pairwise") { + tran.list <- bind_rows(mat) + } + + if (exportTable) { + return(mat) + } + - melted <- melt(indices) - values <- as.character(unique(melted$majorCluster)) - values2 <- quiet(dput(values)) - melted$majorCluster <- factor(melted$majorCluster, levels = values2) + mat_melt <- melt(mat) + values <- as.character(unique(mat_melt$majorCluster)) + values2 <- quiet(dput(values)) + mat_melt$majorCluster <- factor(mat_melt$majorCluster, levels = values2) - plot <- ggplot(melted, aes(x=majorCluster, y=value)) + + plot <- ggplot(mat_melt, aes(x=majorCluster, y=value)) + geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0) + facet_grid(variable ~.) + theme_classic() + @@ -76,229 +126,48 @@ StartracDiversity <- function(sc, guides(fill="none") + theme(axis.title.x = element_blank()) - } else { - indices <- subset(indices, aid == by) - plot <- ggplot(melted, aes(x=majorCluster, y=value)) + - geom_point(aes(fill = by)) + - facet_grid(variable ~.) + - theme_classic() + - ylab("Index Score") + - guides(fill="none") + - theme(axis.title.x = element_blank()) - } - if (exportTable) { - return(indices) - } + + return(plot) } - - -#' The Startrac Class -#' -#' The Startrac object store the data for tcr-based T cell dynamics analysis. -#' @slot aid character. aid of the object, used for identification of the object. -#' For example, patient id. default: "AID" -#' @slot cell.data data.frame. Each line for a cell, and these columns as -#' required: `Cell_Name`, `clone.id`, `patient`, `majorCluster`, `loc` -#' @slot cell.perm.data object. list of `Startrac`` objects constructed from -#' permutated cell data -#' @slot clonotype.data data.frame. Each line for a clonotype; contain the -#' clonotype level indexes information -#' @slot cluster.data data.frame. Each line for a cluster; contain the cluster -#' level indexes information -#' @slot pIndex.migr data.frame. Each line for a cluster; pairwise migration -#' index between the two locations indicated in the column name. -#' @slot pIndex.tran data.frame. Each line for a cluster; pairwise transition -#' index between the two major clusters indicated by the row name and column name. -#' @slot cluster.sig.data data.frame. Each line for a cluster; contains the -#' p values of cluster indices. -#' @slot pIndex.sig.migr data.frame. Each line for a cluster; contains the -#' p values of pairwise migration indices. -#' @slot pIndex.sig.tran data.frame. Each line for a cluster; contains the -#' p values of pairwise transition indices. -#' @slot clonotype.dist.loc matrix. Each line for a clonotype and describe -#' the cells distribution among the locations. -#' @slot clonotype.dist.cluster matrix. Each line for a clonotype and -#' describe the cells distribution among the clusters. -#' @slot clust.size array. Number of cells of each major cluster. -#' @slot patient.size array. Number of cells of each patient. -#' @slot clone.size array. Number of cells of each clone. -#' @slot clone2patient array. Mapping from patient id to clone id. -#' @name Startrac -#' @rdname Startrac -#' @aliases Startrac-class -#' @return method definition for running startrac -Startrac <- setClass("Startrac", - slots = c(aid = "character", - cell.data = "data.frame", - cell.perm.data = "list", - clonotype.data = "data.frame", - cluster.data = "data.frame", - cluster.sig.data = "data.frame", - pIndex.migr = "data.frame", - pIndex.tran = "data.frame", - pIndex.sig.migr = "data.frame", - pIndex.sig.tran = "data.frame", - clonotype.dist.loc = "matrix", - clonotype.dist.cluster = "matrix", - clust.size = "array", - patient.size = "array", - clone.size = "array", - clone2patient = "array")) - - -setValidity("Startrac", - function(object) { - msg <- NULL - if(!is.data.frame(object@cell.data) || - !all(c("Cell_Name","clone.id","patient","majorCluster","loc") %in% colnames(object@cell.data))){ - msg <- sprintf("cell.data must be data.frame and contain these columns: Cell_Name, clone.id, - patient, majorCluster, loc") - } - if (is.null(msg)) TRUE - else msg - } -) - - -#' show method for Startrac -# -#' @param object A Startrac object -#' @name show -#' @aliases show,Startrac-method -#' @docType methods -#' @keywords internal -#' @return method for show -setMethod("show", - signature = "Startrac", - definition = function(object) { - cat(sprintf("An object of class %s, aid %s, %d cells from %d clonotypes\n", - class(object), - object@aid, - nrow(object@cell.data), - nrow(object@clonotype.data))) - invisible(x = NULL) - } -) - -#' initialize method for Startrac -# -#' @importFrom plyr llply ldply -#' @param .Object A Startrac object -#' @param cell.data data.frame contains the input data -#' @param aid character analysis id -#' @param n.perm integer number of permutation will be performed. If NULL, no permutation. (default: NULL) -#' @param cores number of core to be used. Passed to doParallel registerDoParallel. default: NULL. -#' @name initialize -#' @aliases initialize,Startrac-method -#' @docType methods -#' @rdname initialize-methods -#' @return an object of class \code{Startrac} -#' @keywords internal -setMethod("initialize", - signature = "Startrac", - definition = function(.Object, cell.data, aid="AID",n.perm=NULL,cores=NULL){ - .Object@aid <- aid - .Object@cell.data <- as.data.frame(cell.data) - validObject(.Object) - .Object@clonotype.dist.loc <- unclass(table(.Object@cell.data[,c("clone.id","loc")])) - .Object@clonotype.dist.cluster <- unclass(table(.Object@cell.data[,c("clone.id","majorCluster")])) - .Object@clust.size <- unclass(table(.Object@cell.data$majorCluster)) - .Object@patient.size <- unclass(table(.Object@cell.data$patient)) - .Object@clone.size <- unclass(sort(table(.Object@cell.data$clone.id),decreasing = TRUE)) - .clone2patient <- unique(.Object@cell.data[,c("patient","clone.id")]) - .Object@clone2patient <- as.array(structure(.clone2patient$patient, - names=.clone2patient$clone.id)) - .Object@cell.perm.data <- list() - if(!is.null(n.perm)){ - registerDoParallel(if(is.null(cores)) (detectCores()-2) else cores) - .Object@cell.perm.data <- llply(seq_len(n.perm),function(i){ - perm.cell.data <- ldply(unique(.Object@cell.data$patient),function(pp){ - .dat <- subset(.Object@cell.data,patient==pp) - .dat$clone.id <- .dat$clone.id[sample(nrow(.dat))] - return(.dat) - }) - ###perm.cell.data$clone.id <- perm.cell.data$clone.id[sample(nrow(perm.cell.data))] - new("Startrac",perm.cell.data,aid=sprintf("perm%06d",i)) - },.progress = "none",.parallel=TRUE) - } - return(.Object) - } -) - #' Calculate cluster level indices -# -#' @name calIndex -#' @aliases calIndex calIndex,Startrac-method -#' #' @importFrom plyr llply -#' @importFrom parallel makeCluster stopCluster detectCores -#' @importFrom doParallel registerDoParallel -#' @param object A Startrac object -#' @param n.perm integer number of permutation will be performed. If NULL, no permutation. (default: NULL) -#' @param cores number of core to be used. Passed to doParallel registerDoParallel. default: NULL. -#' @param normEntropy logical; whether normalize migration and transition index. default: FALSE. -#' @return an object of class \code{Startrac} #' @keywords internal -Startrac.calIndex <- function(object,cores,n.perm,normEntropy){ - .entropy <- mcol.entropy(object@clonotype.dist.cluster) - .entropy.max <- log2(colSums(object@clonotype.dist.cluster > 0)) - object@cluster.data <- data.frame("aid"=object@aid, - "majorCluster"=colnames(object@clonotype.dist.cluster), - "expa"=1-.entropy/.entropy.max, - stringsAsFactors = FALSE) - if(normEntropy){ - .entropy.migr.max <- log2(ncol(object@clonotype.dist.loc)) - .entropy.tran.max <- log2(ncol(object@clonotype.dist.cluster)) - }else{ - .entropy.migr.max <- 1 - .entropy.tran.max <- 1 - } - object@clonotype.data <- - data.frame("clone.id"=rownames(object@clonotype.dist.loc), - "migr"=mrow.entropy(object@clonotype.dist.loc)/.entropy.migr.max, - "tran"=mrow.entropy(object@clonotype.dist.cluster)/.entropy.tran.max) - weights.mtx <- sweep(object@clonotype.dist.cluster,2,colSums(object@clonotype.dist.cluster),"/") - index.mtx <- t(weights.mtx) %*% (as.matrix(object@clonotype.data[,c("migr","tran")])) - object@cluster.data <- cbind(object@cluster.data,index.mtx) - if(!is.null(n.perm)){ - registerDoParallel(if(is.null(cores)) (detectCores()-2) else cores) - object@cell.perm.data <- llply(object@cell.perm.data,function(x){ - calIndex(x,cores=1,normEntropy=normEntropy) - },.progress = "none",.parallel=TRUE) - } - return(object) +.calIndex <- function(processed){ + clonotype.dist.cluster <- table(processed[,c("clone.id","majorCluster")]) + clonotype.dist.loc <- unclass(table(processed[,c("clone.id","loc")])) + .entropy <- mcol.entropy(clonotype.dist.cluster) + .entropy.max <- log2(colSums(clonotype.dist.cluster > 0)) + expa <- 1-.entropy/.entropy.max + + .entropy.migr.max <- 1 + .entropy.tran.max <- 1 + + + clonotype.data <- + data.frame("clone.id"=rownames(clonotype.dist.loc), + "migr"=mrow.entropy(clonotype.dist.loc)/.entropy.migr.max, + "tran"=mrow.entropy(clonotype.dist.cluster)/.entropy.tran.max) + weights.mtx <- sweep(clonotype.dist.cluster,2,colSums(clonotype.dist.cluster),"/") + calIndex.matrix <- t(weights.mtx) %*% (as.matrix(clonotype.data[,c("migr","tran")])) + calIndex.matrix <- cbind(calIndex.matrix, expa = expa) + calIndex.matrix[is.nan(calIndex.matrix)] <- NA + return(calIndex.matrix) } -setGeneric("calIndex", function(object,cores=NULL,n.perm=NULL,normEntropy=FALSE) standardGeneric("calIndex")) - -#' @rdname calIndex -#' @aliases calIndex -#' @keywords internal -setMethod("calIndex", signature = "Startrac", definition = Startrac.calIndex) - #' Calculate pairwise indices -# -#' @name pIndex -#' @aliases pIndex pIndex,Startrac-method -#' #' @importFrom reshape2 dcast #' @importFrom plyr ldply adply #' @importFrom parallel makeCluster stopCluster detectCores #' @importFrom doParallel registerDoParallel #' @importFrom utils combn -#' @param object A Startrac object -#' @param cores number of core to be used. Passed to doParallel registerDoParallel. default: NULL. -#' @param n.perm integer number of permutation will be performed. If NULL, no permutation. (default: NULL) -#' @return an object of class \code{Startrac} -#' @keywords internal -Startrac.pIndex <- function(object,cores,n.perm){ - clone.dist.loc.majorCluster <- table(object@cell.data[,c("majorCluster","clone.id","loc")]) - withCallingHandlers({ +.pIndex <- function(processed){ + clone.dist.loc.majorCluster <- table(processed[,c("majorCluster","clone.id","loc")]) + cls.migr.index.df <- ldply(seq_len(dim(clone.dist.loc.majorCluster)[1]), function(i,clone.dist.loc.majorCluster){ dat.cls <- clone.dist.loc.majorCluster[i,,] @@ -319,198 +188,35 @@ Startrac.pIndex <- function(object,cores,n.perm){ } return(dat.cls.index) },clone.dist.loc.majorCluster=clone.dist.loc.majorCluster,.progress = "none",.parallel=FALSE) - },warning=function(w) { - if(grepl("... may be used in an incorrect context:",conditionMessage(w))) - invokeRestart("muffleWarning") - }) + if(!is.null(cls.migr.index.df) && nrow(cls.migr.index.df)>0){ cls.migr.index.df$crossLoc <- sprintf("%s-%s",cls.migr.index.df$V1,cls.migr.index.df$V2) - object@pIndex.migr <- dcast(cls.migr.index.df,majorCluster ~ crossLoc,value.var = "pIndex.migr") - object@pIndex.migr <- cbind(data.frame(aid=object@aid,stringsAsFactors = FALSE),object@pIndex.migr) + pIndex.migr <- dcast(cls.migr.index.df,majorCluster ~ crossLoc,value.var = "pIndex.migr") }else{ - object@pIndex.migr <- data.frame() + pIndex.migr <- data.frame() } - if(!is.null(ncol(object@clonotype.dist.cluster)) && ncol(object@clonotype.dist.cluster)>=2){ - comb.cls <- expand.grid(colnames(object@clonotype.dist.cluster), - colnames(object@clonotype.dist.cluster),stringsAsFactors = FALSE) + clonotype.dist.cluster <- table(processed[,c("clone.id","majorCluster")]) + if(!is.null(ncol(clonotype.dist.cluster)) && ncol(clonotype.dist.cluster)>=2){ + comb.cls <- expand.grid(colnames(clonotype.dist.cluster), + colnames(clonotype.dist.cluster),stringsAsFactors = FALSE) comb.cls <- comb.cls[comb.cls[,1]!=comb.cls[,2],] - withCallingHandlers({ - cls.tran.index.df <- adply(comb.cls,1,function(x,object){ - dat.block <- object@clonotype.dist.cluster[,c(x[[1]],x[[2]])] + cls.tran.index.df <- adply(comb.cls,1,function(x,object){ + dat.block <- clonotype.dist.cluster[,c(x[[1]],x[[2]])] dat.block.clone.index <- mrow.entropy(dat.block) dat.block.clone.index[is.na(dat.block.clone.index)] <- 0 data.frame(pIndex.tran= t(rowSums(dat.block)/sum(dat.block)) %*% dat.block.clone.index) },object=object,.progress = "none",.parallel=FALSE) - },warning=function(w) { - if(grepl("... may be used in an incorrect context:",conditionMessage(w))) - invokeRestart("muffleWarning") - }) - object@pIndex.tran <- dcast(cls.tran.index.df,Var2~Var1,value.var = "pIndex.tran") - colnames(object@pIndex.tran)[1] <- "majorCluster" - object@pIndex.tran <- cbind(data.frame(aid=object@aid,stringsAsFactors = FALSE),object@pIndex.tran) - if(!is.null(n.perm)){ - registerDoParallel(if(is.null(cores)) (detectCores()-2) else cores) - object@cell.perm.data <- llply(object@cell.perm.data,function(x){ - pIndex(x,n.perm=NULL) - },.progress = "none",.parallel=TRUE) - } - }else{ - object@pIndex.tran <- data.frame() - if(!is.null(n.perm)){ - } - } - return(object) -} - - -setGeneric("pIndex", function(object,cores=NULL,n.perm=NULL) standardGeneric("pIndex")) - -#' @rdname pIndex -#' @aliases pIndex -#' @keywords internal -setMethod("pIndex", signature = "Startrac", definition = Startrac.pIndex) - - - - -#' Get the p value given one Startrac object and a list of Startrac objects from permutation data -# -#' @name getSig -#' -#' @importFrom plyr laply -#' @importFrom doParallel registerDoParallel -#' @importFrom reshape2 melt -#' @param obj A Startrac object -#' @param obj.perm A list of Startrac objects from permutation data -#' @keywords internal -#' @return an object of class \code{Startrac} -Startrac.getSig <- function(obj,obj.perm) -{ - .get.empirical.p <- function(obj,obj.perm,slot.name) - { - if(nrow(slot(obj,slot.name))==0){ - return(data.frame()) - } - a.index <- melt(slot(obj,slot.name),id.vars=c("aid","majorCluster"),variable.name="index") - if(!is.null(obj.perm)){ - perm.mtx <- t(laply(obj.perm,function(x){ - vv <- melt(slot(x,slot.name),id.vars=c("aid","majorCluster"),variable.name="index") - vv.mtx <- as.matrix(vv[,"value",drop=FALSE]) - rownames(vv.mtx) <- sprintf("%s.%s",vv[["majorCluster"]],vv[["index"]]) - colnames(vv.mtx) <- x@aid - return(vv.mtx) - })) - stopifnot(all(sprintf("%s.%s",a.index$majorCluster,a.index$index)==rownames(perm.mtx))) - a.index$p.value <- sapply(seq_along(a.index$value),function(i){ - v.rnd <- perm.mtx[i,,drop=FALSE] - sum(v.rnd >= a.index$value[i])/length(v.rnd) - }) - }else{ - a.index$p.value <- NA - } - return(a.index) + pIndex.tran <- as.data.frame(dcast(cls.tran.index.df,Var2~Var1,value.var = "pIndex.tran")) + colnames(pIndex.tran)[1] <- "majorCluster" + + } else{ + pIndex.tran <- data.frame() } - obj@cluster.sig.data <- .get.empirical.p(obj,obj.perm,"cluster.data") - obj@pIndex.sig.migr <- .get.empirical.p(obj,obj.perm,"pIndex.migr") - obj@pIndex.sig.tran <- .get.empirical.p(obj,obj.perm,"pIndex.tran") - return(obj) -} - - -setGeneric("getSig", function(obj,obj.perm=NULL) standardGeneric("getSig")) - -#' @rdname getSig -#' @aliases getSig -#' @keywords internal -setMethod("getSig", signature = "Startrac", definition = Startrac.getSig) - - - -#' The StartracOUt Class -#' -#' Object store the result of Startrac.run: -#' @slot proj character. identification of the object. For example, patient id. -#' default: "AID" -#' @slot cluster.data data.frame. Each line for a cluster; contain the cluster -#' level indexes information -#' @slot pIndex.migr data.frame. Each line for a cluster; pairwise migration -#' index between the two locations indicated in the column name. -#' @slot pIndex.tran data.frame. Each line for a cluster; pairwise transition -#' index between the two major clusters indicated by the row name and column -#' name. -#' @slot cluster.sig.data data.frame. Each line for a cluster; contains the p -#' values of cluster indices. -#' @slot pIndex.sig.migr data.frame. Each line for a cluster; contains the p -#' values of pairwise migration indices. -#' @slot pIndex.sig.tran data.frame. Each line for a cluster; contains the p -#' values of pairwise transition indices. -#' @slot objects list. other objects -#' @name StartracOut -#' @rdname StartracOut -#' @aliases StartracOut-class -#' @exportClass StartracOut -#' @keywords internal -StartracOut <- setClass("StartracOut", - slots = c(proj = "character", - cluster.data = "data.frame", - cluster.sig.data = "data.frame", - pIndex.migr = "data.frame", - pIndex.tran = "data.frame", - pIndex.sig.migr = "data.frame", - pIndex.sig.tran = "data.frame", - objects = "list")) - -#' initialize method for StartracOut -# -#' @param .Object A StartracOut object -#' @param proj character analysis id -#' @aliases initialize,StartracOut-method -#' @docType methods -#' @keywords internal -#' @return an object of class \code{StartracOut} -setMethod("initialize", - signature = "StartracOut", - definition = function(.Object, proj="AID"){ - .Object@proj <- proj - return(.Object) - } -) - - -#' show method for StartracOut -# -#' @importFrom utils head -#' @param object A StartracOut object -#' @aliases show,StartracOut-method -#' @docType methods -#' @keywords internal -setMethod("show", - signature = "StartracOut", - definition = function(object) { - cat(sprintf("An object of class %s, proj %s:\n", - class(object), - object@proj)) - cat("head of the clusters' index:\n") - print(head(object@cluster.data)) - cat("head of the pairwise migration index:\n") - print(head(object@pIndex.migr)) - cat("head of the pairwise transition index:\n") - print(head(object@pIndex.tran[,seq_len(min(5,ncol(object@pIndex.tran)))])) - invisible(x = NULL) - } -) - - - -#' dispaly message with time stamp -#' @param msg characters; message to display -#' @keywords internal -#' @return Estimate of sys.time -loginfo <- function(msg) { - timestamp <- sprintf("%s", Sys.time()) - msg <- paste0("[",timestamp, "] ", msg,"\n") - cat(msg) + + pIndex.matrix <- list("tran" = pIndex.tran, + "migr" = pIndex.migr) + return(pIndex.matrix) } #' entropy of each row of the input matrix @@ -534,82 +240,3 @@ mcol.entropy <- function(x) H <- - colSums(ifelse(freqs>0,freqs* log2(freqs),0)) return(H) } - -#' wrapper function for Startrac analysis -#' @importFrom reshape2 dcast -#' @importFrom plyr ldply adply llply -#' @importFrom parallel makeCluster stopCluster -#' @importFrom doParallel registerDoParallel -#' @importFrom methods new slot slot<- new validObject -#' @param cell.data data.frame. Each line for a cell, and these columns as required: `Cell_Name`, `clone.id`, `patient`, `majorCluster`, `loc` -#' @param proj character. String used to annotate the project. -#' @param cores integer. number of core to be used. default: NULL. -#' @param n.perm integer. number of permutation will be performed. If NULL, no permutation. (default: NULL) -#' @param verbose logical. whether return intermediate result (some Startrac objects) -#' @details run the Startrac pipeline -#' @keywords internal -#' @return an list contains data.frame elements "cluster.data","pIndex.migr" and "pIndex.tran" - -Startrac.run <- function(cell.data, proj="CRC", cores=NULL,n.perm=NULL,verbose=FALSE) -{ - ##tic("obj.proj") - loginfo("initialize Startrac ...") - obj.proj <- new("Startrac",cell.data,aid=proj,n.perm=n.perm,cores=cores) - loginfo("calculate startrac index ...") - obj.proj <- calIndex(obj.proj,cores=cores,n.perm=n.perm) - loginfo("calculate pairwise index ...") - obj.proj <- pIndex(obj.proj,cores=cores,n.perm=n.perm) - if(!is.null(n.perm)){ - loginfo("get the significance") - obj.proj <- getSig(obj.proj,obj.proj@cell.perm.data) - }else{ - obj.proj <- getSig(obj.proj,NULL) - } - ##toc() - - obj.list <- NULL - if(length(obj.proj@patient.size)>1) - { - loginfo("calculate indices of each patient ...") - patient.vec <- names(obj.proj@patient.size[obj.proj@patient.size > 30]) - #cl <- makeCluster(if(is.null(cores)) (detectCores()-2) else cores) - #registerDoParallel(cl) - withCallingHandlers({ - obj.list <- llply(patient.vec,function(pid,cell.data){ - obj <- new("Startrac",subset(cell.data,patient==pid),aid=pid) - obj <- calIndex(obj) - obj <- pIndex(obj,cores=1) - obj <- getSig(obj,NULL) - obj - },cell.data=cell.data,.progress = "none",.parallel=FALSE) - },warning=function(w) { - if(grepl("... may be used in an incorrect context:",conditionMessage(w))) - ### strange bug, see https://github.com/hadley/plyr/issues/203 - invokeRestart("muffleWarning") - }) - #stopCluster(cl) - } - loginfo("collect result") - ret <- new("StartracOut",proj=proj) - ## cluster index - ret.slot.names <- c("cluster.data","pIndex.migr","pIndex.tran", - "cluster.sig.data","pIndex.sig.migr","pIndex.sig.tran") - # if(!is.null(n.perm)){ - # ret.slot.names <- c(ret.slot.names, - # c("cluster.sig.data","pIndex.sig.migr","pIndex.sig.tran")) - # } - for(v in ret.slot.names) - { - slot(ret, v) <- slot(obj.proj,v) - if(!is.null(obj.list)){ - slot(ret, v) <- rbind(slot(ret, v),ldply(obj.list,function(obj){ - slot(obj,v) - })) - } - } - if(verbose){ - ret@objects <- c(obj.proj,obj.list) - } - loginfo("return") - return(ret) -} From da0e0cc229ff11c88a365263cb7d4d8bbcca35d8 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 13 Sep 2023 13:22:19 -0500 Subject: [PATCH 040/142] Update startrac.R Clean --- R/startrac.R | 143 +++++++++++---------------------------------------- 1 file changed, 30 insertions(+), 113 deletions(-) diff --git a/R/startrac.R b/R/startrac.R index 38411223..1416f76a 100644 --- a/R/startrac.R +++ b/R/startrac.R @@ -35,20 +35,19 @@ #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param type The variable in the meta data that provides tissue type #' @param group.by The variable in the meta data to group by, often samples -#' @param metric Calculate the "standard" values across the group.by variable -#' or "pairwise" values #' @param exportTable Returns the data frame used for forming the graph #' @importFrom reshape2 melt #' @import ggplot2 #' @export #' @return ggplot object of Startrac diversity metrics +#' @author Liangtao Zheng StartracDiversity <- function(sc, cloneCall = "strict", chain = "both", - type = "Type", + type = NULL, group.by = NULL, - metric = "standard", - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { cloneCall <- theCall(cloneCall) df <- grabMeta(sc) @@ -61,7 +60,7 @@ StartracDiversity <- function(sc, } group.by <- "orig.ident" } - group.levels = unique(dat[,group.by]) + group.levels = unique(df[,group.by]) cloneCall <- theCall(cloneCall) if (chain != "both") { @@ -79,151 +78,72 @@ StartracDiversity <- function(sc, df <- df[-remove.pos,] } df[,"clone.status"] <- ifelse(df[,"n"] > 1, "Yes", "No") - - if (is.null(sample)) { - stop("Must Add the sample information in order to make - the StarTrac calculations") - } else { - processed <- data.frame(rownames(df), df[,cloneCall], df$clone.status, + + processed <- data.frame(rownames(df), df[,cloneCall], df$clone.status, df[,group.by], df[,"majorCluster"], df[,type], stringsAsFactors = FALSE) - colnames(processed) <- c("Cell_Name", "clone.id", "clone.status", "patient", + colnames(processed) <- c("Cell_Name", "clone.id", "clone.status", "patient", "majorCluster", "loc") - } processed[processed == "NA"] <- NA processed <- na.omit(processed) - indexFunc <- switch(metric, - "standard" = .calIndex, - "pairwise" = .pIndex, - stop("Invalid metric provided")) mat.list <- list() for(i in seq_along(group.levels)) { - mat.list[[i]] <- as.data.frame(indexFunc(processed[processed[,4] == group.levels[i],])) + mat.list[[i]] <- as.data.frame(.calIndex(processed[processed[,4] == group.levels[i],])) } names(mat.list) <- group.levels - if(metric == "standard") { - mat <- bind_rows(mat.list, .id = "group") - } else if (metric == "pairwise") { - tran.list <- bind_rows(mat) - } - + mat <- bind_rows(mat.list, .id = "group") if (exportTable) { return(mat) } - - mat_melt <- melt(mat) + mat_melt <- melt(mat, id = c("group", "majorCluster")) values <- as.character(unique(mat_melt$majorCluster)) - values2 <- quiet(dput(values)) - mat_melt$majorCluster <- factor(mat_melt$majorCluster, levels = values2) + values <- quiet(dput(values)) + mat_melt$majorCluster <- factor(mat_melt$majorCluster, levels = values) + mat_melt$value <- as.numeric(mat_melt$value) + col <- length(unique(mat_melt[,"majorCluster"])) plot <- ggplot(mat_melt, aes(x=majorCluster, y=value)) + - geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0) + - facet_grid(variable ~.) + - theme_classic() + - ylab("Index Score") + - guides(fill="none") + - theme(axis.title.x = element_blank()) - - - + geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0) + + facet_grid(variable ~.) + + theme_classic() + + ylab("Index Score") + + guides(fill="none") + + scale_fill_manual(values = .colorizer(palette, col)) + + theme(axis.title.x = element_blank()) + return(plot) } #' Calculate cluster level indices #' @importFrom plyr llply -#' @keywords internal .calIndex <- function(processed){ clonotype.dist.cluster <- table(processed[,c("clone.id","majorCluster")]) clonotype.dist.loc <- unclass(table(processed[,c("clone.id","loc")])) - .entropy <- mcol.entropy(clonotype.dist.cluster) + .entropy <- .mcol.entropy(clonotype.dist.cluster) .entropy.max <- log2(colSums(clonotype.dist.cluster > 0)) expa <- 1-.entropy/.entropy.max .entropy.migr.max <- 1 .entropy.tran.max <- 1 - clonotype.data <- data.frame("clone.id"=rownames(clonotype.dist.loc), - "migr"=mrow.entropy(clonotype.dist.loc)/.entropy.migr.max, - "tran"=mrow.entropy(clonotype.dist.cluster)/.entropy.tran.max) + "migr"=.mrow.entropy(clonotype.dist.loc)/.entropy.migr.max, + "tran"=.mrow.entropy(clonotype.dist.cluster)/.entropy.tran.max) weights.mtx <- sweep(clonotype.dist.cluster,2,colSums(clonotype.dist.cluster),"/") calIndex.matrix <- t(weights.mtx) %*% (as.matrix(clonotype.data[,c("migr","tran")])) calIndex.matrix <- cbind(calIndex.matrix, expa = expa) calIndex.matrix[is.nan(calIndex.matrix)] <- NA + calIndex.matrix <- cbind(rownames(calIndex.matrix), calIndex.matrix) + colnames(calIndex.matrix)[1] <- "majorCluster" return(calIndex.matrix) } - - -#' Calculate pairwise indices -#' @importFrom reshape2 dcast -#' @importFrom plyr ldply adply -#' @importFrom parallel makeCluster stopCluster detectCores -#' @importFrom doParallel registerDoParallel -#' @importFrom utils combn -.pIndex <- function(processed){ - clone.dist.loc.majorCluster <- table(processed[,c("majorCluster","clone.id","loc")]) - - cls.migr.index.df <- ldply(seq_len(dim(clone.dist.loc.majorCluster)[1]), - function(i,clone.dist.loc.majorCluster){ - dat.cls <- clone.dist.loc.majorCluster[i,,] - i.name <- dimnames(clone.dist.loc.majorCluster)[["majorCluster"]][i] - dat.cls.index <- NULL - if(!is.null(ncol(dat.cls)) && ncol(dat.cls)>=2){ - comb.loc <- as.data.frame(t(combn(colnames(dat.cls),2)),stringsAsFactors=FALSE) - dat.cls.pIndex.migr <- apply(comb.loc,1,function(x){ - dat.block <- dat.cls[,x] - dat.block.clone.index <- mrow.entropy(dat.block) - dat.block.clone.index[is.na(dat.block.clone.index)] <- 0 - t(rowSums(dat.block)/sum(dat.block)) %*% dat.block.clone.index - }) - dat.cls.index <- cbind(data.frame(majorCluster=rep(i.name,nrow(comb.loc)), - pIndex.migr=dat.cls.pIndex.migr, - stringsAsFactors = FALSE), - comb.loc) - } - return(dat.cls.index) - },clone.dist.loc.majorCluster=clone.dist.loc.majorCluster,.progress = "none",.parallel=FALSE) - - if(!is.null(cls.migr.index.df) && nrow(cls.migr.index.df)>0){ - cls.migr.index.df$crossLoc <- sprintf("%s-%s",cls.migr.index.df$V1,cls.migr.index.df$V2) - pIndex.migr <- dcast(cls.migr.index.df,majorCluster ~ crossLoc,value.var = "pIndex.migr") - }else{ - pIndex.migr <- data.frame() - } - clonotype.dist.cluster <- table(processed[,c("clone.id","majorCluster")]) - if(!is.null(ncol(clonotype.dist.cluster)) && ncol(clonotype.dist.cluster)>=2){ - comb.cls <- expand.grid(colnames(clonotype.dist.cluster), - colnames(clonotype.dist.cluster),stringsAsFactors = FALSE) - comb.cls <- comb.cls[comb.cls[,1]!=comb.cls[,2],] - cls.tran.index.df <- adply(comb.cls,1,function(x,object){ - dat.block <- clonotype.dist.cluster[,c(x[[1]],x[[2]])] - dat.block.clone.index <- mrow.entropy(dat.block) - dat.block.clone.index[is.na(dat.block.clone.index)] <- 0 - data.frame(pIndex.tran= t(rowSums(dat.block)/sum(dat.block)) %*% dat.block.clone.index) - },object=object,.progress = "none",.parallel=FALSE) - - pIndex.tran <- as.data.frame(dcast(cls.tran.index.df,Var2~Var1,value.var = "pIndex.tran")) - colnames(pIndex.tran)[1] <- "majorCluster" - - } else{ - pIndex.tran <- data.frame() - } - - pIndex.matrix <- list("tran" = pIndex.tran, - "migr" = pIndex.migr) - return(pIndex.matrix) -} - #' entropy of each row of the input matrix -#' @param x matrix; -#' @keywords internal -#' @return row entropy -mrow.entropy <- function(x) +.mrow.entropy <- function(x) { freqs <- sweep(x,1,rowSums(x),"/") H <- - rowSums(ifelse(freqs>0,freqs* log2(freqs),0)) @@ -231,10 +151,7 @@ mrow.entropy <- function(x) } #' entropy of each column of the input matrix -#' @param x matrix; -#' @keywords internal -#' @return column entropy -mcol.entropy <- function(x) +.mcol.entropy <- function(x) { freqs <- sweep(x,2,colSums(x),"/") H <- - colSums(ifelse(freqs>0,freqs* log2(freqs),0)) From 698ea4f5e8f5fd66968e8ca6e547f380be9da682 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 13 Sep 2023 13:46:16 -0500 Subject: [PATCH 041/142] Update after new check() Udate manual --- DESCRIPTION | 2 - NAMESPACE | 15 +------ NEWS | 7 ++- R/clonalDiversity.R | 2 +- R/clonalOverlap.R | 1 - R/{startrac.R => startracDiversity.R} | 15 ++++--- R/utils.R | 1 + man/Startrac.Rd | 61 --------------------------- man/Startrac.run.Rd | 35 --------------- man/StartracDiversity.Rd | 44 +++++++++++-------- man/StartracOut.Rd | 39 ----------------- man/calIndex.Rd | 28 ------------ man/clonalOverlap.Rd | 9 ++-- man/getSig.Rd | 24 ----------- man/initialize-StartracOut-method.Rd | 21 --------- man/initialize-methods.Rd | 28 ------------ man/loginfo.Rd | 18 -------- man/mcol.entropy.Rd | 18 -------- man/mrow.entropy.Rd | 18 -------- man/pIndex.Rd | 26 ------------ man/show-StartracOut-method.Rd | 16 ------- man/show.Rd | 20 --------- vignettes/vignette.Rmd | 3 +- 23 files changed, 52 insertions(+), 399 deletions(-) rename R/{startrac.R => startracDiversity.R} (93%) delete mode 100644 man/Startrac.Rd delete mode 100644 man/Startrac.run.Rd delete mode 100644 man/StartracOut.Rd delete mode 100644 man/calIndex.Rd delete mode 100644 man/getSig.Rd delete mode 100644 man/initialize-StartracOut-method.Rd delete mode 100644 man/initialize-methods.Rd delete mode 100644 man/loginfo.Rd delete mode 100644 man/mcol.entropy.Rd delete mode 100644 man/mrow.entropy.Rd delete mode 100644 man/pIndex.Rd delete mode 100644 man/show-StartracOut-method.Rd delete mode 100644 man/show.Rd diff --git a/DESCRIPTION b/DESCRIPTION index 1262cdae..3d07b198 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -23,8 +23,6 @@ Imports: SingleCellExperiment, SummarizedExperiment, plyr, - parallel, - doParallel, methods, utils, rlang, diff --git a/NAMESPACE b/NAMESPACE index a9d65928..e2905413 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -32,7 +32,6 @@ export(quantContig) export(scatterClonotype) export(subsetContig) export(vizGenes) -exportClasses(StartracOut) import(dplyr) import(ggplot2) importFrom(Rcpp,sourceCpp) @@ -42,7 +41,6 @@ importFrom(SingleCellExperiment,colData) importFrom(SingleCellExperiment,reducedDim) importFrom(SummarizedExperiment,"colData<-") importFrom(SummarizedExperiment,colData) -importFrom(doParallel,registerDoParallel) importFrom(dplyr,"%>%") importFrom(dplyr,bind_rows) importFrom(dplyr,count) @@ -69,17 +67,7 @@ importFrom(igraph,V) importFrom(igraph,`V<-`) importFrom(igraph,components) importFrom(igraph,graph_from_data_frame) -importFrom(methods,"slot<-") -importFrom(methods,new) -importFrom(methods,slot) -importFrom(methods,validObject) -importFrom(parallel,detectCores) -importFrom(parallel,makeCluster) -importFrom(parallel,stopCluster) -importFrom(plyr,adply) importFrom(plyr,join) -importFrom(plyr,laply) -importFrom(plyr,ldply) importFrom(plyr,llply) importFrom(powerTCR,fdiscgammagpd) importFrom(powerTCR,get_distances) @@ -96,8 +84,7 @@ importFrom(stringr,str_replace_all) importFrom(stringr,str_replace_na) importFrom(stringr,str_sort) importFrom(stringr,str_split) +importFrom(stringr,str_to_title) importFrom(tidygraph,activate) importFrom(tidygraph,as_tbl_graph) -importFrom(utils,combn) -importFrom(utils,head) useDynLib(scRepertoire, .registration = TRUE) diff --git a/NEWS b/NEWS index 8d9a282b..37a5d3a1 100644 --- a/NEWS +++ b/NEWS @@ -1,12 +1,17 @@ CHANGES IN VERSION 2.0.0 ------------------------ -SIGNIFICANT USER-VISIBLE CHANGES +NEW FEATURES * Added percentAA() * Added percentGenes() * Added percentVJ() +UNDERLYING CHANGES + +* startrac diversity metric re-implemented to remove startrac-class object intermediary +* Universalized underlying function language and intermediate variables + DEPRECATED AND DEFUNCT diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 2421f1c3..47b81ce8 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -34,7 +34,7 @@ #' will automatically exportTable = TRUE #' @param skip.boots remove downsampling and boot strapping from the calculation #' @importFrom stringr str_sort str_split -#' @importFrom reshape2 mat_melt +#' @importFrom reshape2 melt #' @importFrom dplyr sample_n #' @import ggplot2 #' @export diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index c104aaa3..be1fbd95 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -41,7 +41,6 @@ clonalOverlap <- function(df, method = NULL, chain = "both", split.by = NULL, - text.color = NULL, exportTable = FALSE, palette = "inferno"){ if(method == "morisita") { diff --git a/R/startrac.R b/R/startracDiversity.R similarity index 93% rename from R/startrac.R rename to R/startracDiversity.R index 1416f76a..6feaadd4 100644 --- a/R/startrac.R +++ b/R/startracDiversity.R @@ -16,13 +16,15 @@ #' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object -#' scRep_example <- get(data("scRep_example ")) +#' scRep_example <- get(data("scRep_example")) #' scRep_example <- combineExpression(combined, scRep_example ) #' scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #' scRep_example$Type <- substring(scRep_example$orig.ident,4,4) #' -#' #Using occupiedscRepertoire() -#' StartracDiversity(scRep_example , type = "Type", sample = "Patient", by = "overall") +#' #Using StartracDiversity() +#' StartracDiversity(scRep_example, +#' type = "Type", +#' group.by = "Patient") #' #' #' @param sc The seurat or SCE object to visualize after combineExpression(). @@ -36,6 +38,7 @@ #' @param type The variable in the meta data that provides tissue type #' @param group.by The variable in the meta data to group by, often samples #' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom reshape2 melt #' @import ggplot2 #' @export @@ -117,7 +120,7 @@ StartracDiversity <- function(sc, } -#' Calculate cluster level indices +# Calculate cluster level indices #' @importFrom plyr llply .calIndex <- function(processed){ clonotype.dist.cluster <- table(processed[,c("clone.id","majorCluster")]) @@ -142,7 +145,7 @@ StartracDiversity <- function(sc, return(calIndex.matrix) } -#' entropy of each row of the input matrix +# entropy of each row of the input matrix .mrow.entropy <- function(x) { freqs <- sweep(x,1,rowSums(x),"/") @@ -150,7 +153,7 @@ StartracDiversity <- function(sc, return(H) } -#' entropy of each column of the input matrix +# entropy of each column of the input matrix .mcol.entropy <- function(x) { freqs <- sweep(x,2,colSums(x),"/") diff --git a/R/utils.R b/R/utils.R index e14a2db3..fcffa1fd 100644 --- a/R/utils.R +++ b/R/utils.R @@ -129,6 +129,7 @@ checkSingleObject <- function(sc) { #This is to grab the meta data from a seurat or SCE object #' @importFrom SingleCellExperiment colData #' @importFrom SeuratObject Idents +#' @importFrom methods slot grabMeta <- function(sc) { if (is_seurat_object(sc)) { meta <- data.frame(sc[[]], slot(sc, "active.ident")) diff --git a/man/Startrac.Rd b/man/Startrac.Rd deleted file mode 100644 index 2f354b11..00000000 --- a/man/Startrac.Rd +++ /dev/null @@ -1,61 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\docType{class} -\name{Startrac} -\alias{Startrac} -\alias{Startrac-class} -\title{The Startrac Class} -\value{ -method definition for running startrac -} -\description{ -The Startrac object store the data for tcr-based T cell dynamics analysis. -} -\section{Slots}{ - -\describe{ -\item{\code{aid}}{character. aid of the object, used for identification of the object. -For example, patient id. default: "AID"} - -\item{\code{cell.data}}{data.frame. Each line for a cell, and these columns as -required: `Cell_Name`, `clone.id`, `patient`, `majorCluster`, `loc`} - -\item{\code{cell.perm.data}}{object. list of `Startrac`` objects constructed from -permutated cell data} - -\item{\code{clonotype.data}}{data.frame. Each line for a clonotype; contain the -clonotype level indexes information} - -\item{\code{cluster.data}}{data.frame. Each line for a cluster; contain the cluster -level indexes information} - -\item{\code{pIndex.migr}}{data.frame. Each line for a cluster; pairwise migration -index between the two locations indicated in the column name.} - -\item{\code{pIndex.tran}}{data.frame. Each line for a cluster; pairwise transition -index between the two major clusters indicated by the row name and column name.} - -\item{\code{cluster.sig.data}}{data.frame. Each line for a cluster; contains the -p values of cluster indices.} - -\item{\code{pIndex.sig.migr}}{data.frame. Each line for a cluster; contains the -p values of pairwise migration indices.} - -\item{\code{pIndex.sig.tran}}{data.frame. Each line for a cluster; contains the -p values of pairwise transition indices.} - -\item{\code{clonotype.dist.loc}}{matrix. Each line for a clonotype and describe -the cells distribution among the locations.} - -\item{\code{clonotype.dist.cluster}}{matrix. Each line for a clonotype and -describe the cells distribution among the clusters.} - -\item{\code{clust.size}}{array. Number of cells of each major cluster.} - -\item{\code{patient.size}}{array. Number of cells of each patient.} - -\item{\code{clone.size}}{array. Number of cells of each clone.} - -\item{\code{clone2patient}}{array. Mapping from patient id to clone id.} -}} - diff --git a/man/Startrac.run.Rd b/man/Startrac.run.Rd deleted file mode 100644 index 8bd184c0..00000000 --- a/man/Startrac.run.Rd +++ /dev/null @@ -1,35 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{Startrac.run} -\alias{Startrac.run} -\title{wrapper function for Startrac analysis} -\usage{ -Startrac.run( - cell.data, - proj = "CRC", - cores = NULL, - n.perm = NULL, - verbose = FALSE -) -} -\arguments{ -\item{cell.data}{data.frame. Each line for a cell, and these columns as required: `Cell_Name`, `clone.id`, `patient`, `majorCluster`, `loc`} - -\item{proj}{character. String used to annotate the project.} - -\item{cores}{integer. number of core to be used. default: NULL.} - -\item{n.perm}{integer. number of permutation will be performed. If NULL, no permutation. (default: NULL)} - -\item{verbose}{logical. whether return intermediate result (some Startrac objects)} -} -\value{ -an list contains data.frame elements "cluster.data","pIndex.migr" and "pIndex.tran" -} -\description{ -wrapper function for Startrac analysis -} -\details{ -run the Startrac pipeline -} -\keyword{internal} diff --git a/man/StartracDiversity.Rd b/man/StartracDiversity.Rd index 0c1e66e9..b3907610 100644 --- a/man/StartracDiversity.Rd +++ b/man/StartracDiversity.Rd @@ -1,15 +1,17 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R +% Please edit documentation in R/startracDiversity.R \name{StartracDiversity} \alias{StartracDiversity} -\title{Diversity indices for single-cell RNA-seq} +\title{Startrac-based diversity indices for single-cell RNA-seq} \usage{ StartracDiversity( sc, - type = "Type", - sample = NULL, - by = "overall", - exportTable = FALSE + cloneCall = "strict", + chain = "both", + type = NULL, + group.by = NULL, + exportTable = FALSE, + palette = "inferno" ) } \arguments{ @@ -17,14 +19,16 @@ StartracDiversity( For SCE objects, the cluster variable must be in the meta data under "cluster".} -\item{type}{The column header in the meta data that gives the where the -cells were derived from, not the patient sample IDs} +\item{cloneCall}{How to call the clonotype - VDJC gene (gene), +CDR3 nucleotide (nt), CDR3 amino acid (aa), or +VDJC gene + CDR3 nucleotide (strict).} -\item{sample}{The column header corresponding to individual samples -or patients.} +\item{chain}{indicate if both or a specific chain should be used - +e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{by}{Method to subset the indices by either overall -(across all samples) or by specific group} +\item{type}{The variable in the meta data that provides tissue type} + +\item{group.by}{The variable in the meta data to group by, often samples} \item{exportTable}{Returns the data frame used for forming the graph} } @@ -32,7 +36,7 @@ or patients.} ggplot object of Startrac diversity metrics } \description{ -This function utilizes the Startrac R package derived from +This function utilizes the Startrac approach derived from \href{https://pubmed.ncbi.nlm.nih.gov/30479382/}{PMID: 30479382} Required to run the function, the "type" variable needs to include the difference in where the cells were derived. The output of this function @@ -42,7 +46,6 @@ to understand the underlying analyses of the outputs please read and cite the linked manuscript. } \examples{ -\dontrun{ #Getting the combined contigs combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", @@ -51,9 +54,16 @@ combined <- combineTCR(contig_list, #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example ")) scRep_example <- combineExpression(combined, scRep_example ) +scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) +scRep_example$Type <- substring(scRep_example$orig.ident,4,4) -#Using occupiedscRepertoire() -StartracDiversity(scRep_example , type = "Type", sample = "Patient", by = "overall") -} +#Using StartracDiversity() +StartracDiversity(scRep_example, + type = "Type", + group.by = "Patient") + +} +\author{ +Liangtao Zheng } diff --git a/man/StartracOut.Rd b/man/StartracOut.Rd deleted file mode 100644 index 0e8378d5..00000000 --- a/man/StartracOut.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\docType{class} -\name{StartracOut} -\alias{StartracOut} -\alias{StartracOut-class} -\title{The StartracOUt Class} -\description{ -Object store the result of Startrac.run: -} -\section{Slots}{ - -\describe{ -\item{\code{proj}}{character. identification of the object. For example, patient id. -default: "AID"} - -\item{\code{cluster.data}}{data.frame. Each line for a cluster; contain the cluster -level indexes information} - -\item{\code{pIndex.migr}}{data.frame. Each line for a cluster; pairwise migration -index between the two locations indicated in the column name.} - -\item{\code{pIndex.tran}}{data.frame. Each line for a cluster; pairwise transition -index between the two major clusters indicated by the row name and column -name.} - -\item{\code{cluster.sig.data}}{data.frame. Each line for a cluster; contains the p -values of cluster indices.} - -\item{\code{pIndex.sig.migr}}{data.frame. Each line for a cluster; contains the p -values of pairwise migration indices.} - -\item{\code{pIndex.sig.tran}}{data.frame. Each line for a cluster; contains the p -values of pairwise transition indices.} - -\item{\code{objects}}{list. other objects} -}} - -\keyword{internal} diff --git a/man/calIndex.Rd b/man/calIndex.Rd deleted file mode 100644 index 8726f032..00000000 --- a/man/calIndex.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{calIndex} -\alias{calIndex} -\alias{Startrac.calIndex} -\alias{calIndex,Startrac-method} -\title{Calculate cluster level indices} -\usage{ -Startrac.calIndex(object, cores, n.perm, normEntropy) - -\S4method{calIndex}{Startrac}(object, cores = NULL, n.perm = NULL, normEntropy = FALSE) -} -\arguments{ -\item{object}{A Startrac object} - -\item{cores}{number of core to be used. Passed to doParallel registerDoParallel. default: NULL.} - -\item{n.perm}{integer number of permutation will be performed. If NULL, no permutation. (default: NULL)} - -\item{normEntropy}{logical; whether normalize migration and transition index. default: FALSE.} -} -\value{ -an object of class \code{Startrac} -} -\description{ -Calculate cluster level indices -} -\keyword{internal} diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 4a587233..206e59b7 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -10,6 +10,7 @@ clonalOverlap( method = NULL, chain = "both", split.by = NULL, + text.color = NULL, exportTable = FALSE, palette = "inferno" ) @@ -21,8 +22,8 @@ clonalOverlap( CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} -\item{method}{The method to calculate the overlap, either the "overlap" -coefficient, "morisita", "jaccard" indices, "cosine" similarity or "raw" +\item{method}{The method to calculate the overlap, + "overlap" for the base numbers.} \item{chain}{indicate if both or a specific chain should be used - @@ -52,6 +53,8 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -clonalOverlap(combined, cloneCall = "gene", method = "overlap") +clonalOverlap(combined, + cloneCall = "gene", + method = "jaccard") } diff --git a/man/getSig.Rd b/man/getSig.Rd deleted file mode 100644 index 8d44cdae..00000000 --- a/man/getSig.Rd +++ /dev/null @@ -1,24 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{getSig} -\alias{getSig} -\alias{Startrac.getSig} -\alias{getSig,Startrac-method} -\title{Get the p value given one Startrac object and a list of Startrac objects from permutation data} -\usage{ -Startrac.getSig(obj, obj.perm) - -\S4method{getSig}{Startrac}(obj, obj.perm = NULL) -} -\arguments{ -\item{obj}{A Startrac object} - -\item{obj.perm}{A list of Startrac objects from permutation data} -} -\value{ -an object of class \code{Startrac} -} -\description{ -Get the p value given one Startrac object and a list of Startrac objects from permutation data -} -\keyword{internal} diff --git a/man/initialize-StartracOut-method.Rd b/man/initialize-StartracOut-method.Rd deleted file mode 100644 index 25b132a3..00000000 --- a/man/initialize-StartracOut-method.Rd +++ /dev/null @@ -1,21 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\docType{methods} -\name{initialize,StartracOut-method} -\alias{initialize,StartracOut-method} -\title{initialize method for StartracOut} -\usage{ -\S4method{initialize}{StartracOut}(.Object, proj = "AID") -} -\arguments{ -\item{.Object}{A StartracOut object} - -\item{proj}{character analysis id} -} -\value{ -an object of class \code{StartracOut} -} -\description{ -initialize method for StartracOut -} -\keyword{internal} diff --git a/man/initialize-methods.Rd b/man/initialize-methods.Rd deleted file mode 100644 index 9a374cf0..00000000 --- a/man/initialize-methods.Rd +++ /dev/null @@ -1,28 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\docType{methods} -\name{initialize} -\alias{initialize} -\alias{initialize,Startrac-method} -\title{initialize method for Startrac} -\usage{ -\S4method{initialize}{Startrac}(.Object, cell.data, aid = "AID", n.perm = NULL, cores = NULL) -} -\arguments{ -\item{.Object}{A Startrac object} - -\item{cell.data}{data.frame contains the input data} - -\item{aid}{character analysis id} - -\item{n.perm}{integer number of permutation will be performed. If NULL, no permutation. (default: NULL)} - -\item{cores}{number of core to be used. Passed to doParallel registerDoParallel. default: NULL.} -} -\value{ -an object of class \code{Startrac} -} -\description{ -initialize method for Startrac -} -\keyword{internal} diff --git a/man/loginfo.Rd b/man/loginfo.Rd deleted file mode 100644 index 8ef44bb2..00000000 --- a/man/loginfo.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{loginfo} -\alias{loginfo} -\title{dispaly message with time stamp} -\usage{ -loginfo(msg) -} -\arguments{ -\item{msg}{characters; message to display} -} -\value{ -Estimate of sys.time -} -\description{ -dispaly message with time stamp -} -\keyword{internal} diff --git a/man/mcol.entropy.Rd b/man/mcol.entropy.Rd deleted file mode 100644 index 614fa146..00000000 --- a/man/mcol.entropy.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{mcol.entropy} -\alias{mcol.entropy} -\title{entropy of each column of the input matrix} -\usage{ -mcol.entropy(x) -} -\arguments{ -\item{x}{matrix;} -} -\value{ -column entropy -} -\description{ -entropy of each column of the input matrix -} -\keyword{internal} diff --git a/man/mrow.entropy.Rd b/man/mrow.entropy.Rd deleted file mode 100644 index 5967aa7b..00000000 --- a/man/mrow.entropy.Rd +++ /dev/null @@ -1,18 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{mrow.entropy} -\alias{mrow.entropy} -\title{entropy of each row of the input matrix} -\usage{ -mrow.entropy(x) -} -\arguments{ -\item{x}{matrix;} -} -\value{ -row entropy -} -\description{ -entropy of each row of the input matrix -} -\keyword{internal} diff --git a/man/pIndex.Rd b/man/pIndex.Rd deleted file mode 100644 index 1569c32c..00000000 --- a/man/pIndex.Rd +++ /dev/null @@ -1,26 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\name{pIndex} -\alias{pIndex} -\alias{Startrac.pIndex} -\alias{pIndex,Startrac-method} -\title{Calculate pairwise indices} -\usage{ -Startrac.pIndex(object, cores, n.perm) - -\S4method{pIndex}{Startrac}(object, cores = NULL, n.perm = NULL) -} -\arguments{ -\item{object}{A Startrac object} - -\item{cores}{number of core to be used. Passed to doParallel registerDoParallel. default: NULL.} - -\item{n.perm}{integer number of permutation will be performed. If NULL, no permutation. (default: NULL)} -} -\value{ -an object of class \code{Startrac} -} -\description{ -Calculate pairwise indices -} -\keyword{internal} diff --git a/man/show-StartracOut-method.Rd b/man/show-StartracOut-method.Rd deleted file mode 100644 index b0a45f3e..00000000 --- a/man/show-StartracOut-method.Rd +++ /dev/null @@ -1,16 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\docType{methods} -\name{show,StartracOut-method} -\alias{show,StartracOut-method} -\title{show method for StartracOut} -\usage{ -\S4method{show}{StartracOut}(object) -} -\arguments{ -\item{object}{A StartracOut object} -} -\description{ -show method for StartracOut -} -\keyword{internal} diff --git a/man/show.Rd b/man/show.Rd deleted file mode 100644 index 02281c60..00000000 --- a/man/show.Rd +++ /dev/null @@ -1,20 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/startrac.R -\docType{methods} -\name{show} -\alias{show} -\alias{show,Startrac-method} -\title{show method for Startrac} -\usage{ -\S4method{show}{Startrac}(object) -} -\arguments{ -\item{object}{A Startrac object} -} -\value{ -method for show -} -\description{ -show method for Startrac -} -\keyword{internal} diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 9cdeff7b..044c228f 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -778,8 +778,7 @@ In order to use the ```StartracDiversity()``` function, you will need to include ```{r} StartracDiversity(scRep_example, type = "Type", - sample = "Patient", - by = "overall") + group.by = "Patient") ``` ## clonotypeBias From b73bc9a68d3d9c595d061a936ab4b91da21a71a2 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 14 Sep 2023 05:21:58 -0500 Subject: [PATCH 042/142] clonalCompare --- NAMESPACE | 3 +- NEWS | 2 + R/{compareClonotypes.R => clonalCompare.R} | 85 ++++++++++++++----- man/StartracDiversity.Rd | 4 +- ...{compareClonotypes.Rd => clonalCompare.Rd} | 26 ++++-- man/clonalOverlap.Rd | 1 - vignettes/vignette.Rmd | 30 +++++-- 7 files changed, 111 insertions(+), 40 deletions(-) rename R/{compareClonotypes.R => clonalCompare.R} (55%) rename man/{compareClonotypes.Rd => clonalCompare.Rd} (74%) diff --git a/NAMESPACE b/NAMESPACE index e2905413..9366ac2f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,6 +5,7 @@ export(abundanceContig) export(addVariable) export(alluvialClonotypes) export(checkContig) +export(clonalCompare) export(clonalDiversity) export(clonalHomeostasis) export(clonalNetwork) @@ -17,7 +18,6 @@ export(clusterTCR) export(combineBCR) export(combineExpression) export(combineTCR) -export(compareClonotypes) export(createHTOContigList) export(expression2List) export(getCirclize) @@ -67,6 +67,7 @@ importFrom(igraph,V) importFrom(igraph,`V<-`) importFrom(igraph,components) importFrom(igraph,graph_from_data_frame) +importFrom(methods,slot) importFrom(plyr,join) importFrom(plyr,llply) importFrom(powerTCR,fdiscgammagpd) diff --git a/NEWS b/NEWS index 37a5d3a1..ca3abff6 100644 --- a/NEWS +++ b/NEWS @@ -6,11 +6,13 @@ NEW FEATURES * Added percentAA() * Added percentGenes() * Added percentVJ() +* Changed compareClonotypes to clonalCompare UNDERLYING CHANGES * startrac diversity metric re-implemented to remove startrac-class object intermediary * Universalized underlying function language and intermediate variables +* Added features to clonalCompare to allow for highlighting sequences, relabeling clonotypes. DEPRECATED AND DEFUNCT diff --git a/R/compareClonotypes.R b/R/clonalCompare.R similarity index 55% rename from R/compareClonotypes.R rename to R/clonalCompare.R index cb540a51..3aa7e278 100644 --- a/R/compareClonotypes.R +++ b/R/clonalCompare.R @@ -9,22 +9,27 @@ #' @examples #' #Making combined contig data #' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' compareClonotypes(combined, -#' numbers = 5, -#' samples = c("PX_P", "PX_T"), -#' cloneCall="aa") +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' clonalCompare(combined, +#' top.clones = 5, +#' samples = c("P17B", "P17L"), +#' cloneCall="aa") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of combineTCR(), combineBCR(), expression2List(), +#' or combineExpression(). #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param samples The specific samples to isolate for visualization. -#' @param clonotypes The specific sequences of interest. -#' @param numbers The top number clonotype sequences per group +#' @param clonotypes The specific clonal sequences of interest. +#' @param top.clones The top number of clonotype sequences per group +#' @param highlight.clones Clonal sequences to highlight, if present, +#' all other clones returned will be grey. +#' @param relabel.clones Simplify the legend of the graph by returning +#' clones that are numerically indexed. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param graph The type of graph produced, either "alluvial" or "area". @@ -35,23 +40,30 @@ #' @export #' @return ggplot of the proportion of total sequencing read of #' selecting clonotypes -compareClonotypes <- function(df, +clonalCompare <- function(df, cloneCall = "strict", chain = "both", samples = NULL, clonotypes = NULL, - numbers = NULL, + top.clones = NULL, + highlight.clones = NULL, + relabel.clones = FALSE, split.by = NULL, graph = "alluvial", exportTable = FALSE, palette = "inferno"){ + + #Tie goes to indicated clones over top clones + if(!is.null(top.clones) & !is.null(clonotypes)) { + top.clones <- NULL + } + df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) - if (!is.null(numbers) & !is.null(clonotypes)) { - stop("Make sure your inputs are either numbers or clonotype sequences.") - } Con.df <- NULL + + #Loop through the list to get a proportional summary for (i in seq_along(df)) { if (chain != "both") { df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) @@ -62,34 +74,63 @@ compareClonotypes <- function(df, tbl$Sample <- names(df[i]) Con.df <- rbind.data.frame(Con.df, tbl) } + + #Filtering steps if (!is.null(samples)) { Con.df <- Con.df[Con.df$Sample %in% samples,] } if (!is.null(clonotypes)) { Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] - } - if (!is.null(numbers)) { + } else if (!is.null(top.clones)) { top <- Con.df %>% group_by(Con.df[,3]) %>% - slice_max(n = numbers, order_by = Proportion, with_ties = FALSE) + slice_max(n = top.clones, order_by = Proportion, with_ties = FALSE) Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] } if (nrow(Con.df) < length(unique(Con.df$Sample))) { stop("Reasses the filtering strategies here, there is not - enough clonotypes to examine.") } - if (exportTable == TRUE) { return(Con.df)} + enough clonotypes to examine.") + } + #Clonotype relabeling + clones.returned <- as.vector(unique(Con.df[,"Clonotypes"])) + if (relabel.clones) { + new.clones <- paste0("Clonotype: ", seq_len(length(clones.returned))) + names(new.clones) <- clones.returned + #Isolated new clone names for highlight purposes + if(!is.null(highlight.clones)) { + highlight.clones <- unname(new.clones[which(names(new.clones) %in% highlight.clones)]) + } + Con.df[,"Clonotypes"] <- new.clones[as.vector(Con.df[,"Clonotypes"])] + clones.returned <- as.vector(unique(Con.df[,"Clonotypes"])) + } + if (exportTable == TRUE) { + return(Con.df) + } + #Plotting Functions plot <- ggplot(Con.df, aes(x = Sample, fill = Clonotypes, group = Clonotypes, stratum = Clonotypes, alluvium = Clonotypes, y = Proportion, label = Clonotypes)) + theme_classic() + - theme(axis.title.x = element_blank()) + theme(axis.title.x = element_blank(), + legend.text=element_text(size=rel(0.5)), + legend.key.size = unit(0.5,"line")) if (graph == "alluvial") { plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") } else if (graph == "area") { plot <- plot + geom_area(aes(group = Clonotypes), color = "black") } - plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"Clonotypes"])))) + + #Highlighting specific clones + if (!is.null(highlight.clones)) { + clone.colors <- rep("grey", length(clones.returned)) + pos <- which(clones.returned %in% highlight.clones) + clone.colors[pos] <- .colorizer(palette, length(pos)) + names(clone.colors) <- clones.returned + plot <- plot + scale_fill_manual(values = clone.colors) + } else { + plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"Clonotypes"])))) + } return(plot) -} \ No newline at end of file +} diff --git a/man/StartracDiversity.Rd b/man/StartracDiversity.Rd index b3907610..68e965d9 100644 --- a/man/StartracDiversity.Rd +++ b/man/StartracDiversity.Rd @@ -31,6 +31,8 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{group.by}{The variable in the meta data to group by, often samples} \item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ ggplot object of Startrac diversity metrics @@ -52,7 +54,7 @@ combined <- combineTCR(contig_list, "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object -scRep_example <- get(data("scRep_example ")) +scRep_example <- get(data("scRep_example")) scRep_example <- combineExpression(combined, scRep_example ) scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) scRep_example$Type <- substring(scRep_example$orig.ident,4,4) diff --git a/man/compareClonotypes.Rd b/man/clonalCompare.Rd similarity index 74% rename from man/compareClonotypes.Rd rename to man/clonalCompare.Rd index 50b416e5..3e77a029 100644 --- a/man/compareClonotypes.Rd +++ b/man/clonalCompare.Rd @@ -1,16 +1,18 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/compareClonotypes.R -\name{compareClonotypes} -\alias{compareClonotypes} +% Please edit documentation in R/clonalCompare.R +\name{clonalCompare} +\alias{clonalCompare} \title{Demonstrate the difference in clonal proportion between clonotypes} \usage{ -compareClonotypes( +clonalCompare( df, cloneCall = "strict", chain = "both", samples = NULL, clonotypes = NULL, - numbers = NULL, + top.clones = NULL, + highlight.clones = NULL, + relabel.clones = FALSE, split.by = NULL, graph = "alluvial", exportTable = FALSE, @@ -29,9 +31,15 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{samples}{The specific samples to isolate for visualization.} -\item{clonotypes}{The specific sequences of interest.} +\item{clonotypes}{The specific clonal sequences of interest.} -\item{numbers}{The top number clonotype sequences per group} +\item{top.clones}{The top number of clonotype sequences per group} + +\item{highlight.clones}{Clonal sequences to highlight, if present, +all other clones returned will be grey.} + +\item{relabel.clones}{Simplify the legend of the graph by returning +clones that are numerically indexed.} \item{split.by}{If using a single-cell object, the column header to group the new list. NULL will return clusters.} @@ -59,8 +67,8 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) compareClonotypes(combined, - numbers = 5, - samples = c("PX_P", "PX_T"), + top.clones = 5, + samples = c("P17B", "P17L"), cloneCall="aa") } diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 206e59b7..ba640911 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -10,7 +10,6 @@ clonalOverlap( method = NULL, chain = "both", split.by = NULL, - text.color = NULL, exportTable = FALSE, palette = "inferno" ) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 044c228f..ec28054c 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -287,9 +287,9 @@ lengthContig(combined, scale = TRUE) ``` -## compareClonotypes +## clonalCompare -We can also look at clonotypes between samples and changes in dynamics by using the `compareClonotypes()` function. +We can also look at clonotypes between samples and changes in dynamics by using the `clonalCompare()` function. **samples** * Can be used to isolate specific samples based on the name of the list element @@ -298,7 +298,7 @@ We can also look at clonotypes between samples and changes in dynamics by using * "alluvial" - graph imaged below * "area" - graph by area of the respective clonotype -**number** +**top.clones** * The top number of clonotypes to graph, this will be calculated based on the frequency of the individual sample. This can also be left blank. We can also look at clonotypes between samples and changes in dynamics by using the `compareClonotypes()` function. @@ -310,15 +310,33 @@ We can also look at clonotypes between samples and changes in dynamics by using * "alluvial" - graph imaged below * "area" - graph by area of the respective clonotype -**number** +**top.clones** * The top number of clonotypes to graph, this will be calculated based on the frequency of the individual sample. This can also be left blank. **clonotypes** * Can be used to isolate specific clonotype sequences, ensure the call matches the sequences you would like to visualize. +**highlight.clones** +* Specifically color certain clonotypes, other clones will be greyed out. + +**relabel.clones** +* Simplify the isolated clonotypes to numerical designations to allow for a tidier visualization + +```{r} +clonalCompare(combined, + top.clones = 10, + samples = c("P17B", "P17L"), + cloneCall="aa", + graph = "alluvial") +``` +We can also choose to highlight specific clonotypes, such as in the case of *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*: + + ```{r} -compareClonotypes(combined, - numbers = 10, +clonalCompare(combined, + top.clones = 10, + highlight.clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"), + relabel.clones = TRUE, samples = c("P17B", "P17L"), cloneCall="aa", graph = "alluvial") From 9cbf09e08ae287bf2e782e27cad7f2d726679bf6 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 15 Sep 2023 11:03:40 -0500 Subject: [PATCH 043/142] clonalSizeDistribution --- DESCRIPTION | 8 +- NAMESPACE | 19 +- NEWS | 3 +- R/clonalSizeDistribution.R | 628 ++++++++++++++++++ R/clonesizeDistribution.R | 86 --- man/clonalCompare.Rd | 15 +- ...tribution.Rd => clonalSizeDistribution.Rd} | 21 +- vignettes/vignette.Rmd | 8 +- 8 files changed, 678 insertions(+), 110 deletions(-) create mode 100644 R/clonalSizeDistribution.R delete mode 100644 R/clonesizeDistribution.R rename man/{clonesizeDistribution.Rd => clonalSizeDistribution.Rd} (77%) diff --git a/DESCRIPTION b/DESCRIPTION index 3d07b198..3d3476ef 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,7 +19,6 @@ Imports: reshape2, ggalluvial, stringr, - powerTCR, SingleCellExperiment, SummarizedExperiment, plyr, @@ -31,7 +30,12 @@ Imports: tidygraph, SeuratObject, stats, - Rcpp + Rcpp, + evmix, + ggdendro, + truncdist, + VGAM, + cubature Suggests: knitr, rmarkdown, diff --git a/NAMESPACE b/NAMESPACE index 9366ac2f..45865bdd 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -12,7 +12,7 @@ export(clonalNetwork) export(clonalOverlap) export(clonalOverlay) export(clonalProportion) -export(clonesizeDistribution) +export(clonalSizeDistribution) export(clonotypeBias) export(clusterTCR) export(combineBCR) @@ -41,6 +41,8 @@ importFrom(SingleCellExperiment,colData) importFrom(SingleCellExperiment,reducedDim) importFrom(SummarizedExperiment,"colData<-") importFrom(SummarizedExperiment,colData) +importFrom(VGAM,dpareto) +importFrom(cubature,adaptIntegrate) importFrom(dplyr,"%>%") importFrom(dplyr,bind_rows) importFrom(dplyr,count) @@ -50,11 +52,19 @@ importFrom(dplyr,sample_n) importFrom(dplyr,select) importFrom(dplyr,summarise) importFrom(dplyr,summarize_all) +importFrom(evmix,dgpd) +importFrom(evmix,fgpd) +importFrom(evmix,pgpd) +importFrom(evmix,qgpd) +importFrom(evmix,rgpd) importFrom(ggalluvial,StatStratum) importFrom(ggalluvial,geom_alluvium) importFrom(ggalluvial,geom_flow) importFrom(ggalluvial,geom_stratum) importFrom(ggalluvial,to_lodes_form) +importFrom(ggdendro,dendro_data) +importFrom(ggdendro,label) +importFrom(ggdendro,segment) importFrom(ggplot2,ggplot) importFrom(ggraph,circle) importFrom(ggraph,geom_edge_bend) @@ -70,12 +80,13 @@ importFrom(igraph,graph_from_data_frame) importFrom(methods,slot) importFrom(plyr,join) importFrom(plyr,llply) -importFrom(powerTCR,fdiscgammagpd) -importFrom(powerTCR,get_distances) importFrom(reshape2,dcast) importFrom(reshape2,melt) importFrom(rlang,"%||%") +importFrom(stats,hclust) importFrom(stats,na.omit) +importFrom(stats,optim) +importFrom(stats,pgamma) importFrom(stats,quantile) importFrom(stats,sd) importFrom(stringdist,stringdist) @@ -88,4 +99,6 @@ importFrom(stringr,str_split) importFrom(stringr,str_to_title) importFrom(tidygraph,activate) importFrom(tidygraph,as_tbl_graph) +importFrom(truncdist,qtrunc) +importFrom(truncdist,rtrunc) useDynLib(scRepertoire, .registration = TRUE) diff --git a/NEWS b/NEWS index ca3abff6..6ee6fe43 100644 --- a/NEWS +++ b/NEWS @@ -7,14 +7,15 @@ NEW FEATURES * Added percentGenes() * Added percentVJ() * Changed compareClonotypes to clonalCompare +* Changed clonotypeSizeDistribution to clonalSizeDistribution UNDERLYING CHANGES * startrac diversity metric re-implemented to remove startrac-class object intermediary +* implemented powerTCR locally to reduce dependencies and continue support * Universalized underlying function language and intermediate variables * Added features to clonalCompare to allow for highlighting sequences, relabeling clonotypes. - DEPRECATED AND DEFUNCT * Deprecate stripBarcodes() diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R new file mode 100644 index 00000000..3c6be50b --- /dev/null +++ b/R/clonalSizeDistribution.R @@ -0,0 +1,628 @@ +#' Hierarchical clustering of clonotypes on clonotype size and +#' Jensen-Shannon divergence +#' +#' This function produces a hierarchical clustering of clonotypes by sample +#' using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold +#' model in +#' \href{https://bioconductor.org/packages/devel/bioc/html/powerTCR.html}{powerTCR R package}. +#' Please read and cite PMID: 30485278 if using the function for analyses. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' clonalSizeDistribution(combined, cloneCall = "strict", method="ward.D2") +#' +#' @param df The product of combineTCR(), combineBCR(), +#' expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param threshold Numerical vector containing the thresholds +#' the grid search was performed over. +#' @param method The clustering parameter for the dendrogram. +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() +#' @import ggplot2 +#' @importFrom dplyr bind_rows +#' @importFrom ggdendro dendro_data segment label +#' @importFrom stats hclust optim pgamma +#' @export +#' @return ggplot dendrogram of the clone size distribution +#' @author Hillary Koch + +clonalSizeDistribution <- function(df, + cloneCall ="strict", + chain = "both", + method = "ward.D2", + threshold = 1, + group.by = NULL, + split.by = NULL, + exportTable = FALSE, + palette = "inferno") { + xend <- yend <- mpg_div_hp <- NULL + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + df <- checkBlanks(df, cloneCall) + if(!is.null(group.by)) { + df <- groupList(df, group.by) + } + data <- bind_rows(df) + unique_df <- unique(data[,cloneCall]) + Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) + Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) + colnames(Con.df)[1] <- "clonotype" + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + data <- df[[i]] + data <- data.frame(table(data[,cloneCall]), + stringsAsFactors = FALSE) + colnames(data) <- c(cloneCall, "Freq") + for (y in seq_along(unique_df)){ + clonotype.y <- Con.df$clonotype[y] + location.y <- which(clonotype.y == data[,cloneCall]) + Con.df[y,i+1] <- data[location.y[1],"Freq"] + } + } + colnames(Con.df)[2:(length(df)+1)] <- names(df) + Con.df[is.na(Con.df)] <- 0 + list <- list() + for (i in seq_along(df)) { + list[[i]] <- suppressWarnings(.fdiscgammagpd(Con.df[,i+1], useq = threshold)) + } + names(list) <- names(df) + grid <- 0:10000 + distances <- .get_distances(list, grid, modelType="Spliced") + mat_melt <- dendro_data(hclust(as.dist(distances), method = method), type = "rectangle") + + plot <- ggplot() + + geom_segment(data = segment(mat_melt), + aes(x = x, y = y, xend = xend, yend = yend)) + + geom_text(data = label(mat_melt), + aes(x = x, y = -0.02, label = label, hjust = 0), size = 4) + + geom_point(data = label(mat_melt), + aes(x = x, y = -0.01, color = as.factor(label)), size = 2) + + coord_flip() + + scale_y_reverse(expand = c(0.2, 0)) + + scale_color_manual(values = .colorizer(palette, nrow(label(mat_melt)))) + + theme_classic() + + guides(color = "none") + + theme(axis.title = element_blank(), + axis.ticks.y = element_blank(), + axis.text.y = element_blank()) + + if (exportTable) { + return(distances) + } + return(plot) +} + +##################################################### +#Functions to run the fdiscgammagpd and get_distances +##################################################### +#This is to reduce dependency footprint of scRepertoire +#and ensure compatibility going forward. +#' @importFrom evmix fgpd +.fdiscgammagpd <- function(x, useq, shift = NULL, pvector=NULL, + std.err = TRUE, method = "Nelder-Mead", ...){ + if(!is(x, "numeric")){ + stop("x must be numeric.") + } + + if(!is.null(shift)){ + if(!is(shift, "numeric")){ + stop("shift must be numeric.") + } + if(shift != round(shift)){ + stop("shift must be an integer.") + } + } + + if(!is(useq, "numeric")){ + stop("useq must be numeric.") + } + + if(any(x != round(x))){ + stop("all elements in x must be integers.") + } + + if(any(useq != round(useq))){ + stop("all elements in useq must be integers.") + } + + if(!is.null(pvector) & !(length(pvector) == 5)){ + stop("pvector must contain 5 elements.") + } + + if(!(is.logical(std.err))){ + stop("std.err must be TRUE or FALSE.") + } + + if(!(method %in% c("Nelder-Mead","BFGS", + "CG", "L-BFGS-B", "SANN", "Brent"))){ + stop("invalid method supplied.") + } + + if(is.null(shift)){ + shift <- min(x) + } + + if (is.null(pvector)) { + pvector <- rep(NA,5) + s <- log(mean(x+0.5))-mean(log(x+0.5)) + k <- (3-s + sqrt((s-3)^2 + 24*s))/12/s + pvector[1] <- k + pvector[2] <- k/mean(x) + pvector[3] <- as.vector(quantile(x, 0.9)) + + xu <- x[x>=pvector[3]] + initfgpd <- fgpd(xu, min(xu)-10^(-5)) + pvector[4] <- initfgpd$mle[1] + pvector[5] <- initfgpd$mle[2] + } + + bulk <- lapply(seq_along(useq), + function(idx,x,useq) x < useq[idx], x=x, useq=useq) + tail <- lapply(seq_along(useq), + function(idx,x,useq) x >= useq[idx], x=x, useq=useq) + phiu <- lapply(seq_along(useq), + function(idx,tail) mean(tail[[idx]]), tail=tail) + + gammfit <- list() + gpdfit <- list() + nllhu <- rep(NA, length(useq)) + for(i in seq_along(useq)){ + gammfit[[i]] <- tryCatch(expr = .fdiscgamma(pvector[1:2],x[bulk[[i]]], + useq[i], + phiu[[i]], + shift, + method="Nelder-Mead"), + error = function(err) NA) + gpdfit[[i]] <- tryCatch(expr = .fdiscgpd(pvector[4:5], + x[tail[[i]]], + useq[i], + phiu[[i]], + method="Nelder-Mead"), + error = function(err) { + pvec3 <- as.vector(quantile(x,1-phiu[[i]])) + xu <- x[x>=pvec3] + initfgpd.adj <- + fgpd(x, min(xu)-10^(-5)) + pvec4 <- initfgpd.adj$mle[1] + pvec5 <- initfgpd.adj$mle[2] + tryCatch(expr = .fdiscgpd(c(pvec4,pvec5), + x[tail[[i]]], + useq[i], + phiu[[i]], + method="Nelder-Mead"), + error = function(err2) NA) + }) + nllhu[i] <- tryCatch(expr = gammfit[[i]]$value + gpdfit[[i]]$value, + error = function(err) NA) + } + + bestfit <- which.min(nllhu) + fit.out <- list(gammfit[[bestfit]], gpdfit[[bestfit]]) + names(fit.out) <- c("bulk","tail") + mle <- c(mean(x >= useq[bestfit]), + exp(fit.out$bulk$par), + useq[bestfit], + exp(fit.out$tail$par[1]), + fit.out$tail$par[2]) + names(mle) <- c("phi","shape","rate","thresh","sigma","xi") + if(std.err){ + H <- fit.out$bulk$hessian %>% rbind(matrix(rep(0,4),nrow = 2)) %>% + cbind(rbind(matrix(rep(0,4),nrow = 2),fit.out$tail$hessian)) + fisherInf <- tryCatch(expr = solve(H), error = function(err) NA) + out <- list(x = as.vector(x), shift = shift, init = as.vector(pvector), + useq = useq, nllhuseq = nllhu, + optim = fit.out, nllh = nllhu[bestfit], + mle=mle, fisherInformation = fisherInf) + } else{ + out <- list(x = as.vector(x), shift = shift, init = as.vector(pvector), + useq = useq, nllhuseq = nllhu, + optim = fit.out, nllh = nllhu[bestfit], + mle=mle) + } + out +} +##----------------------------------------------------------------------------- +## Density, distribution, and quantile functions, random number generation +## for discrete truncated gamma and discrete gpd +##----------------------------------------------------------------------------- + +.ddiscgamma <- function(x, shape, rate, thresh, phiu, shift = 0, log = FALSE){ + if(any(x != floor(x))){ + stop("x must be an integer") + } + + out <- rep(0, length(x)) + + up <- pgamma(x+1-shift, shape=shape, rate=rate) + down <- pgamma(x-shift, shape=shape, rate=rate) + + if(!log){ + b <- pgamma(thresh-shift, shape=shape, rate=rate) + out[x < thresh] <- ((1-phiu)*(up-down)/b)[x < thresh] + } else{ + b <- pgamma(thresh-shift, shape=shape, rate=rate, log.p = TRUE) + out[x < thresh] <- (log(1-phiu)+log(up-down) - b)[x < thresh] + } + out +} + +.pdiscgamma <- function(q, shape, rate, thresh, phiu, shift = 0){ + probs <- .ddiscgamma(0:q, shape, rate, thresh, phiu, shift) + sum(probs) +} + +#' @importFrom truncdist qtrunc +.qdiscgamma <- function(p, shape, rate, thresh, phiu, shift = 0){ + qtrunc(p/(1-phiu), spec = "gamma", a=0, + b=thresh-shift, shape=shape,rate=rate) %>% floor + shift +} + +#' @importFrom truncdist rtrunc +.rdiscgamma <- function(n, shape, rate, thresh, shift = 0){ + rtrunc(n, spec = "gamma", a=0, + b=thresh-shift, shape=shape, rate=rate) %>% floor + shift +} + +#' @importFrom evmix pgpd +.ddiscgpd <- function(x, thresh, sigma, xi, phiu, log = FALSE){ + up <- pgpd(x+1, u=thresh, sigmau=sigma, xi=xi) + down <- pgpd(x, u=thresh, sigmau=sigma, xi=xi) + + if(!log){ + phiu*(up-down) + } else{ + log(phiu) + log(up-down) + } +} + +pdiscgpd <- function(q, thresh, sigma, xi, phiu){ + probs <- .ddiscgpd(thresh:q, thresh, sigma, xi, phiu) + sum(probs) +} + +#' @importFrom evmix qgpd +.qdiscgpd <- function(p, thresh, sigma, xi, phiu){ + qgpd(p/phiu, u=thresh, sigmau = sigma, xi=xi) %>% floor +} + +#' @importFrom evmix rgpd +.rdiscgpd <- function(n, thresh, sigma, xi){ + rgpd(n, u=thresh, sigmau=sigma, xi=xi) %>% floor +} + + +##---------------------------------------------------------------------------------- +## negative log likelihood and parameter estimation functions +## for discrete truncated gamma and discrete gpd +##---------------------------------------------------------------------------------- + +.discgammanll <- function(param, dat, thresh, phiu, shift=0){ + shape <- exp(param[1]) + rate <- exp(param[2]) + + if(any(dat > thresh-1)){ warning("data must be less than the threshold") } + + ll <- log(.ddiscgamma(dat, shape, rate, thresh, phiu, shift)) + + sum(-ll) +} + +.discgpdnll <- function(param, dat, thresh, phiu){ + sigma <- exp(param[1]) + xi <- param[2] + ll <- log(.ddiscgpd(dat, thresh, sigma, xi, phiu)) + + sum(-ll) +} + +.fdiscgamma <- function(param, dat, thresh, phiu, shift = 0, method, ...){ + opt <- optim(log(param), .discgammanll, dat=dat, thresh=thresh, + phiu=phiu, shift=shift, method=method, hessian = TRUE, ...) + opt +} + +.fdiscgpd <- function(param, dat, thresh, phiu, method, ...){ + opt <- optim(c(log(param[1]),param[2]), .discgpdnll, dat=dat, thresh=thresh, + phiu=phiu, method=method, hessian = TRUE, ...) + opt +} + +.get_distances <- function(fits, grid, modelType = "Spliced"){ + if(!is(grid, "numeric")){ + stop("grid must be numeric.") + } + if(any(grid != round(grid))){ + stop("all elements in grid must be integers.") + } + if(!(modelType %in% c("Spliced", "Desponds"))){ + stop("modelType must be either \"Spliced\" or \"Desponds\".") + } + + distances <- matrix(rep(0, length(fits)^2), nrow = length(fits)) + if(!is.null(names(fits))){ + rownames(distances) <- colnames(distances) <- names(fits) + } + + for(i in seq_len((length(fits)-1))){ + for(j in (i+1):length(fits)){ + distances[i,j] <- .JS_dist(fits[[i]], + fits[[j]], + grid, + modelType = modelType) + } + } + distances <- distances + t(distances) + distances +} + +.JS_dist <- function(fit1, fit2, grid, modelType = "Spliced"){ + if(!(modelType %in% c("Spliced", "Desponds"))){ + stop("modelType must be either \"Spliced\" or \"Desponds\".") + } + + if(modelType == "Spliced"){ + if(!all(c("x", "init", "useq", "nllhuseq", "nllh", + "optim", "mle") %in% names(fit1))){ + stop("\"fit1\" is not of the correct structure. It must be a model + fit from fdiscgammagpd.") + } + if(!all(c("x", "init", "useq", "nllhuseq", "nllh", + "optim", "mle") %in% names(fit2))){ + stop("\"fit2\" is not of the correct structure. It must be a model + fit from fdiscgammagpd.") + } + shiftp <- fit1$shift + shiftq <- fit2$shift + phip <- fit1$mle['phi'] + phiq <- fit2$mle['phi'] + shapep <- fit1$mle['shape'] + shapeq <- fit2$mle['shape'] + ratep <- fit1$mle['rate'] + rateq <- fit2$mle['rate'] + threshp <- fit1$mle['thresh'] + threshq <- fit2$mle['thresh'] + sigmap <- fit1$mle['sigma'] + sigmaq <- fit2$mle['sigma'] + xip <- fit1$mle['xi'] + xiq <- fit2$mle['xi'] + + out <- .JS_spliced(grid, shiftp, shiftq, phip, phiq, shapep, shapeq, + ratep, rateq, threshp, threshq, sigmap, sigmaq, + xip, xiq) + } else if(modelType == "Desponds"){ + if(!all(c("min.KS", "Cmin", "powerlaw.exponent", + "pareto.alpha") == names(fit1))){ + stop("\"fit1\" is not of the correct structure. It must be a model + fit from fdesponds.") + } + if(!all(c("min.KS", "Cmin", "powerlaw.exponent", + "pareto.alpha") == names(fit2))){ + stop("\"fit2\" is not of the correct structure. It must be a model + fit from fdesponds.") + } + Cminp <- fit1['Cmin'] + Cminq <- fit2['Cmin'] + alphap <- fit1['pareto.alpha'] + alphaq <- fit2['pareto.alpha'] + out <- .JS_desponds(grid, Cminp, Cminq, alphap, alphaq) + } + out +} + +#' @importFrom evmix dgpd +.JS_spliced <- function(grid, shiftp, shiftq, phip, phiq, shapep, shapeq, ratep, + rateq, threshp, threshq, sigmap, sigmaq, xip, xiq){ + if(!is(grid, "numeric")){ + stop("grid must be numeric.") + } + + if(any(grid != round(grid))){ + stop("all elements in grid must be integers.") + } + + if(any(!is(c(shiftp, shiftq, phip, phiq, shapep, shapeq, + ratep, rateq, threshp, threshq, + sigmap, sigmaq, xip, xiq), "numeric"))){ + stop("shiftp, shiftq, phip, phiq, shapep, shapeq, ratep, rateq, + threshp, threshq, sigmap, sigmaq, xip, and xiq must be numeric.") + } + + if(shiftp != round(shiftp) | shiftq != round(shiftq)){ + stop("shiftp and shiftq must be integers.") + } + + if(any(c(shapep, shapeq, ratep, rateq, sigmap, sigmaq) <= 0)){ + stop("shapep, shapeq, ratep, rateq, sigmap, and sigmaq must be + greater than 0.") + } + + if(any(c(phip, phiq) > 1) | any(c(phip, phiq) < 0)){ + stop("phip and phiq must be in [0,1].") + } + + if(ratep <= 0 | rateq <= 0){ + stop("ratep and rateq must be greater than 0.") + } + + if(shapep <= 0 | shapeq <= 0){ + stop("shapep and shapeq must be greater than 0.") + } + + if(threshp != round(threshp) | threshq != round(threshq)){ + stop("threshp and threshq must be integers.") + } + + K <- max(grid) + + P <- .ddiscgammagpd(min(grid):K, shape = shapep, rate = ratep, + u=threshp, sigma = sigmap, + xi = xip, phiu = phip, shift=shiftp, + log = FALSE) + adjp <- which(P == 0) + if(length(adjp) != 0){ + P[adjp] <- dgpd(adjp+0.5, u=threshp, + sigmau = sigmap, xi = xip, phiu = phip) + } + + Q <- .ddiscgammagpd(min(grid):K, shape = shapeq, rate = rateq, + u=threshq, sigma = sigmaq, + xi = xiq, phiu = phiq, shift=shiftq, + log = FALSE) + adjq <- which(Q == 0) + if(length(adjq) != 0){ + Q[adjq] <- dgpd(adjq+0.5, u=threshq, + sigmau = sigmaq, xi = xiq, phiu = phiq) + } + + M <- 0.5*(P+Q) + pzero <- which(P == 0) + qzero <- which(Q == 0) + + sum1 <- sum2 <- rep(NA, length(grid)) + sum1 <- P*(log(P) - log(M)) + sum2 <- Q*(log(Q) - log(M)) + + if(length(intersect(pzero, qzero)) != 0){ + sum1[intersect(pzero, qzero)] <- 0 + sum2[intersect(pzero, qzero)] <- 0 + } + if(length(setdiff(pzero, qzero)) != 0){ + sum1[setdiff(pzero, qzero)] <- 0 + } + if(length(setdiff(qzero, pzero)) != 0){ + sum2[setdiff(qzero, pzero)] <- 0 + } + + out <- sqrt(0.5*(sum(sum1) + sum(sum2))) + out +} + + +#' @importFrom cubature adaptIntegrate +.JS_desponds <- function(grid, Cminp, Cminq, alphap, alphaq){ + if(!is(grid, "numeric")){ + stop("grid must be numeric.") + } + + if(any(grid != round(grid))){ + stop("all elements in grid must be integers.") + } + + if(any(!is(c(Cminp, Cminq, alphap, alphaq), "numeric"))){ + stop("Cminp, Cminq, alphap, and alphaq must be numeric.") + } + + if(Cminp != round(Cminp) | Cminq != round(Cminq)){ + stop("Cminp and Cminq must be integers.") + } + + if(alphap <= 0 | alphaq <= 0){ + stop("alphap and alphaq must be greater than 0.") + } + + lower = min(grid) + upper = max(grid) + + out <- adaptIntegrate(eval_desponds, + lowerLimit = lower, upperLimit = upper, + Cminp = Cminp, Cminq = Cminq, + alphap = alphap, alphaq = alphaq)$integral %>% sqrt + out +} + +.ddiscgammagpd <- function(x, fit=NULL, shape, rate, u, sigma, xi, + phiu=NULL, shift = 0, log = FALSE){ + if(!is.null(fit)){ + if(!all(c("x", "shift", "init", "useq", "nllhuseq", "nllh", + "optim", "mle") %in% names(fit))){ + stop("\"fit\" is not of the correct structure. It must be one model + fit from fdiscgammagpd.") + } + phiu <- fit$mle['phi'] + shape <- fit$mle['shape'] + rate <- fit$mle['rate'] + u <- fit$mle['thresh'] + sigma <- fit$mle['sigma'] + xi <- fit$mle['xi'] + shift <- fit$shift + } + if(!is(x, "numeric")){ + stop("x must be numeric.") + } + + if(!is(shift, "numeric")){ + stop("shift must be numeric.") + } + + if(any(x != floor(x))){ + stop("x must be an integer") + } + + if(shift != round(shift)){ + stop("shift must be an integer.") + } + + if(any(c(shape, rate, sigma) <= 0)){ + stop("shape, rate, and sigma must all be positive.") + } + + if(!is.null(phiu)){ + if(phiu < 0 | phiu > 1){ + stop("phiu must be in [0,1].") + } + } + + if(is.null(phiu)){ + phiu <- 1-.pdiscgamma(u-1, shape=shape, rate=rate, + thresh=Inf, phiu = 0, shift=shift) + } + + out <- rep(NA, length(x)) + + if(sum(x>=u) != 0){ + out[x>=u] <- .ddiscgpd(x[x>=u], u, sigma, xi, phiu, log=log) + } + + if(sum(x Date: Fri, 15 Sep 2023 11:06:06 -0500 Subject: [PATCH 044/142] Update clonalSizeDistribution.R --- R/clonalSizeDistribution.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 3c6be50b..f2a8bc95 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -536,7 +536,7 @@ pdiscgpd <- function(q, thresh, sigma, xi, phiu){ lower = min(grid) upper = max(grid) - out <- adaptIntegrate(eval_desponds, + out <- adaptIntegrate(.eval_desponds, lowerLimit = lower, upperLimit = upper, Cminp = Cminp, Cminq = Cminq, alphap = alphap, alphaq = alphaq)$integral %>% sqrt From b656fb3fd2db4945290156b5a53505f0ab90cc9f Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 21 Sep 2023 11:20:27 -0500 Subject: [PATCH 045/142] Update README.md --- README.md | 18 +++++++----------- 1 file changed, 7 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index cbfb918b..ff39b16b 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,7 @@ [![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.borch.dev/uploads/vignette/vignette) -#### A toolkit for single-cell immune profiling +## A toolkit for single-cell immune profiling @@ -16,21 +16,17 @@ Single-cell sequencing is an emerging technology in the field of immunology and ### Applying Deep Learning to VDJ data scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/ncborcherding/Trex) for deep-learning-based autoencoding of the T cell receptor and [Ibex](https://github.com/ncborcherding/Ibex) for the B cell receptor. -### Wrapper Functions -scRepertoire v1.0.2 has the functionality of the [powerTCR](https://github.com/hillarykoch/powerTCR) approach to comparing clone size distribution, [please cite](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006571) the manuscript if using the ```clonesizeDistribution()``` function. In addition, we recently added the [Startrac](https://github.com/Japrin/STARTRAC) clonotype metrics, if using the ```StartracDiversity()``` please read and cite [the accompanying article](https://www.nature.com/articles/s41586-018-0694-x). +## Installation -### Installation of Master Branch +#### Installation of Master Branch ``` devtools::install_github("ncborcherding/scRepertoire") ``` #### Most up-to-date version - ``` devtools::install_github("ncborcherding/scRepertoire@dev") ``` -We are working on constantly improving the scRepertoire package - these improvements can be found under the "refine" branch. These changes will be incorporated into the main branch as they are tested more throughly. - #### Bioconductor [scRepertoire](https://www.bioconductor.org/packages/release/bioc/html/scRepertoire.html) is now on Bioconductor. Use the link to find out more information. scRepertoire (for R v4 or greater) can be installed from Bioconductor with: @@ -42,17 +38,17 @@ if (!requireNamespace("BiocManager", quietly = TRUE)) BiocManager::install("scRepertoire") ``` -### Getting Data +## Getting Data Unfortunately, Github limits the size of individual files. In order to access the seurat object paired with scRepertoire please download the .rda from [here](https://drive.google.com/file/d/1Iv6t2BScpnLLrFWaWFUGwne3XzRAwMOc/view?usp=share_link). -### Learning To Use scRepertoire +## Learning To Use scRepertoire Vignette available [here](https://www.borch.dev/uploads/vignette/vignette), includes almost 13,000 T cells across 3 patients with renal clear cell carcinoma. -### Please Cite +## Please Cite Feel free to use, edit, modify scRepertoire, but if you do, please cite the [manuscript](https://f1000research.com/articles/9-47/v1). -### Contact +## Contact Questions, comments, suggestions, please feel free to contact Nick Borcherding via this repository, [email](mailto:ncborch@gmail.com), or using [twitter](https://twitter.com/theHumanBorch). From 5123243c4472e5c7a60bbb594e3a477aff40e5f2 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 21 Sep 2023 11:28:31 -0500 Subject: [PATCH 046/142] Update data.R --- R/data.R | 39 ++++----------------------------------- 1 file changed, 4 insertions(+), 35 deletions(-) diff --git a/R/data.R b/R/data.R index 33615823..4342e327 100644 --- a/R/data.R +++ b/R/data.R @@ -5,8 +5,8 @@ #' NULL -#' A seurat object of 100 single T cells derived -#' from 3 clear cell renal carcinoma patients. +#' A seurat object of 500 single T cells derived +#' from 4 patients with acute respiratory distress (ARDS). #' #' @description The object is compatible with `contig_list` and the TCR #' sequencing data can be added with `combineExpression`. @@ -18,8 +18,8 @@ NULL #' Processed subset of `contig_list` #' -#' @description A list of 6 dataframes of T cell contigs outputted from the -#' `filtered_contig_annotation` files, but subsetted to about 92 valid T cells +#' @description A list of 8 dataframes of T cell contigs outputted from the +#' `filtered_contig_annotation` files, but subsetted to 365 valid T cells #' which correspond to the same barcodes found in `scRep_example` #' #' @usage data("mini_contig_list") @@ -31,34 +31,3 @@ NULL #' @seealso \code{\link{contig_list}} #' "mini_contig_list" - -# # Code used for creating the mini_contig_list: - -# library(hash, usethis) -# -#data("contig_list", "scRep_example") - -#mini_contig_list <- combineTCR( -# contig_list, -# samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), -# ID = c("P", "T", "P", "T", "P", "T") -#) -#all_barcodes <- names(scRep_example@active.ident) -#barcode_set <- hash::hash(all_barcodes, all_barcodes) # a worse version of a set -#col_names <- names(mini_contig_list[[1]]) - -#for (i in seq_along(mini_contig_list)) { -# curr_df <- setNames( -# data.frame(replicate(length(col_names), character(0))), col_names -# ) -# len <- 0 -# for (j in seq_along(mini_contig_list[[i]][[1]])) { -# if (is.null(barcode_set[[mini_contig_list[[i]][[1]][[j]]]])) { -# next -# } -# len <- len + 1 -# curr_df[len, ] <- mini_contig_list[[i]][j, ] -# } -# mini_contig_list[[i]] <- curr_df -#} -#usethis::use_data(mini_contig_list) From 2edcc1c109515cf6936e6381d0bce5e8ed4efdb5 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 21 Sep 2023 11:40:55 -0500 Subject: [PATCH 047/142] Update vignette.Rmd Adjusted for new samples --- vignettes/vignette.Rmd | 46 +++++++++++++++++------------------------- 1 file changed, 18 insertions(+), 28 deletions(-) diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 19ad3e60..577b2162 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -73,7 +73,7 @@ contigs <- read.csv(".../outs/filtered_contig_annotations.csv") contig.list <- createHTOContigList(contigs, Seurat.Obj, group.by = "HTO_maxID") ``` -scRepertoire comes with a data set derived from T cells derived from three patients with renal clear cell carcinoma in order to demonstrate the functionality of the R package. More information on the data set can be found at the corresponding [manuscript](https://www.nature.com/articles/s42003-020-01625-6). The samples consist of paired peripheral-blood and tumor-infiltrating runs, effectively creating 6 distinct runs for T cell receptor (TCR) enrichment. We can preview the elements in the list by using the head function and looking at the first contig annotation. +scRepertoire comes with a data set derived from T cells derived from four patients with acute respiratory distress in order to demonstrate the functionality of the R package. More information on the data set can be found at the corresponding [manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/). The samples consist of paired peripheral-blood (B) and bronchoalveolar lavage (L), effectively creating 8 distinct runs for T cell receptor (TCR) enrichment. We can preview the elements in the list by using the head function and looking at the first contig annotation. ```{r} data("contig_list") #the data built into scRepertoire @@ -81,29 +81,6 @@ data("contig_list") #the data built into scRepertoire head(contig_list[[1]]) ``` -Some workflows will have the additional labeling of the standard barcode. Before we proceed, we can use the function `stripBarcode()` in order to avoid any labeling issues down the line. Importantly, `stripBarcode()` is for *removing prefixes* on barcodes that have resulted from other pipelines. - -**No need for stripBarcode function**, if the barcodes look like: -* AAACGGGAGATGGCGT-1 -* AAACGGGAGATGGCGT - -In terms of using `stripBarcode()`, please think about the following parameters. - -**column** -* The column in which the barcodes are present - -**connector** -* The character that is connecting the barcode with the prefix - -**num_connects** -* The levels of barcode prefix, where *X_X_AAACGGGAGATGGCGT-1* == 3, *X_AAACGGGAGATGGCGT-1* = 2. - -```{r eval=FALSE} -for (i in seq_along(contig_list)) { - contig_list[[i]] <- stripBarcode(contig_list[[i]], column = 1, connector = "_", num_connects = 3) -} -``` - ## loadContigs New to this version of the package is support for BD Rhapsody, AIRR, [WAT3R](https://www.biorxiv.org/content/10.1101/2022.01.26.477886v1), and [TRUST4](https://www.nature.com/articles/s41592-021-01142-2) single-cell outputs. By indicating the directory of the outputs from these pipelines are located, ```loadContigs()``` will generate a contig list that is compatible with the rest of scRepertoire. Alternatively, ```loadContigs()``` will also accept a list of data frames that you have loaded individualy. In addition, although not necessary, ```loadContigs()``` can be used for 10X filtered_contig_annotation.csv files. @@ -199,13 +176,26 @@ quantContig(combined, cloneCall="strict", chain = "both", scale = TRUE) +``` +For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme + ++ Change the **palette** parameter to desired color scheme. This approach uses the built-in palettes of grDevices and you can access the list of color schemes using ``hcl.pals()``. ++ Add an additional ggplot layer with new color scheme. -#Adjust colors with the palette parameter +```{r} quantContig(combined, cloneCall="strict", chain = "both", scale = TRUE, palette = "Zissou 1") + +quantContig(combined, + cloneCall="strict", + chain = "both", + scale = TRUE) + + scale_color_manual(values = hcl.colors(8,"geyser")) + + ``` Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph, instead of the visual output. @@ -404,7 +394,7 @@ vizGenes(combined, scale = TRUE) ``` -We can also use `vizGenes()` to look at the usage of genes in a single chain. So for example say we are interesting in the difference in TRB V and J usage between tumor and peripheral blood samples - we can easily take a look at this using the following code: +We can also use `vizGenes()` to look at the usage of genes in a single chain. So for example say we are interesting in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: ```{r} #Peripheral Blood @@ -559,7 +549,7 @@ plotUMAP(sce, colour_by = "seurat_clusters") Next we can take the clonotypic information and attach it to our Seurat object using the *combineExpression()* function. **Importantly**, the major requirement for the attachment is matching contig cell barcodes and barcodes in the row names of the meta data of the Seurat or SCE object. If these do not match, the attachment will fail. Based on ease, we suggest you make the changes to the Seurat object row names. -We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 amino acid sequence, 3) CDR3 nucleotide sequence, or 4) VDJC genes and CDR3 nucleotide sequence. The attaching function will also calculate the frequency of the clonotype based on the **group.by** variable. If blank, **group.by** will calculate frequencies of clonotypes by individual run, but because we have 6 samples of paired peripheral and tumor T cells, we are actually going to use the group.by variable to call "sample" in order to calculate frequencies across both the peripheral blood and tumor T cells of the same patient. +We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 amino acid sequence, 3) CDR3 nucleotide sequence, or 4) VDJC genes and CDR3 nucleotide sequence. The attaching function will also calculate the frequency of the clonotype based on the **group.by** variable. If blank, **group.by** will calculate frequencies of clonotypes by individual run, but because we have 6 samples of paired peripheral blood and lung T cells, we are actually going to use the group.by variable to call "sample" in order to calculate frequencies across both the blood and lung T cells of the same patient. In order to categorize the frequency, we have the variable **proportion** which if TRUE allows for the relative proportion or when FALSE will use absolute frequency to define clonotype groups **cloneTypes** acts as a bin to place labels. As a default, **cloneTypes** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below you can see an example of using total frequency as expansion assignments. @@ -595,7 +585,7 @@ seurat <- combineExpression(list.receptors, proportion = TRUE) ``` -We first want to look at the distribution of peripheral versus tumor T cells. We can use the same color scheme as the rest of the scRepertoire package by calling the object **colorblind_vector** using the following hex codes. +We first want to look at the distribution of peripheral blood versus lung T cells. We can use the same color scheme as the rest of the scRepertoire package by calling the object **colorblind_vector** using the following hex codes. ```{r} colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) From 4f5a7d25a0c77386ea5ee1a34cda15789bc211b7 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 21 Sep 2023 11:52:15 -0500 Subject: [PATCH 048/142] Update abundanceContig.R --- R/abundanceContig.R | 83 ++++++++++++++++++++++++--------------------- 1 file changed, 45 insertions(+), 38 deletions(-) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index 8aff3405..fea29775 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -1,10 +1,9 @@ #' Demonstrate the relative abundance of clonotypes by group or sample. #' -#' This function takes the output of combineTCR(), combineBCR(), or -#' expression2List() and displays the number of clonotypes at specific -#' frequencies by sample or group. Visualization can either be a line -#' graph using calculated numbers or if scale = TRUE, the output will -#' be a density plot. Multiple sequencing runs can be group together +#' Displays the number of clonotypes at specific requencies by sample +#' or group. Visualization can either be a line graph using +#' calculated numbers or if scale = TRUE, the output will be a +#' density plot. Multiple sequencing runs can be group together #' using the group parameter. If a matrix output for the data is #' preferred, set exportTable = TRUE. #' @@ -49,35 +48,38 @@ abundanceContig <- function(df, cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) names <- names(df) + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + } + if (!is.null(group.by)) { for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } data1 <- parseContigs(df, i, names, cloneCall) label <- df[[i]][1,group.by] data1[,paste(group.by)] <- label Con.df<- rbind.data.frame(Con.df, data1) } - Con.df <- data.frame(Con.df) - col <- length(unique(Con.df[,group.by])) - fill <- group.by - if (scale == TRUE) { ylab <- "Density of Clonotypes" - plot <- ggplot(Con.df, aes(x=Abundance, fill=Con.df[,group.by])) + - geom_density(aes(y=after_stat(scaled)), alpha=0.5, - lwd=0.25, color="black", bw=0.5) + - scale_fill_manual(values = .colorizer(palette,col)) + - labs(fill = fill) - } else { ylab <- "Number of Clonotypes" - plot <- ggplot(Con.df, aes(x=Abundance, group.by = values, + Con.df <- data.frame(Con.df) + col <- length(unique(Con.df[,group.by])) + fill <- group.by + if (scale == TRUE) { + ylab <- "Density of Clonotypes" + plot <- ggplot(Con.df, aes(x=Abundance, fill=Con.df[,group.by])) + + geom_density(aes(y=after_stat(scaled)), alpha=0.5, + lwd=0.25, color="black", bw=0.5) + + scale_fill_manual(values = .colorizer(palette,col)) + + labs(fill = fill) + } else { + ylab <- "Number of Clonotypes" + plot <- ggplot(Con.df, aes(x=Abundance, group.by = values, color = Con.df[,group.by])) + - geom_line(stat="count") + - scale_color_manual(values = .colorizer(palette,col)) + - labs(color = fill)} + geom_line(stat="count") + + scale_color_manual(values = .colorizer(palette,col)) + + labs(color = fill) + } } else { for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } data1 <- parseContigs(df, i, names, cloneCall) Con.df<- rbind.data.frame(Con.df, data1) } @@ -87,20 +89,25 @@ abundanceContig <- function(df, } col <- length(unique(Con.df$values)) fill <- "Samples" - if (scale == TRUE) { ylab <- "Density of Clonotypes" - plot <- ggplot(Con.df, aes(Abundance, fill=values)) + - geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, - color="black", bw=0.5) + - scale_fill_manual(values = .colorizer(palette,col)) + - labs(fill = fill) - } else { ylab <- "Number of Clonotypes" - plot <- ggplot(Con.df, aes(x=Abundance, group = values, + if (scale == TRUE) { + ylab <- "Density of Clonotypes" + plot <- ggplot(Con.df, aes(Abundance, fill=values)) + + geom_density(aes(y=after_stat(scaled)), alpha=0.5, lwd=0.25, + color="black", bw=0.5) + + scale_fill_manual(values = .colorizer(palette,col)) + + labs(fill = fill) + } else { + ylab <- "Number of Clonotypes" + plot <- ggplot(Con.df, aes(x=Abundance, group = values, color = values)) + - geom_line(stat="count") + - scale_color_manual(values = .colorizer(palette,col)) + - labs(color = fill)} } - if (exportTable == TRUE) { return(Con.df) } + geom_line(stat="count") + + scale_color_manual(values = .colorizer(palette,col)) + + labs(color = fill) + } } + if (exportTable == TRUE) { + return(Con.df) + } plot <- plot + scale_x_log10() + ylab(ylab) + xlab(xlab) + theme_classic() return(plot) -} \ No newline at end of file +} From 26e4eb1337dfbe46953eeea475af849d82432354 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 21 Sep 2023 16:01:46 -0500 Subject: [PATCH 049/142] clonalScatter consistent naming, code annotation for above function. Clean code for percent..() functions --- NAMESPACE | 2 +- R/clonalScatter.R | 142 ++++++++++++++++++ R/percentGenes.R | 9 +- R/percentVJ.R | 5 +- R/scatterClonotypes.R | 114 -------------- man/abundanceContig.Rd | 9 +- man/{scatterClonotype.Rd => clonalScatter.Rd} | 44 +++++- man/mini_contig_list.Rd | 4 +- man/percentGenes.Rd | 1 + man/screp_example.Rd | 4 +- vignettes/vignette.Rmd | 18 +-- 11 files changed, 203 insertions(+), 149 deletions(-) create mode 100644 R/clonalScatter.R delete mode 100644 R/scatterClonotypes.R rename man/{scatterClonotype.Rd => clonalScatter.Rd} (61%) diff --git a/NAMESPACE b/NAMESPACE index 45865bdd..b46ef90b 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -12,6 +12,7 @@ export(clonalNetwork) export(clonalOverlap) export(clonalOverlay) export(clonalProportion) +export(clonalScatter) export(clonalSizeDistribution) export(clonotypeBias) export(clusterTCR) @@ -29,7 +30,6 @@ export(percentAA) export(percentGenes) export(percentVJ) export(quantContig) -export(scatterClonotype) export(subsetContig) export(vizGenes) import(dplyr) diff --git a/R/clonalScatter.R b/R/clonalScatter.R new file mode 100644 index 00000000..f60d4be1 --- /dev/null +++ b/R/clonalScatter.R @@ -0,0 +1,142 @@ +#' Scatter plot comparing the expansion of two samples +#' +#' This function produces a scatter plot directly comparing the specific clonotypes +#' between two samples. The clonotypes will be categorized by counts into singlets or multiplets, +#' either exclusive or shared between the selected samples. Visualization inspired +#' by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' clonalScatter(combined, +#' x.axis = "P17B", +#' y.axis = "P17L", +#' graph = "proportion") +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param x.axis name of the list element to appear on the x.axis +#' @param y.axis name of the list element to appear on the y.axis +#' @param dot.size either total or the name of the list element to +#' use for size of dots +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param graph graph either proportion or raw clonotype count +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param seed the integer seed to set for the random variation of point coords. +#' +#' @import ggplot2 +#' +#' @export +#' @return ggplot of the relative clonotype numbers + +clonalScatter <- function(df, + cloneCall ="strict", + x.axis = NULL, + y.axis = NULL, + chain = "both", + dot.size = "total", + split.by = NULL, + graph = "proportion", + exportTable = FALSE, + palette = "inferno", + seed = NULL) { + if (!is.null(seed)) { + set.seed(seed) + } + df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) + axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) + if (chain != "both") { + for (i in axes) { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + } + + #Making new data frame and adding variables to graph + x.df <- as.data.frame(table(df[[x.axis]][,cloneCall])) + colnames(x.df)[2] <- x.axis + y.df <- as.data.frame(table(df[[y.axis]][,cloneCall])) + colnames(y.df)[2] <- y.axis + Con.df <- merge(x.df, y.df, by = "Var1", all = TRUE) + + if (dot.size != "total") { + if (dot.size %!in% colnames(Con.df)) { + size.df <- as.data.frame(table(df[[dot.size]][,cloneCall])) + colnames(size.df)[2] <- dot.size + Con.df <- merge(Con.df, size.df, by = "Var1", all = TRUE) } + Con.df[is.na(Con.df)] <- 0 + Con.df[,paste0("size", ".fraction")] <- Con.df[,dot.size]/sum(Con.df[,dot.size]) + labeling <- unique(c(x.axis, y.axis, dot.size)) + } else { + Con.df[is.na(Con.df)] <- 0 + labeling <- unique(c(x.axis, y.axis)) } + + Con.df[,"class"] <- NA + Con.df[,"sum"] <- rowSums(Con.df[,labeling]) + + #Assigning class based on x and y-axis counts + for (i in seq_along(labeling)) { + if (length(labeling) > 2) { + Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] == 1 & rowSums(Con.df[,labeling[-i]]) == 0, + paste0(labeling[i], ".singlet"), #if + Con.df[,"class"]) #else + Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] > 1 & rowSums(Con.df[,labeling[-i]]) == 0, + paste0(labeling[i], ".expanded"), #if + Con.df[,"class"]) #else + } else if (length(labeling) == 2) { + Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] == 1 & Con.df[,labeling[-i]] == 0, + paste0(labeling[i], ".singlet"), #if + Con.df[,"class"]) #else + Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] > 1 & Con.df[,labeling[-i]] == 0, + paste0(labeling[i], ".expanded"), #if + Con.df[,"class"]) #else + } + } + #Adding dual-expanded class + Con.df[,"class"] <- ifelse(Con.df[,y.axis] >= 1 & Con.df[,x.axis] >= 1, paste0("dual.expanded"), Con.df[,"class"]) + + #Calculating relative proportion + Con.df[,paste0(x.axis, ".fraction")] <- Con.df[,x.axis]/sum(Con.df[,x.axis]) + Con.df[,paste0(y.axis, ".fraction")] <- Con.df[,y.axis]/sum(Con.df[,y.axis]) + + #Altering the graphing parameters + if (graph == "proportion") { + x <- Con.df[,paste0(x.axis, ".fraction")] + y <- Con.df[,paste0(y.axis, ".fraction")] + } else if (graph == "count") { + x <- Con.df[,x.axis] + y <- Con.df[,y.axis] } + if (dot.size != "total") { + size <- Con.df[,dot.size] + } else { size <- Con.df[,"sum"] } + if (exportTable == TRUE) { return(Con.df) } + + plot <- ggplot(Con.df, aes(x=x, y = y, fill = class)) + + theme_classic() + + scale_fill_manual(values = .colorizer(palette,length(unique(Con.df$class)))) + + xlab(x.axis) + + ylab(y.axis) + + labs(size = "Total n") + if (graph == "proportion") { + plot <- plot + + geom_abline(slope = 1, intercept = 0, alpha = 0.4, lty=2) + + scale_y_sqrt() + + scale_x_sqrt() + } else if (graph == "count") { + plot <- plot + + ylim(0, max(x,y)) + + xlim(0, max(x,y)) + } + plot <- plot + + geom_jitter(aes(size = size,), shape = 21, color = "black", stroke = 0.25) + + return(plot) +} diff --git a/R/percentGenes.R b/R/percentGenes.R index be2b3fec..77084d20 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -9,7 +9,7 @@ #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' percentGenes(combined, chain = "TRB", gene = "Vgene") - +#' #' @param df The product of combineTCR(), combineBCR(), #' expression2List(), or combineExpression(). #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" @@ -38,7 +38,6 @@ percentGenes <- function(df, for(i in seq_along(df)) { df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") } - #Parsing gene input if (gene %in% c("Vgene", "V", "v", "v.gene")) { gene.loci <- paste0(chain, "V") @@ -51,7 +50,6 @@ percentGenes <- function(df, stop(paste0("There is not the D locus for ", gene)) } } - #Getting indicated genes across list gene_counts <- lapply(df, function(x) { tmp <- unlist(str_split(x[,"CTgene"], ";")) @@ -72,14 +70,13 @@ percentGenes <- function(df, percentages <- c(percentages, percentages.to.add) } percentages[match(str_sort(names(percentages), numeric = TRUE), names(percentages))] - - }) + summary <- do.call(rbind,summary) if (exportTable == TRUE) { return(summary) } - #Melting matrix and Visualizing + #Melting matrix and visualizing mat_melt <- melt(summary) plot <- ggplot(mat_melt, aes(y=Var1, x = Var2, fill=round(value*100,2))) + geom_tile(lwd= 0.25, color = "black") + diff --git a/R/percentVJ.R b/R/percentVJ.R index 90225213..1c2d74f6 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -9,7 +9,6 @@ #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' percentVJ(combined, chain = "TRB") - #' @param df The product of combineTCR(), combineBCR(), #' expression2List(), or combineExpression(). #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" @@ -43,7 +42,6 @@ percentVJ <- function(df, positions <- c(1,3) } - #Getting indicated genes across list gene_counts <- lapply(df, function(x) { tmp <- unlist(str_split(x[,"CTgene"], ";")) @@ -73,7 +71,6 @@ percentVJ <- function(df, return(summary.matrix) } mat <- lapply(summary, function(x) { - # Create an empty matrix result_matrix <- matrix(0, nrow = length(V_values), ncol = length(unique(J_values))) rownames(result_matrix) <- V_values @@ -84,8 +81,8 @@ percentVJ <- function(df, result_matrix[coordinates[1], coordinates[2]] <- x[i] } result_matrix - }) + #Melting matrix and Visualizing mat_melt <- melt(mat) plot <- ggplot(mat_melt, aes(y=Var1, x = Var2, fill=round(value*100,2))) + diff --git a/R/scatterClonotypes.R b/R/scatterClonotypes.R deleted file mode 100644 index dd7a12cb..00000000 --- a/R/scatterClonotypes.R +++ /dev/null @@ -1,114 +0,0 @@ -#' Scatter plot comparing the expansion of two samples -#' -#' This function produces a scatter plot directly comparing the specific clonotypes -#' between two samples. The clonotypes will be categorized by counts into singlets or multiplets, -#' either exclusive or shared between the selected samples. Visualization inspired -#' by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. -#' -#' @examples -#' #Making combined contig data -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' scatterClonotype(combined, x.axis = "P17B", y.axis = "P17L", -#' graph = "proportion") -#' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). -#' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param x.axis name of the list element to appear on the x.axis -#' @param y.axis name of the list element to appear on the y.axis -#' @param dot.size either total or the name of the list element to -#' use for size of dots -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param graph graph either proportion or raw clonotype count -#' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() -#' @param seed the integer seed to set for the random variation of point coords. -#' -#' @import ggplot2 -#' -#' @export -#' @return ggplot of the relative clonotype numbers - -scatterClonotype <- function(df, - cloneCall ="strict", - x.axis = NULL, - y.axis = NULL, - chain = "both", - dot.size = "total", - split.by = NULL, - graph = "proportion", - exportTable = FALSE, - palette = "inferno", - seed = NULL) { - if (!is.null(seed)) {set.seed(seed)} - - df <- list.input.return(df, split.by) - cloneCall <- theCall(cloneCall) - axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) - if (chain != "both") { - for (i in axes) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } - x.df <- as.data.frame(table(df[[x.axis]][,cloneCall])) - colnames(x.df)[2] <- x.axis - y.df <- as.data.frame(table(df[[y.axis]][,cloneCall])) - colnames(y.df)[2] <- y.axis - combined.df <- merge(x.df, y.df, by = "Var1", all = TRUE) - if (dot.size != "total") { - if (dot.size %!in% colnames(combined.df)) { - size.df <- as.data.frame(table(df[[dot.size]][,cloneCall])) - colnames(size.df)[2] <- dot.size - combined.df <- merge(combined.df, size.df, by = "Var1", all = TRUE) } - combined.df[is.na(combined.df)] <- 0 - combined.df[,paste0("size", ".fraction")] <- combined.df[,dot.size]/sum(combined.df[,dot.size]) - labeling <- unique(c(x.axis, y.axis, dot.size)) - } else { - combined.df[is.na(combined.df)] <- 0 - labeling <- unique(c(x.axis, y.axis)) } - combined.df[,"class"] <- NA - combined.df[,"sum"] <- rowSums(combined.df[,labeling]) - for (i in seq_along(labeling)) { - if (length(labeling) > 2) { - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] == 1 & rowSums(combined.df[,labeling[which(labeling != labeling[i])]]) == 0, - paste0(labeling[i], ".singlet"), combined.df[,"class"]) - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] > 1 & rowSums(combined.df[,labeling[which(labeling != labeling[i])]]) == 0, - paste0(labeling[i], ".multiplet"), combined.df[,"class"]) - } else if (length(labeling) == 2) { - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] == 1 & combined.df[,labeling[which(labeling != labeling[i])]] == 0, - paste0(labeling[i], ".singlet"), combined.df[,"class"]) - combined.df[,"class"] <- ifelse(combined.df[,labeling[i]] > 1 & combined.df[,labeling[which(labeling != labeling[i])]] == 0, - paste0(labeling[i], ".multiplet"), combined.df[,"class"]) - }} - combined.df[,"class"] <- ifelse(combined.df[,y.axis] >= 1 & combined.df[,x.axis] >= 1, paste0("dual.expanded"), combined.df[,"class"]) - combined.df[,paste0(x.axis, ".fraction")] <- combined.df[,x.axis]/sum(combined.df[,x.axis]) - combined.df[,paste0(y.axis, ".fraction")] <- combined.df[,y.axis]/sum(combined.df[,y.axis]) - if (graph == "proportion") { - x <- combined.df[,paste0(x.axis, ".fraction")] - y <- combined.df[,paste0(y.axis, ".fraction")] - } else if (graph == "count") { - x <- combined.df[,x.axis] - y <- combined.df[,y.axis] } - if (dot.size != "total") { - size <- combined.df[,dot.size] - } else { size <- combined.df[,"sum"] } - if (exportTable == TRUE) { return(combined.df) } - plot <- ggplot(combined.df, aes(x=x, y = y, fill = class)) + - theme_classic() + - scale_fill_manual(values = .colorizer(palette,length(unique(combined.df$class)))) + - xlab(x.axis) + ylab(y.axis) + labs(size = "Total n") - if (graph == "proportion") { - plot <- plot + geom_abline(slope = 1, intercept = 0, alpha = 0.4, lty=2) + - scale_y_sqrt() + scale_x_sqrt() - } else if (graph == "count") { - plot <- plot + ylim(0, max(x,y)) + xlim(0, max(x,y)) - } - plot <- plot + geom_jitter(aes(size = size,), shape = 21, color = "black", stroke = 0.25) - return(plot) -} diff --git a/man/abundanceContig.Rd b/man/abundanceContig.Rd index 63d0dc35..66a6146a 100644 --- a/man/abundanceContig.Rd +++ b/man/abundanceContig.Rd @@ -46,11 +46,10 @@ ggplot of the total or relative abundance of clonotypes across quanta } \description{ -This function takes the output of combineTCR(), combineBCR(), or -expression2List() and displays the number of clonotypes at specific -frequencies by sample or group. Visualization can either be a line -graph using calculated numbers or if scale = TRUE, the output will -be a density plot. Multiple sequencing runs can be group together +Displays the number of clonotypes at specific requencies by sample +or group. Visualization can either be a line graph using +calculated numbers or if scale = TRUE, the output will be a +density plot. Multiple sequencing runs can be group together using the group parameter. If a matrix output for the data is preferred, set exportTable = TRUE. } diff --git a/man/scatterClonotype.Rd b/man/clonalScatter.Rd similarity index 61% rename from man/scatterClonotype.Rd rename to man/clonalScatter.Rd index 88a61de6..5de69679 100644 --- a/man/scatterClonotype.Rd +++ b/man/clonalScatter.Rd @@ -1,10 +1,24 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/scatterClonotypes.R -\name{scatterClonotype} -\alias{scatterClonotype} +% Please edit documentation in R/clonalScatter.R, R/scatterClonotypes.R +\name{clonalScatter} +\alias{clonalScatter} \title{Scatter plot comparing the expansion of two samples} \usage{ -scatterClonotype( +clonalScatter( + df, + cloneCall = "strict", + x.axis = NULL, + y.axis = NULL, + chain = "both", + dot.size = "total", + split.by = NULL, + graph = "proportion", + exportTable = FALSE, + palette = "inferno", + seed = NULL +) + +clonalScatter( df, cloneCall = "strict", x.axis = NULL, @@ -47,9 +61,16 @@ to group the new list. NULL will return clusters.} \item{seed}{the integer seed to set for the random variation of point coords.} } \value{ +ggplot of the relative clonotype numbers + ggplot of the relative clonotype numbers } \description{ +This function produces a scatter plot directly comparing the specific clonotypes +between two samples. The clonotypes will be categorized by counts into singlets or multiplets, +either exclusive or shared between the selected samples. Visualization inspired +by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. + This function produces a scatter plot directly comparing the specific clonotypes between two samples. The clonotypes will be categorized by counts into singlets or multiplets, either exclusive or shared between the selected samples. Visualization inspired @@ -60,7 +81,18 @@ by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 202 combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -scatterClonotype(combined, x.axis = "P17B", y.axis = "P17L", -graph = "proportion") +clonalScatter(combined, + x.axis = "P17B", + y.axis = "P17L", + graph = "proportion") + +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +clonalScatter(combined, + x.axis = "P17B", + y.axis = "P17L", + graph = "proportion") } diff --git a/man/mini_contig_list.Rd b/man/mini_contig_list.Rd index ad1bb2c8..7e33a720 100644 --- a/man/mini_contig_list.Rd +++ b/man/mini_contig_list.Rd @@ -11,8 +11,8 @@ An R `list` of `data.frame` objects data("mini_contig_list") } \description{ -A list of 6 dataframes of T cell contigs outputted from the -`filtered_contig_annotation` files, but subsetted to about 92 valid T cells +A list of 8 dataframes of T cell contigs outputted from the +`filtered_contig_annotation` files, but subsetted to 365 valid T cells which correspond to the same barcodes found in `scRep_example` } \seealso{ diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd index fb729b69..44488d6d 100644 --- a/man/percentGenes.Rd +++ b/man/percentGenes.Rd @@ -44,4 +44,5 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) percentGenes(combined, chain = "TRB", gene = "Vgene") + } diff --git a/man/screp_example.Rd b/man/screp_example.Rd index 2e3d9d99..636e648a 100644 --- a/man/screp_example.Rd +++ b/man/screp_example.Rd @@ -3,8 +3,8 @@ \docType{data} \name{scRep_example} \alias{scRep_example} -\title{A seurat object of 100 single T cells derived -from 3 clear cell renal carcinoma patients.} +\title{A seurat object of 500 single T cells derived +from 4 patients with acute respiratory distress (ARDS).} \description{ The object is compatible with `contig_list` and the TCR sequencing data can be added with `combineExpression`. diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 577b2162..39e17a9e 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -493,7 +493,7 @@ clonalSizeDistribution(combined, method= "ward.D2") ``` -## Diversity Analysis +## clonalDiversity Diversity can also be measured for samples or by other variables. Diversity is calculated using five metrics: 1) Shannon, 2) inverse Simpson, 3) Chao1, and 4) Abundance-based Coverage Estimator (ACE), and 5) inverse Pielou. With the former two generally used to estimate baseline diversity and Chao/ACE indices used to estimate the richness of the samples. New implementation of this function include downsampling with 100 boot straps (**n.boots**) using the minimum number of unique clonotypes, as a more robust diversity estimate. To get a table of the bootstrapped values, set **return.boots** = TRUE. @@ -504,9 +504,9 @@ clonalDiversity(combined, n.boots = 100) ``` -## scatterClonotype +## clonalScatter -A number of users requested a visualization from the work of [Wu, et al 2020, PMID: 32103181](https://pubmed.ncbi.nlm.nih.gov/32103181/) that allows for the direct comparison of clonotypes between 2 samples. ```scatterClonotype()``` will organize two samples from the combineTCR/BCR product, count the relative clonotypes, and produce a scatter plot comparing the two samples. +A number of users requested a visualization from the work of [Wu, et al 2020, PMID: 32103181](https://pubmed.ncbi.nlm.nih.gov/32103181/) that allows for the direct comparison of clonotypes between 2 samples. ```clonalScatter()``` will organize two samples from the combineTCR/BCR product, count the relative clonotypes, and produce a scatter plot comparing the two samples. **x.axis** and **y.axis** * Names of the list element to place on the x-axis and y-axis - so for example "P18L" and "P18B" @@ -520,12 +520,12 @@ A number of users requested a visualization from the work of [Wu, et al 2020, PM * "count" for the total count of clonotypes by sample ```{r} -scatterClonotype(combined, - cloneCall ="gene", - x.axis = "P18B", - y.axis = "P18L", - dot.size = "total", - graph = "proportion") +clonalScatter(combined, + cloneCall ="gene", + x.axis = "P18B", + y.axis = "P18L", + dot.size = "total", + graph = "proportion") ``` *** From b6ed26fbff4ac152a3d8a210e324778431373609 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 22 Sep 2023 05:32:11 -0500 Subject: [PATCH 050/142] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ff39b16b..17769ce2 100644 --- a/README.md +++ b/README.md @@ -47,7 +47,7 @@ Unfortunately, Github limits the size of individual files. In order to access th Vignette available [here](https://www.borch.dev/uploads/vignette/vignette), includes almost 13,000 T cells across 3 patients with renal clear cell carcinoma. ## Please Cite -Feel free to use, edit, modify scRepertoire, but if you do, please cite the [manuscript](https://f1000research.com/articles/9-47/v1). +Feel free to use, edit, modify scRepertoire, but if you do, please cite the [manuscript](https://f1000research.com/articles/9-47/v1). If you are building your own tool based on scRepertoire, reach out, I am happy to help and make things compatible. ## Contact Questions, comments, suggestions, please feel free to contact Nick Borcherding via this repository, [email](mailto:ncborch@gmail.com), or using [twitter](https://twitter.com/theHumanBorch). From c1fb5a956daa9032d3ea4491e8f4fb4e39661e21 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 22 Sep 2023 08:03:06 -0500 Subject: [PATCH 051/142] Clean vignette --- DESCRIPTION | 2 +- LICENSE.md | 298 ++----------------------- NAMESPACE | 2 +- NEWS | 3 + R/{quantContig.R => clonalQuant.R} | 84 +++---- R/clonalScatter.R | 56 ++--- R/percentAA.R | 4 +- R/percentVJ.R | 1 + man/{quantContig.Rd => clonalQuant.Rd} | 10 +- man/clonalScatter.Rd | 32 +-- vignettes/vignette.Rmd | 231 +++++++++++-------- 11 files changed, 244 insertions(+), 479 deletions(-) rename R/{quantContig.R => clonalQuant.R} (64%) rename man/{quantContig.Rd => clonalQuant.Rd} (92%) diff --git a/DESCRIPTION b/DESCRIPTION index 3d3476ef..f20117ea 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -5,7 +5,7 @@ Authors@R: c( person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"), person(given = "Qile", family = "Yang", role = c("aut"), email = "qile0317@gmail.com")) Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. -License: GPL (>= 2) +License: MIT Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.3 diff --git a/LICENSE.md b/LICENSE.md index f67ff502..a68bbe78 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -1,277 +1,21 @@ -GNU General Public License -========================== - -_Version 2, June 1991_ -_Copyright © 1989, 1991 Free Software Foundation, Inc.,_ -_51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA_ - -Everyone is permitted to copy and distribute verbatim copies -of this license document, but changing it is not allowed. - -### Preamble - -The licenses for most software are designed to take away your -freedom to share and change it. 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IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/NAMESPACE b/NAMESPACE index b46ef90b..c72e126a 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -12,6 +12,7 @@ export(clonalNetwork) export(clonalOverlap) export(clonalOverlay) export(clonalProportion) +export(clonalQuant) export(clonalScatter) export(clonalSizeDistribution) export(clonotypeBias) @@ -29,7 +30,6 @@ export(occupiedscRepertoire) export(percentAA) export(percentGenes) export(percentVJ) -export(quantContig) export(subsetContig) export(vizGenes) import(dplyr) diff --git a/NEWS b/NEWS index 6ee6fe43..ceb1f7ce 100644 --- a/NEWS +++ b/NEWS @@ -8,6 +8,8 @@ NEW FEATURES * Added percentVJ() * Changed compareClonotypes to clonalCompare * Changed clonotypeSizeDistribution to clonalSizeDistribution +* Changed scatterClonotypes to clonalScatter +* Changed quantContig to clonalQuant UNDERLYING CHANGES @@ -15,6 +17,7 @@ UNDERLYING CHANGES * implemented powerTCR locally to reduce dependencies and continue support * Universalized underlying function language and intermediate variables * Added features to clonalCompare to allow for highlighting sequences, relabeling clonotypes. +* License change to MIT DEPRECATED AND DEFUNCT diff --git a/R/quantContig.R b/R/clonalQuant.R similarity index 64% rename from R/quantContig.R rename to R/clonalQuant.R index 37e755b4..d3d70c1c 100644 --- a/R/quantContig.R +++ b/R/clonalQuant.R @@ -12,7 +12,7 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' quantContig(combined, cloneCall="strict", scale = TRUE) +#' clonalQuant(combined, cloneCall="strict", scale = TRUE) #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). #' @param cloneCall How to call the clonotype - VDJC gene (gene), @@ -30,7 +30,7 @@ #' @import ggplot2 #' @export #' @return ggplot of the total or relative unique clonotypes -quantContig <- function(df, +clonalQuant <- function(df, cloneCall = "strict", chain = "both", scale=FALSE, @@ -51,63 +51,71 @@ quantContig <- function(df, df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) } } + + #Set up mat to store and selecting graph parameters if (!is.null(group.by)) { x <- group.by labs <- group.by - Con.df <- data.frame(matrix(NA, length(df), 4)) - colnames(Con.df) <- c("contigs","values", "total", group.by) + mat <- data.frame(matrix(NA, length(df), 4)) + colnames(mat) <- c("contigs","values", "total", group.by) for (i in seq_along(df)) { - Con.df[i,1] <- length(unique(df[[i]][,cloneCall])) - Con.df[i,2] <- names(df)[i] - Con.df[i,3] <- length(df[[i]][,cloneCall]) + mat[i,1] <- length(unique(df[[i]][,cloneCall])) + mat[i,2] <- names(df)[i] + mat[i,3] <- length(df[[i]][,cloneCall]) location <- which(colnames(df[[i]]) == group.by) - Con.df[i,4] <- df[[i]][1,location] } - col <- length(unique(Con.df[,group.by])) + mat[i,4] <- df[[i]][1,location] } + col <- length(unique(mat[,group.by])) if (scale) { y <- "scaled" - Con.df$scaled <- Con.df$contigs/Con.df$total*100 + mat$scaled <- mat$contigs/mat$total*100 ylab <- "Percent of Unique Clonotype" - } else { y <- "contigs" - x <- group.by - ylab <- "Unique Clonotypes"} + } else { + y <- "contigs" + x <- group.by + ylab <- "Unique Clonotypes"} } else { x <- "values" labs <- "Samples" - Con.df <- data.frame(matrix(NA, length(df), 3)) - colnames(Con.df) <- c("contigs","values", "total") + mat <- data.frame(matrix(NA, length(df), 3)) + colnames(mat) <- c("contigs","values", "total") + + #Generating quantification for (i in seq_along(df)) { - Con.df[i,1] <- length(unique(df[[i]][,cloneCall])) - Con.df[i,2] <- names(df)[i] - Con.df[i,3] <- length(df[[i]][,cloneCall]) } - col <- length(unique(Con.df$values)) - if (scale == TRUE) { y <- "scaled" - Con.df$scaled <- Con.df$contigs/Con.df$total*100 - ylab <- "Percent of Unique Clonotype" - } else { y <- "contigs" - ylab <- "Unique Clonotypes" } } - + mat[i,1] <- length(unique(df[[i]][,cloneCall])) + mat[i,2] <- names(df)[i] + mat[i,3] <- length(df[[i]][,cloneCall]) + } + col <- length(unique(mat$values)) + if (scale == TRUE) { + y <- "scaled" + mat$scaled <- mat$contigs/mat$total*100 + ylab <- "Percent of Unique Clonotype" + } else { + y <- "contigs" + ylab <- "Unique Clonotypes" + } + } if (exportTable) { if (length(df) > 1) { - return(Con.df) + return(mat) } - # if a single sample, remove the "values" column if NA - if (is.na(Con.df[[2]])) { - Con.df[[2]] <- NULL + if (is.na(mat[[2]])) { + mat[[2]] <- NULL } - return(Con.df) + return(mat) } if(order & is.null(group.by)) { - Con.df[,x] <- factor(Con.df[,x], levels = Con.df[,x]) + mat[,x] <- factor(mat[,x], levels = mat[,x]) } - plot <- ggplot(aes(x=Con.df[,x], y=Con.df[,y], - fill=as.factor(Con.df[,x])), data = Con.df) + - stat_summary(geom = "errorbar", fun.data = mean_se, - position = "dodge", width=.5) + labs(fill = labs) + - stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ - theme_classic() + xlab("Samples") + ylab(ylab) + - scale_fill_manual(values = .colorizer(palette, col)) + + plot <- ggplot(aes(x=mat[,x], y=mat[,y], fill=as.factor(mat[,x])), data = mat) + + stat_summary(geom = "errorbar", fun.data = mean_se, + position = "dodge", width=.5) + labs(fill = labs) + + stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ + theme_classic() + xlab("Samples") + ylab(ylab) + + scale_fill_manual(values = .colorizer(palette, col)) # if it is a single run, remove x axis labels if sample name missing if ((length(df) == 1) && identical(names(df), NA_character_)) { diff --git a/R/clonalScatter.R b/R/clonalScatter.R index f60d4be1..05208d24 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -65,63 +65,63 @@ clonalScatter <- function(df, colnames(x.df)[2] <- x.axis y.df <- as.data.frame(table(df[[y.axis]][,cloneCall])) colnames(y.df)[2] <- y.axis - Con.df <- merge(x.df, y.df, by = "Var1", all = TRUE) + mat <- merge(x.df, y.df, by = "Var1", all = TRUE) if (dot.size != "total") { - if (dot.size %!in% colnames(Con.df)) { + if (dot.size %!in% colnames(mat)) { size.df <- as.data.frame(table(df[[dot.size]][,cloneCall])) colnames(size.df)[2] <- dot.size - Con.df <- merge(Con.df, size.df, by = "Var1", all = TRUE) } - Con.df[is.na(Con.df)] <- 0 - Con.df[,paste0("size", ".fraction")] <- Con.df[,dot.size]/sum(Con.df[,dot.size]) + mat <- merge(mat, size.df, by = "Var1", all = TRUE) } + mat[is.na(mat)] <- 0 + mat[,paste0("size", ".fraction")] <- mat[,dot.size]/sum(mat[,dot.size]) labeling <- unique(c(x.axis, y.axis, dot.size)) } else { - Con.df[is.na(Con.df)] <- 0 + mat[is.na(mat)] <- 0 labeling <- unique(c(x.axis, y.axis)) } - Con.df[,"class"] <- NA - Con.df[,"sum"] <- rowSums(Con.df[,labeling]) + mat[,"class"] <- NA + mat[,"sum"] <- rowSums(mat[,labeling]) #Assigning class based on x and y-axis counts for (i in seq_along(labeling)) { if (length(labeling) > 2) { - Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] == 1 & rowSums(Con.df[,labeling[-i]]) == 0, + mat[,"class"] <- ifelse(mat[,labeling[i]] == 1 & rowSums(mat[,labeling[-i]]) == 0, paste0(labeling[i], ".singlet"), #if - Con.df[,"class"]) #else - Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] > 1 & rowSums(Con.df[,labeling[-i]]) == 0, + mat[,"class"]) #else + mat[,"class"] <- ifelse(mat[,labeling[i]] > 1 & rowSums(mat[,labeling[-i]]) == 0, paste0(labeling[i], ".expanded"), #if - Con.df[,"class"]) #else + mat[,"class"]) #else } else if (length(labeling) == 2) { - Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] == 1 & Con.df[,labeling[-i]] == 0, + mat[,"class"] <- ifelse(mat[,labeling[i]] == 1 & mat[,labeling[-i]] == 0, paste0(labeling[i], ".singlet"), #if - Con.df[,"class"]) #else - Con.df[,"class"] <- ifelse(Con.df[,labeling[i]] > 1 & Con.df[,labeling[-i]] == 0, + mat[,"class"]) #else + mat[,"class"] <- ifelse(mat[,labeling[i]] > 1 & mat[,labeling[-i]] == 0, paste0(labeling[i], ".expanded"), #if - Con.df[,"class"]) #else + mat[,"class"]) #else } } #Adding dual-expanded class - Con.df[,"class"] <- ifelse(Con.df[,y.axis] >= 1 & Con.df[,x.axis] >= 1, paste0("dual.expanded"), Con.df[,"class"]) + mat[,"class"] <- ifelse(mat[,y.axis] >= 1 & mat[,x.axis] >= 1, paste0("dual.expanded"), mat[,"class"]) #Calculating relative proportion - Con.df[,paste0(x.axis, ".fraction")] <- Con.df[,x.axis]/sum(Con.df[,x.axis]) - Con.df[,paste0(y.axis, ".fraction")] <- Con.df[,y.axis]/sum(Con.df[,y.axis]) + mat[,paste0(x.axis, ".fraction")] <- mat[,x.axis]/sum(mat[,x.axis]) + mat[,paste0(y.axis, ".fraction")] <- mat[,y.axis]/sum(mat[,y.axis]) #Altering the graphing parameters if (graph == "proportion") { - x <- Con.df[,paste0(x.axis, ".fraction")] - y <- Con.df[,paste0(y.axis, ".fraction")] + x <- mat[,paste0(x.axis, ".fraction")] + y <- mat[,paste0(y.axis, ".fraction")] } else if (graph == "count") { - x <- Con.df[,x.axis] - y <- Con.df[,y.axis] } + x <- mat[,x.axis] + y <- mat[,y.axis] } if (dot.size != "total") { - size <- Con.df[,dot.size] - } else { size <- Con.df[,"sum"] } - if (exportTable == TRUE) { return(Con.df) } + size <- mat[,dot.size] + } else { size <- mat[,"sum"] } + if (exportTable == TRUE) { return(mat) } - plot <- ggplot(Con.df, aes(x=x, y = y, fill = class)) + + plot <- ggplot(mat, aes(x=x, y = y, fill = class)) + theme_classic() + - scale_fill_manual(values = .colorizer(palette,length(unique(Con.df$class)))) + + scale_fill_manual(values = .colorizer(palette,length(unique(mat$class)))) + xlab(x.axis) + ylab(y.axis) + labs(size = "Total n") diff --git a/R/percentAA.R b/R/percentAA.R index 7a83f5fe..61f66187 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -69,7 +69,9 @@ percentAA <- function(df, if(length(res.list) > 1) { plot <- plot + facet_grid(group~.) } - if (exportTable == TRUE) { return(mat_melt) } + if (exportTable == TRUE) { + return(mat_melt) + } return(plot) } diff --git a/R/percentVJ.R b/R/percentVJ.R index 1c2d74f6..298defef 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -9,6 +9,7 @@ #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' percentVJ(combined, chain = "TRB") +#' #' @param df The product of combineTCR(), combineBCR(), #' expression2List(), or combineExpression(). #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" diff --git a/man/quantContig.Rd b/man/clonalQuant.Rd similarity index 92% rename from man/quantContig.Rd rename to man/clonalQuant.Rd index d8694c48..52fbb35a 100644 --- a/man/quantContig.Rd +++ b/man/clonalQuant.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/quantContig.R -\name{quantContig} -\alias{quantContig} +% Please edit documentation in R/clonalQuant.R +\name{clonalQuant} +\alias{clonalQuant} \title{Quantify the unique clonotypes in the filtered contigs.} \usage{ -quantContig( +clonalQuant( df, cloneCall = "strict", chain = "both", @@ -55,6 +55,6 @@ for the data is preferred, set exportTable = TRUE. combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -quantContig(combined, cloneCall="strict", scale = TRUE) +clonalQuant(combined, cloneCall="strict", scale = TRUE) } diff --git a/man/clonalScatter.Rd b/man/clonalScatter.Rd index 5de69679..c8f6cd17 100644 --- a/man/clonalScatter.Rd +++ b/man/clonalScatter.Rd @@ -1,23 +1,9 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/clonalScatter.R, R/scatterClonotypes.R +% Please edit documentation in R/clonalScatter.R \name{clonalScatter} \alias{clonalScatter} \title{Scatter plot comparing the expansion of two samples} \usage{ -clonalScatter( - df, - cloneCall = "strict", - x.axis = NULL, - y.axis = NULL, - chain = "both", - dot.size = "total", - split.by = NULL, - graph = "proportion", - exportTable = FALSE, - palette = "inferno", - seed = NULL -) - clonalScatter( df, cloneCall = "strict", @@ -61,16 +47,9 @@ to group the new list. NULL will return clusters.} \item{seed}{the integer seed to set for the random variation of point coords.} } \value{ -ggplot of the relative clonotype numbers - ggplot of the relative clonotype numbers } \description{ -This function produces a scatter plot directly comparing the specific clonotypes -between two samples. The clonotypes will be categorized by counts into singlets or multiplets, -either exclusive or shared between the selected samples. Visualization inspired -by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. - This function produces a scatter plot directly comparing the specific clonotypes between two samples. The clonotypes will be categorized by counts into singlets or multiplets, either exclusive or shared between the selected samples. Visualization inspired @@ -86,13 +65,4 @@ clonalScatter(combined, y.axis = "P17L", graph = "proportion") -#Making combined contig data -combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) -clonalScatter(combined, - x.axis = "P17B", - y.axis = "P17L", - graph = "proportion") - } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 39e17a9e..cf0742f8 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -38,7 +38,7 @@ quiet_bioc_load("BiocStyle", "scater") # Introduction -scRepertoire is designed to take filter contig outputs from the 10x Genomics Cell Ranger pipeline, processes that data to assign clonotype based on two TCR or Ig chains, and analyze the clonotype dynamics. The latter can be separated into 1) clonotype-only analysis functions, such as unique clonotypes or clonal space quantification and 2) interaction with mRNA expression data using Seurat, SingleCellExperiment or Monocle 3 packages. +scRepertoire is designed to take filter contig outputs from the 10x Genomics Cell Ranger pipeline, process that data to assign clonotype based on two TCR or Ig chains and analyze the clonotype dynamics. The latter can be separated into 1) clonotype-only analysis functions, such as unique clonotypes or clonal space quantification, and 2) interaction with mRNA expression data using Seurat, SingleCellExperiment or Monocle packages. ## Loading Libraries @@ -50,7 +50,7 @@ suppressMessages(library(scRepertoire)) ### What data to load into scRepertoire? -scRepertoire functions using the **filtered_contig_annotations.csv** output from the 10x Genomics Cell Ranger. This file is located in the ./outs/ directory of the VDJ alignment folder. To generate a list of contigs to use for scRepertoire, just 1) load the **filtered_contig_annotations.csv** for each of your samples and then 2) make a list. For example: +scRepertoire functions using the **filtered_contig_annotations.csv** output from the 10x Genomics Cell Ranger. This file is located in the ./outs/ directory of the VDJ alignment folder. To generate a list of contigs to use for scRepertoire, just 1) load the **filtered_contig_annotations.csv** for each of the samples and then 2) make a list. For example: ```{r, eval=FALSE} S1 <- read.csv(".../Sample1/outs/filtered_contig_annotations.csv") @@ -63,9 +63,9 @@ contig_list <- list(S1, S2, S3, S4) ### Multiplexed Experiment? -It is now easy to create the contig list from a multiplexed experiment by first generating a single-cell RNA object (either Seurat or Single Cell Experiment), loading the filtered contig file and then using `createHTOContigList()`. This function will return a list, separated by the **group.by** variable(s). +It is now easy to create the contig list from a multiplexed experiment by first generating a single-cell RNA object (either Seurat or Single Cell Experiment), loading the filtered contig file and then using `createHTOContigList()`. This function will return a list separated by the **group.by** variable(s). -This function depends on the match of barcodes between the single-cell object and contigs. If there is a prefix or different suffix added to the barcode, this will result in no contigs recovered. As of right now, it is recommended you do this step before the integrated, as integration workflows commonly alter the barcodes. There is a **multi.run** variable that can be used on the integrated object, but it assumes you have modified the barcodes with the Seurat pipeline (automatic addition of _# to end) and your contig list is in the same order. +This function depends on the match of barcodes between the single-cell object and contigs. If there is a prefix or different suffix added to the barcode, this will result in no contigs recovered. Currently, it is recommended you do this step before the integration, as integration workflows commonly alter the barcodes. There is a **multi.run** variable that can be used on the integrated object. However, it assumes you have modified the barcodes with the Seurat pipeline (automatic addition of _# to end), and your contig list is in the same order. ```{r, eval = F} contigs <- read.csv(".../outs/filtered_contig_annotations.csv") @@ -73,7 +73,7 @@ contigs <- read.csv(".../outs/filtered_contig_annotations.csv") contig.list <- createHTOContigList(contigs, Seurat.Obj, group.by = "HTO_maxID") ``` -scRepertoire comes with a data set derived from T cells derived from four patients with acute respiratory distress in order to demonstrate the functionality of the R package. More information on the data set can be found at the corresponding [manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/). The samples consist of paired peripheral-blood (B) and bronchoalveolar lavage (L), effectively creating 8 distinct runs for T cell receptor (TCR) enrichment. We can preview the elements in the list by using the head function and looking at the first contig annotation. +scRepertoire comes with a data set from T cells derived from four patients with acute respiratory distress to demonstrate the functionality of the R package. More information on the data set can be found in the corresponding [manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/). The samples consist of paired peripheral-blood (B) and bronchoalveolar lavage (L), effectively creating 8 distinct runs for T cell receptor (TCR) enrichment. We can preview the elements in the list by using the head function and looking at the first contig annotation. ```{r} data("contig_list") #the data built into scRepertoire @@ -83,7 +83,7 @@ head(contig_list[[1]]) ## loadContigs -New to this version of the package is support for BD Rhapsody, AIRR, [WAT3R](https://www.biorxiv.org/content/10.1101/2022.01.26.477886v1), and [TRUST4](https://www.nature.com/articles/s41592-021-01142-2) single-cell outputs. By indicating the directory of the outputs from these pipelines are located, ```loadContigs()``` will generate a contig list that is compatible with the rest of scRepertoire. Alternatively, ```loadContigs()``` will also accept a list of data frames that you have loaded individualy. In addition, although not necessary, ```loadContigs()``` can be used for 10X filtered_contig_annotation.csv files. +New to this version of the package is support for BD Rhapsody, AIRR, [WAT3R](https://www.biorxiv.org/content/10.1101/2022.01.26.477886v1), and [TRUST4](https://www.nature.com/articles/s41592-021-01142-2) single-cell outputs. By indicating the directory of the outputs from these pipelines are located, ```loadContigs()``` will generate a contig list that is compatible with the rest of scRepertoire. Alternatively, ```loadContigs()``` will also accept a list of data frames loaded individually. In addition, although unnecessary, ```loadContigs()``` can be used for 10X filtered_contig_annotation.csv files. ```{r, eval=FALSE} contig.output <- c("~/Documents/MyExperiment") @@ -121,13 +121,13 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) ``` -The output of `combineTCR()` will be a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It will also combine the multiple reads into clonotype calls by either the nucleotide sequence (CTnt), amino acid sequence (CTaa), the VDJC gene sequence (CTgene) or the combination of the nucleotide and gene sequence (CTstrict). The analogous function for B cells, `combineBCR()` functions similarly with 2 major caveats: 1) Each barcode can only have a maximum of 2 sequences, if greater exists, the 2 with the highest reads are selected. 2) The strict definition of clonotype (CTstrict) is based on the V gene and >85% normalized Levenshtein distance of the nucleotide sequence. The Levenshtein distance is calculated across all BCR sequences recovered, regardless of the run. +The output of `combineTCR()` will be a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It will also combine the multiple reads into clonotype calls by either the nucleotide sequence (CTnt), amino acid sequence (CTaa), the VDJC gene sequence (CTgene), or the combination of the nucleotide and gene sequence (CTstrict). The analogous function for B cells, `combineBCR()` functions similarly with 2 major caveats: 1) Each barcode can only have a maximum of 2 sequences, if greater exists, the 2 with the highest reads are selected. 2) The strict definition of clonotype (CTstrict) is based on the V gene and >85% normalized Levenshtein distance of the nucleotide sequence. Regardless of the run, the Levenshtein distance is calculated across all BCR sequences recovered. # Other Processing Functions ## addVariable -What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the Typees in which the samples were processed and sequenced. +What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. ```{r} combined <- addVariable(combined, @@ -139,7 +139,7 @@ combined[[1]][1:5,ncol(combined[[1]])] # This is showing the first 5 values of t ## subsetContig -Likewise we can remove specific list elements after `combineTCR()` using the `subsetContig()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and also the variable values to subset (**variables**). Below you can see us isolate just the 2 sequencing results from P18L and P18B. +Likewise, we can remove specific list elements after `combineTCR()` using the `subsetContig()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and the variable values to subset (**variables**). Below, we isolate just the 2 sequencing results from P18L and P18B. ```{r} subset <- subsetContig(combined, @@ -157,14 +157,14 @@ subset <- subsetContig(combined, * "aa" - use the amino acid sequence of the CDR3 region * "strict" - use the VDJC genes comprising the TCR + the nucleotide sequence of the CDR3 region. This is the [proper definition of clonotype](https://www.ncbi.nlm.nih.gov/pubmed/19568742). For ```combineBCR()``` strict refers to the edit distance clusters + Vgene of the Ig. -Important to note, that the clonotype is called using essentially the combination of genes or nt/aa CDR3 sequences for both loci. As of this implementation of scRepertoire, clonotype calling is not incorporating small variations within the CDR3 sequences. As such the **gene** approach will be the most sensitive, while the use of **nt** or **aa** moderately so, and the most specific for clonotypes being **strict**. Additionally, the clonotype call is trying to incorporate both loci, *i.e*, both **TCRA** and **TCRB** chains and if a single cell barcode has multiple sequences identified (*i.e.*, 2 TCRA chains expressed in one cell). Using the 10x approach, there is a subset of barcodes that only return one of the immune receptor chains, the unreturned chain is assigned an **NA** value. +It is important to note that the clonotype is called using essentially the combination of genes or nt/aa CDR3 sequences for both loci. As of this implementation of scRepertoire, clonotype calling is not incorporating small variations within the CDR3 sequences. As such the **gene** approach will be the most sensitive, while the use of **nt** or **aa** is moderately so, and the most specific for clonotypes being **strict**. Additionally, the clonotype call is trying to incorporate both loci, *i.e.*, both **TCRA** and **TCRB** chains and if a single cell barcode has multiple sequences identified (*i.e.*, 2 TCRA chains expressed in one cell). Using the 10x approach, there is a subset of barcodes that only return one of the immune receptor chains. The unreturned chain is assigned an **NA** value. -## quantContig +## clonalQuant -The first function to explore the clonotypes is `quantContig()` to return the total or relative numbers of unique clonotypes. +The first function to explore the clonotypes is `clonalQuant()` to return the total or relative numbers of unique clonotypes. **scale** -* TRUE - relative percent of unique clonotypes scaled by total size of the size of the clonotype repertoire +* TRUE - relative percent of unique clonotypes scaled by the total size of the clonal repertoire * FALSE - Report the total number of unique clonotypes **chain** @@ -172,46 +172,45 @@ The first function to explore the clonotypes is `quantContig()` to return the to + "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" to select single chain ```{r} -quantContig(combined, +clonalQuant(combined, cloneCall="strict", chain = "both", scale = TRUE) ``` -For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme -+ Change the **palette** parameter to desired color scheme. This approach uses the built-in palettes of grDevices and you can access the list of color schemes using ``hcl.pals()``. -+ Add an additional ggplot layer with new color scheme. +For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme/ + ++ Change the **palette** parameter to the desired color scheme. This approach uses the built-in palettes of grDevices and we can access the list of color schemes using ``hcl.pals()``. ++ Add a ggplot layer with a new color scheme. ```{r} -quantContig(combined, +clonalQuant(combined, cloneCall="strict", chain = "both", scale = TRUE, palette = "Zissou 1") -quantContig(combined, +clonalQuant(combined, cloneCall="strict", chain = "both", scale = TRUE) + scale_color_manual(values = hcl.colors(8,"geyser")) - - ``` -Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph, instead of the visual output. +Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph instead of the visual output. ```{r} -quantContig_output <- quantContig(combined, +clonalQuant_output <- clonalQuant(combined, cloneCall="strict", scale = TRUE, exportTable = TRUE) -quantContig_output +clonalQuant_output ``` -Another option here is to be able to define the visualization by data classes. Here we used the *combineTCR()* to define the **Type** variable as part of the naming structure. We can the **group.by** to specifically use a column in the data set to organize the visualization. +Another option here is to be able to define the visualization by data classes. Here, we used the *combineTCR()* to define the **Type** variable as part of the naming structure. We can use the **group.by** to specifically use a column in the data set to organize the visualization. ```{r} -quantContig(combined, +clonalQuant(combined, cloneCall="gene", group.by = "Type", scale = TRUE) @@ -248,11 +247,7 @@ We can also examine the relative distribution of clonotypes by abundance. Here ` abundanceContig(combined, cloneCall = "gene", scale = FALSE) -``` -As you can see the peripheral blood sample derived from patient 1 is a relative extreme outlier. Another method to examine the relative abundance is to look at the density by using the **scale** call in the function. - -```{r} abundanceContig(combined, group.by = "Type", scale = TRUE) @@ -291,18 +286,6 @@ We can also look at clonotypes between samples and changes in dynamics by using **top.clones** * The top number of clonotypes to graph, this will be calculated based on the frequency of the individual sample. This can also be left blank. -We can also look at clonotypes between samples and changes in dynamics by using the `compareClonotypes()` function. - -**samples** -* Can be used to isolate specific samples based on the name of the list element - -**graph** -* "alluvial" - graph imaged below -* "area" - graph by area of the respective clonotype - -**top.clones** -* The top number of clonotypes to graph, this will be calculated based on the frequency of the individual sample. This can also be left blank. - **clonotypes** * Can be used to isolate specific clonotype sequences, ensure the call matches the sequences you would like to visualize. @@ -319,8 +302,8 @@ clonalCompare(combined, cloneCall="aa", graph = "alluvial") ``` -We can also choose to highlight specific clonotypes, such as in the case of *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*: +We can also choose to highlight specific clonotypes, such as in the case of *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*: ```{r} clonalCompare(combined, @@ -334,27 +317,7 @@ clonalCompare(combined, ## vizGenes -A visualizations of the relative usage of genes of the TCR or BCR, using `vizGenes()`. - -**gene** -+ "V" -+ "D" -+ "J" -+ "C" - -**chain** -+ "TRB" -+ "TRA" -+ "TRG" -+ "TRD" -+ "IGH" -+ "IGL" - -**plot** -+ "bar" for a bar chart -+ "heatmap" for a heatmap - -Last of the basic analysis visualizations is the relative usage of genes of the TCR or BCR, using `vizGenes()`. +A visualization of the relative usage of genes of the TCR or BCR, using `vizGenes()`. **gene** + "V" @@ -375,7 +338,7 @@ Last of the basic analysis visualizations is the relative usage of genes of the + "heatmap" for a heatmap **y.axis** -Variable to separate the counts along the y-axis. If not specified +Variable to separate the counts along the y-axis. If not specified, the default is to separate by the list element names. **scale** @@ -394,7 +357,7 @@ vizGenes(combined, scale = TRUE) ``` -We can also use `vizGenes()` to look at the usage of genes in a single chain. So for example say we are interesting in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: +We can also use `vizGenes()` to look at the usage of genes in a single chain. For example, say we are interested in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: ```{r} #Peripheral Blood @@ -418,7 +381,7 @@ vizGenes(combined[c(2,4,6,8)], ## percentAA -Quantify the proportion of amino acids along the cdr3 sequence with `percentAA()`. By default the function will pad the sequences with NAs up to the maximum of **aa.length**. Sequences longer than **aa.length** will be removed before visualization. +Quantify the proportion of amino acids along the cdr3 sequence with `percentAA()`. By default, the function will pad the sequences with NAs up to the maximum of **aa.length**. Sequences longer than **aa.length** will be removed before visualization. ```{r} percentAA(combined, @@ -426,15 +389,76 @@ percentAA(combined, aa.length = 20) ``` +## percentGenes + +Quantify the proportion of V or J gene usage with `percentGenes()`. Like `percentAA()`, we select the chain of interest and then indicate the gene of interest with the **gene** parameter. Two major limitations of `percentGenes()` are, 1) the function quantifies only V or J genes, and 2) the quantification of the genes are limited to all the V or J genes seen across the samples, not all possible V or J genes. + +```{r} +percentGenes(combined, + chain = "TRB", + gene = "Vgene") +``` + +We can also use the output `percentGenes()` for dimensional reduction to summarise the gene usage by sample. This can be done with a simple principal component analysis (below) or even more complex reductions. + +```{r} +df.genes <- percentGenes(combined, + chain = "TRB", + gene = "Vgene", + exportTable = TRUE) + +#Performing PCA +pc <- prcomp(df.genes) + +#Getting data frame to plot from +df <- cbind(pc$x[,1:2], rownames(df.genes)) %>% as.data.frame() +df$PC1 <- as.numeric(df$PC1) +df$PC2 <- as.numeric(df$PC2) + +#Plotting +ggplot(df, aes(x = PC1, y = PC2)) + + geom_point(aes(fill =df[,3]), shape = 21, size = 5) + + guides(fill=guide_legend(title="Samples")) + + scale_fill_manual(values = hcl.colors(nrow(df), "inferno")) + + theme_classic() +``` + +## percentVJ + +Quantify the proportion of V and J gene usage with `percentVJ()`. Like `percentGenes()`, this function will quantify the percentage of V and J paired together across individual repertoires. The output can be visualized using a heatmap or as input for futher dimensional reduction. + +```{r} +percentVJ(combined, + chain = "TRB") + +df.genes <- percentVJ(combined, + chain = "TRB", + exportTable = TRUE) + +#Performing PCA +pc <- prcomp(df.genes) + +#Getting data frame to plot from +df <- cbind(pc$x[,1:2], rownames(df.genes)) %>% as.data.frame() +df$PC1 <- as.numeric(df$PC1) +df$PC2 <- as.numeric(df$PC2) + +#Plotting +ggplot(df, aes(x = PC1, y = PC2)) + + geom_point(aes(fill =df[,3]), shape = 21, size = 5) + + guides(fill=guide_legend(title="Samples")) + + scale_fill_manual(values = hcl.colors(nrow(df), "inferno")) + + theme_classic() +``` *** # More Advanced Clonal Analysis -After we have completed the basic processing and summary functions in scRepertoire, we can begin to explore the clonotypes of the single-cell data in more detail. +After completing the basic processing and summary functions in scRepertoire, we can explore the clonotypes of the single-cell data in more detail. ## clonalHomeostasis -By examining the clonal space, we are effectively looking at the relative space occupied by clones at specific proportions. Another way to think about this would be thinking of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different number of clonal proportions. Clonal space homeostasis is asking what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cutpoints are set under the **cloneType** variable in the function and can be adjusted, at baseline the bins are as follows: +By examining the clonal space, we effectively look at the relative space occupied by clones at specific proportions. Another way to think about this would be to think of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different numbers of clonal proportions. Clonal space homeostasis asks what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cutpoints are set under the **cloneType** variable in the function and can be adjusted. **cloneTypes** * Rare = .0001 @@ -454,9 +478,9 @@ clonalHomeostasis(combined, ## clonalProportion -Like clonal space homeostasis above, clonal proportion acts to place clones into separate bins. The key difference is instead of looking at the relative proportion of the clone to the total, the `clonalProportion()` function will rank the clones by total number and place them into bins. +Like clonal space homeostasis above, clonal proportion places clones into separate bins. The key difference is that instead of looking at the relative proportion of the clone to the total, the `clonalProportion()` function will rank the clones by total number and place them into bins. -The **split** represents ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **split** variable in the function and can be adjusted, but at baseline they are as follows. +The **split** represents the ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **split** variable in the function and can be adjusted, but they are as follows at baseline. **split** * 10 @@ -477,7 +501,7 @@ clonalProportion(combined, ## clonalOverlap -If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. Three methods currently can be performed in `clonalOverlap()` 1) overlap coefficient, 2) Morisita index, or 3) Jaccard index. The former is looking at the overlap of clonotypes scaled to the length of unique clonotypes in the smaller sample. The Morisita index is more complex, it is an ecological measure of the dispersion of individuals within a population, incorporating the size of the population. The Jaccard Similarity Index is very similar to the overlap coefficient - instead of using the length of the smaller sample, the denominator for the Jaccard Index is the union of the two comparisons, leading to a much smaller number. +If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. Three methods currently can be performed in `clonalOverlap()` 1) overlap coefficient, 2) Morisita index, 3) Jaccard index, 4) Cosine similarity, and 5) raw numbers. ```{r} clonalOverlap(combined, @@ -485,7 +509,9 @@ clonalOverlap(combined, method = "morisita") ``` -Another addition to scRepertoire is the ability to cluster the samples by the clone size distribution using `clonalSizeDistribution()` adapted from the [powerTCR](https://bioconductor.org/packages/release/bioc/html/powerTCR.html) R package. Please read and cite the respective [citation](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006571) if using this function to analyze the similarities of sample clone size distributions. In this function, method refers to the method for which the hierarchical clustering will be based on. +## clonalSizeDistribution + +Another addition to scRepertoire is the ability to cluster the samples by the clone size distribution adapted from the [powerTCR](https://bioconductor.org/packages/release/bioc/html/powerTCR.html) R package. Please read and cite the respective [citation](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1006571) if using this function to analyze the similarities of sample clone size distributions. In this function, method refers to the method on which the hierarchical clustering will be based. ```{r} clonalSizeDistribution(combined, @@ -495,13 +521,23 @@ clonalSizeDistribution(combined, ## clonalDiversity -Diversity can also be measured for samples or by other variables. Diversity is calculated using five metrics: 1) Shannon, 2) inverse Simpson, 3) Chao1, and 4) Abundance-based Coverage Estimator (ACE), and 5) inverse Pielou. With the former two generally used to estimate baseline diversity and Chao/ACE indices used to estimate the richness of the samples. New implementation of this function include downsampling with 100 boot straps (**n.boots**) using the minimum number of unique clonotypes, as a more robust diversity estimate. To get a table of the bootstrapped values, set **return.boots** = TRUE. +Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: "shannon" "inv.simpson", "norm.entropy", "gini.simpson", "chao1", and "ACE". + +#' @param n.boots number of bootstraps to downsample in order to get mean diversity +#' @param return.boots export boot strapped values calculated - +#' will automatically exportTable = TRUE +#' @param skip.boots remove downsampling and boot strapping from the calculation + +With the former two generally used to estimate baseline diversity and Chao/ACE indices used to estimate the richness of the samples. New implementation of this function include downsampling with 100 boot straps (**n.boots**) using the minimum number of unique clonotypes, as a more robust diversity estimate. To get a table of the bootstrapped values, set **return.boots** = TRUE. ```{r} clonalDiversity(combined, cloneCall = "gene", group.by = "sample", n.boots = 100) + + +clonalDiversity(combined, cloneCall = "gene") ``` ## clonalScatter @@ -512,7 +548,7 @@ A number of users requested a visualization from the work of [Wu, et al 2020, PM * Names of the list element to place on the x-axis and y-axis - so for example "P18L" and "P18B" **dot.size** -* "total" to display the total number of clones between the the x- and y-axis +* "total" to display the total number of clones between the x- and y-axis * Name of the list element to use for size calculation **graph** @@ -532,7 +568,7 @@ clonalScatter(combined, # Interacting with mRNA Expression -The data in the scRepertoire package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. For the purposes of the vignette, we have randomly sampled 500 cells from the the fully integrated Seurat object to minimize the size of the package. A full version of the Seurat object is available via [GitHub](https://github.com/ncborcherding/scRepertoire) under the *Getting Data* header. We will use both Seurat and SingleCellExperiment (SCE) with scater to perform the further visualizations in tandem. +The data in the scRepertoire package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. For the vignette, we randomly sampled 500 cells from the fully integrated Seurat object to minimize the package size. A full version of the Seurat object is available via [GitHub](https://github.com/ncborcherding/scRepertoire) under the *Getting Data* header. We will use both Seurat and Single Cell Experiment (SCE) with scater to perform further visualizations in tandem. ```{r} scRep_example <- get(data("scRep_example")) @@ -547,11 +583,11 @@ plotUMAP(sce, colour_by = "seurat_clusters") ## combineExpression -Next we can take the clonotypic information and attach it to our Seurat object using the *combineExpression()* function. **Importantly**, the major requirement for the attachment is matching contig cell barcodes and barcodes in the row names of the meta data of the Seurat or SCE object. If these do not match, the attachment will fail. Based on ease, we suggest you make the changes to the Seurat object row names. +Next, we can attach the clonotypic information to our Seurat object using the `combineExpression()` function. function. **Importantly**, the major requirement for the attachment is matching contig cell barcodes and barcodes in the row names of the meta data of the Seurat or SCE object. If these do not match, the attachment will fail. Based on ease, we suggest making changes to the single-cell object barcodes. -We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 amino acid sequence, 3) CDR3 nucleotide sequence, or 4) VDJC genes and CDR3 nucleotide sequence. The attaching function will also calculate the frequency of the clonotype based on the **group.by** variable. If blank, **group.by** will calculate frequencies of clonotypes by individual run, but because we have 6 samples of paired peripheral blood and lung T cells, we are actually going to use the group.by variable to call "sample" in order to calculate frequencies across both the blood and lung T cells of the same patient. +We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 amino acid sequence, 3) CDR3 nucleotide sequence, or 4) VDJC genes and CDR3 nucleotide sequence. The attaching function will also calculate the clonotype frequency based on the **group.by** variable. If blank, **group.by** will calculate frequencies of clonotypes by individual run, but because we have 8 samples of paired peripheral blood and lung T cells, we will use the group.by variable to call "sample" to calculate frequencies across the same patient's blood and lung T cells. -In order to categorize the frequency, we have the variable **proportion** which if TRUE allows for the relative proportion or when FALSE will use absolute frequency to define clonotype groups **cloneTypes** acts as a bin to place labels. As a default, **cloneTypes** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below you can see an example of using total frequency as expansion assignments. +In order to categorize the frequency, we have the variable **proportion**, which if TRUE, allows for the relative proportion or when FALSE, will use absolute frequency to define clonotype groups **cloneTypes** acts as a bin to place labels. As a default, **cloneTypes** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below is an example of using total frequency as expansion assignments. ```{r} sce <- combineExpression(combined, @@ -570,10 +606,10 @@ scRep_example <- combineExpression(combined, ## Combining both TCR and BCR -If you have performed TCR/BCR enrichment or want to add info for both gamma-delta and alpha-beta T cells, now we can just make a single list and use ```combineExpression()```. **Major note** if there are duplicate barcodes (if a cell has both Ig and TCR), the immune receptor information will not be added. As an anecdote, the [testing data](https://support.10xgenomics.com/single-cell-vdj/datasets/6.0.1/SC5v2_Melanoma_5Kcells_Connect_single_channel_SC5v2_Melanoma_5Kcells_Connect_single_channel) we used to improve this function had 5-6% of barcode overlap. +If we have TCR/BCR enrichment or want to add info for gamma-delta and alpha-beta T cells, we can make a single list and use ```combineExpression()```. **Major note** if there are duplicate barcodes (if a cell has both Ig and TCR), the immune receptor information will not be added. As an anecdote, the [testing data](https://support.10xgenomics.com/single-cell-vdj/datasets/6.0.1/SC5v2_Melanoma_5Kcells_Connect_single_channel_SC5v2_Melanoma_5Kcells_Connect_single_channel) we used to improve this function had 5-6% of barcode overlap. ```{r, eval=FALSE} -#This is an example of the process, will not be evaluated during knit +#This is an example of the process, which will not be evaluated during knit TCR <- combineTCR(...) BCR <- combineBCR(...) list.receptors <- c(TCR, BCR) @@ -585,7 +621,7 @@ seurat <- combineExpression(list.receptors, proportion = TRUE) ``` -We first want to look at the distribution of peripheral blood versus lung T cells. We can use the same color scheme as the rest of the scRepertoire package by calling the object **colorblind_vector** using the following hex codes. +We first want to look at the distribution of peripheral blood versus lung T cells. Using the following hex codes, we can use the same color scheme as the rest of the scRepertoire package by calling the object **colorblind_vector**. ```{r} colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) @@ -606,7 +642,7 @@ Seurat::DimPlot(scRep_example, group.by = "cloneType") + ## clonalOverlay -Using the dimensional reduction graphs as a reference, we can also generate an overlay of the position of clonally expanded cells using `clonalOverlay()`. Select the **reduction** for the visualization, default is "PCA" and the **freq.cutpoint** or lowest clonal frequency or proportion to generate the contour plot. We can modify the contours by selecting the number of **bins** or number of contours drawn. `clonalOverlay()` can be used to look across all cells or faceted by a meta data variable using **facet**. As we facet, the overall dimensional reduction will be maintained, while the contour plots will adjust based on the **facet** variable. Coloring of the dot plot is based on the active identity of the single-cell object. This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs). +Using the dimensional reduction graphs as a reference, we can also generate an overlay of the position of clonally expanded cells using `clonalOverlay()`. Select the **reduction** for the visualization, default is "PCA" and the **freq.cutpoint** or lowest clonal frequency or proportion to generate the contour plot. We can modify the contours by selecting the number of **bins** or the number of contours drawn. `clonalOverlay()` can be used to look across all cells or faceted by a meta data variable using **facet**. The overall dimensional reduction will be maintained as we facet, while the contour plots will adjust based on the **facet** variable. The coloring of the dot plot is based on the active identity of the single-cell object. This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs). ```{r} clonalOverlay(scRep_example, @@ -619,9 +655,10 @@ clonalOverlay(scRep_example, ## clonalNetwork -Similar to `clonalOverlay()`, we can look at the network interaction of clonotypes shared between clusters along the single-cell dimensional reduction using `clonalNetwork()`. This function shows the relative proportion of clones that come from the starting node, with the ending node indicated by the arrow. +Similar to `clonalOverlay()`, we can look at the network interaction of clonotypes shared between clusters along the single-cell dimensional reduction using `clonalNetwork()`. This function shows the relative proportion of clones from the starting node, with the ending node indicated by the arrow. Filtering for clones can be accomplished using 3 methods: + **filter.clones** * Select a number to isolate the clones comprising the top X number of cells (filter.clones = 2000) * Select "min" to make sure all groups are scaled to the size of the minimum group @@ -666,7 +703,7 @@ head(shared.clones) ## highlightClonotypes -We can also look at the clonotypes by calling specific sequences in the `highlightClonotypes()` below. In order to highlight the clonotypes, we first need to use the **cloneCall** the type of sequence we will be using and then the specific sequences themselves using **sequence**. Below you can see the steps to highlight the two most prominent sequences *"CAVNGGSQGNLIF_CSAEREDTDTQYF"* with a frequency = 482 and *"NA_CATSATLRVVAEKLFF"* with a frequency = 287. +We can also look at the clonotypes by calling specific sequences in the `highlightClonotypes()` below. In order to highlight the clonotypes, we first need to use the **cloneCall**, the type of sequence we will be using, and then the specific sequences themselves using **sequence**. Below, we can see the steps to highlight the two most prominent sequences *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*. ```{r} scRep_example <- highlightClonotypes(scRep_example, @@ -679,10 +716,10 @@ Seurat::DimPlot(scRep_example, group.by = "highlight") ## occupiedscRepertoire -We can also look at the count of cells by cluster assigned into specific frequency ranges, by using the `occupiedscRepertoire()` function and selecting the **x.axis** to display cluster or other variables in the meta data of the single cell object. +We can also look at the count of cells by cluster assigned into specific frequency ranges by using the `occupiedscRepertoire()` function and selecting the **x.axis** to display cluster or other variables in the meta data of the single cell object. -+ **proportion** can be used to looked at a relative level groupings -+ **label** will still return absolute number of clonotypes ++ **proportion** can be used to look at relative level groupings ++ **label** will still return the absolute number of clonotypes ```{r} occupiedscRepertoire(scRep_example, @@ -696,7 +733,7 @@ occupiedscRepertoire(scRep_example, ## alluvialClonotypes -After the metadata has been modified, we can look at clonotypes across multiple categories using the `alluvialClonotypes()` function. To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html), essentially we are able to use the plots to examine the interchange of categorical variables. Because this function will produce a graph with each clonotype arranged by called stratification, this will take some time depending on the size of your total cells. To expedite, we will actually subset the Seurat object before using `alluvialClonotypes()`. +After the metadata has been modified, we can look at clonotypes across multiple categories using the `alluvialClonotypes()` function. To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html). We are able to use the plots to examine the interchange of categorical variables. Because this function will produce a graph with each clonotype arranged by called stratification, this will take some time depending on the size of your total cells. To expedite, we will subset the Seurat object before using `alluvialClonotypes()`. ```{r} alluvialClonotypes(scRep_example, @@ -714,7 +751,7 @@ alluvialClonotypes(scRep_example, ## getCirclize -Like alluvial graphs, we can also visualize the interconnection of clusters using the chord diagrams from the circlize R package. The first step is getting the data frame output to feed into the `chordDiagram()` function in circlize, which can be done using `getCirclize()`. This will calculate the relative number of clonotypes shared based on the **group.by** variable using the product of `combineExpression()`. Important to note, `getCirclize()` will create a matrix the size of the **group.by** variable and then simplify into set of instructions to be read by the circlize R package. The output of which is the total number of unique and shared clones by the **group.by** variable - this will be greater than the number of unique clonotypes due to the shared clone information. +Like alluvial graphs, we can also visualize the interconnection of clusters using the chord diagrams from the circlize R package. The first step is getting the data frame output to feed into the `chordDiagram()` function in circlize, which can be done using `getCirclize()`. This will calculate the relative number of clonotypes shared based on the **group.by** variable using the product of `combineExpression()`. It is important to note `getCirclize()` will create a matrix the size of the **group.by** variable and then simplify into instructions to be read by the circlize R package. The output is the total number of unique and shared clones by the **group.by** variable - this will be greater than the number of unique clonotypes due to the shared clone information. ```{r} library(circlize) @@ -731,7 +768,7 @@ names(grid.cols) <- unique(scRep_example$seurat_clusters) chordDiagram(circles, self.link = 1, grid.col = grid.cols) ``` -This can also be used if we wanted to explore just the lung-specific T cells by just subsetting the single-cell object. +This can also be used if we want to explore just the lung-specific T cells by just subsetting the single-cell object. ```{r} subset <- subset(scRep_example, Type == "L") @@ -752,14 +789,14 @@ chordDiagram(circles, ## clusterTCR -The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes, by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function, but now can be applied to the T cell receptors. It will add to the end of each list element a cluster by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. +The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the T cell receptors. It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. Clonotype cluster by distance: * TCRA:LD.1 [chain:] + :LD + [number] * TCRA.123 [chain:] + [number] -LD denotes if the cluster was called using the normalized Levenshtein distance +LD denotes if the cluster was called using the normalized Levenshtein distance. If performed over the number of samples, such as the list elements, **group.by** can used to calculate only the clusters on the setting of patient sample (group.by = "Patient") or tissue type (group.by = "Type"). This will add the selected group to the beginning of the cluster designation. @@ -774,9 +811,9 @@ sub_combined <- clusterTCR(combined[[2]], ## StartracDiversity -From the excellent work by Lei Zhang, et al in [Lineage tracking reveals dynamic relationships of T cells in colorectal cancer](https://www.nature.com/articles/s41586-018-0694-x), the authors introduce new methods for looking at clonotypes by cellular origins and cluster identification. Their [startrac](https://github.com/Japrin/STARTRAC) software has been incorporated into scRepertoire. If you are using the output of this specific function, please cite their excellent work. +From the excellent work by Lei Zhang, et al. in [Lineage tracking reveals dynamic relationships of T cells in colorectal cancer](https://www.nature.com/articles/s41586-018-0694-x), the authors introduce new methods for looking at clonotypes by cellular origins and cluster identification. Their [startrac](https://github.com/Japrin/STARTRAC) software has been incorporated into scRepertoire and please cite their excellent work. -In order to use the ```StartracDiversity()``` function, you will need to include the product of the ```combinedSeurat()``` function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data,**type** corresponds to the column "Type", which includes the "P" and "T" classifier. The indices can be subsetted for a specific patient or examined overall using the **by** variable. Importantly, the function uses only the strict definition of clonotype of the VDJC genes and the CDR3 nucleotide sequence. +In order to use the ```StartracDiversity()``` function, you will need to include the product of the ```combinedExpression()``` function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data,**type** corresponds to the column "Type", which includes the "P" and "T" classifiers. The indices can be subsetted for a specific patient or examined overall using the **by** variable. Importantly, the function uses only the strict definition of a clonotype of the VDJC genes and the CDR3 nucleotide sequence. *The indices output includes:* + expa - Clonal Expansion @@ -811,7 +848,7 @@ clonotypeBias(scRep_example, # Working with clonotypes after clustering -For users that would like greater ability to use the meta data in the Seurat objects to perform the analysis that scRepertoire provides, there is also the option of using the *expression2List()* function that will take the meta data and output the data as a list by cluster. +For users that would like the greater ability to use the meta data in the Seurat objects to perform the analysis that scRepertoire provides, there is also the option of using the *expression2List()* function that will take the meta data and output the data as a list by cluster. ```{r} combined2 <- expression2List(scRep_example, split.by = "seurat_clusters") @@ -845,7 +882,7 @@ clonalOverlap(combined3, cloneCall="aa", method="overlap") *** # Conclusion -This has been a general overview of the capabilities for scRepertoire from the initial processing and visualization to attach to the mRNA expression values in a Seurat object. If you have any questions, comments or suggestions, feel free to visit the github repository or [email me](mailto:ncborch@gmail.com). +This has been a general overview of the capabilities of scRepertoire from the initial processing and visualization to attach to the mRNA expression values in a single-cell object. If you have any questions, comments, or suggestions, please visit the GitHub repository or [email me](mailto:ncborch@gmail.com). ###Session Info From 27113ec307df7a885ba7b7fb0a085516077fddb9 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 22 Sep 2023 09:41:36 -0500 Subject: [PATCH 052/142] Create exportClones.R --- R/exportClones.R | 53 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 53 insertions(+) create mode 100644 R/exportClones.R diff --git a/R/exportClones.R b/R/exportClones.R new file mode 100644 index 00000000..402ec464 --- /dev/null +++ b/R/exportClones.R @@ -0,0 +1,53 @@ +#' Exporting clonotypes +#' +#' This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) +#' by barcodes. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' +#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param group.by The column header used for grouping. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @importFrom dplyr bind_rows +#' @importFrom stringr str_split +#' @export +#' @return ggplot of percentage of V and J gene pairings as a heatmap +#' @author Jonathan Noonan, Nick Borcherding +exportClones <- function(df, + group.by = NULL, + split.by = NULL, + dir = NULL, + file.name = "clones.csv") { + + df <- list.input.return(df, split.by) + df <- checkBlanks(df, "CTgene") + + df <- bind_rows(df, .id = "group") + + genes <- str_split(df[,"CTgene"], "_", simplify = TRUE) + aa <- str_split(df[,"CTaa"], "_", simplify = TRUE) + nt <- str_split(df[,"CTnt"], "_", simplify = TRUE) + chain1_gene <- str_split(genes[,1], "[.]", simplify = TRUE) + + mat <- data.frame(row.names = df[,"barcode"], + chain1_aa = aa[,1], + chain1_nt = nt[,1], + chain1_genes = genes[,1], + chain2_aa = aa[,2], + chain2_nt = nt[,2], + chain2_genes = genes[,2], + group = df[,"group"]) + mat[mat == "NA"] <- NA + + + if(is.null(dir)) { + dir <- "./" + } + filepath <- paste0(dir, file.name) + write.csv(mat, file = filepath) +} \ No newline at end of file From cd5ddd6a3ece4184968da70efd3261862e258b97 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 22 Sep 2023 10:43:24 -0500 Subject: [PATCH 053/142] Pass check --- NAMESPACE | 1 + NEWS | 1 + R/exportClones.R | 6 ++++++ man/exportClones.Rd | 42 ++++++++++++++++++++++++++++++++++++++++++ man/percentVJ.Rd | 1 + vignettes/vignette.Rmd | 5 +++-- 6 files changed, 54 insertions(+), 2 deletions(-) create mode 100644 man/exportClones.Rd diff --git a/NAMESPACE b/NAMESPACE index c72e126a..d45619f0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -21,6 +21,7 @@ export(combineBCR) export(combineExpression) export(combineTCR) export(createHTOContigList) +export(exportClones) export(expression2List) export(getCirclize) export(highlightClonotypes) diff --git a/NEWS b/NEWS index ceb1f7ce..92a6e2cf 100644 --- a/NEWS +++ b/NEWS @@ -6,6 +6,7 @@ NEW FEATURES * Added percentAA() * Added percentGenes() * Added percentVJ() +* Added exportClones() to get a csv of paired sequences * Changed compareClonotypes to clonalCompare * Changed clonotypeSizeDistribution to clonalSizeDistribution * Changed scatterClonotypes to clonalScatter diff --git a/R/exportClones.R b/R/exportClones.R index 402ec464..08445eea 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -4,17 +4,23 @@ #' by barcodes. #' #' @examples +#' \dontrun{ #' #Making combined contig data #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) +#' exportClones(combined) +#' } #' #' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. +#' @param dir directory location to save the csv +#' @param file.name the cav file name #' @importFrom dplyr bind_rows #' @importFrom stringr str_split +#' @importFrom utils write.csv #' @export #' @return ggplot of percentage of V and J gene pairings as a heatmap #' @author Jonathan Noonan, Nick Borcherding diff --git a/man/exportClones.Rd b/man/exportClones.Rd new file mode 100644 index 00000000..d0d54cdc --- /dev/null +++ b/man/exportClones.Rd @@ -0,0 +1,42 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/exportClones.R +\name{exportClones} +\alias{exportClones} +\title{Exporting clonotypes} +\usage{ +exportClones( + df, + group.by = NULL, + split.by = NULL, + dir = NULL, + file.name = "clones.csv" +) +} +\arguments{ +\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} + +\item{group.by}{The column header used for grouping.} + +\item{split.by}{If using a single-cell object, the column header +to group the new list. NULL will return clusters.} +} +\value{ +ggplot of percentage of V and J gene pairings as a heatmap +} +\description{ +This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) +by barcodes. +} +\examples{ +\dontrun{ +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +exportClones(combined) +} + +} +\author{ +Jonathan Noonan, Nick Borcherding +} diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd index 8806b755..25c66aa2 100644 --- a/man/percentVJ.Rd +++ b/man/percentVJ.Rd @@ -41,4 +41,5 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) percentVJ(combined, chain = "TRB") + } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index cf0742f8..7c99d4b4 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -402,6 +402,7 @@ percentGenes(combined, We can also use the output `percentGenes()` for dimensional reduction to summarise the gene usage by sample. This can be done with a simple principal component analysis (below) or even more complex reductions. ```{r} + df.genes <- percentGenes(combined, chain = "TRB", gene = "Vgene", @@ -411,7 +412,7 @@ df.genes <- percentGenes(combined, pc <- prcomp(df.genes) #Getting data frame to plot from -df <- cbind(pc$x[,1:2], rownames(df.genes)) %>% as.data.frame() +df <- as.data.frame(cbind(pc$x[,1:2], rownames(df.genes))) df$PC1 <- as.numeric(df$PC1) df$PC2 <- as.numeric(df$PC2) @@ -439,7 +440,7 @@ df.genes <- percentVJ(combined, pc <- prcomp(df.genes) #Getting data frame to plot from -df <- cbind(pc$x[,1:2], rownames(df.genes)) %>% as.data.frame() +df <- as.data.frame(cbind(pc$x[,1:2], rownames(df.genes))) df$PC1 <- as.numeric(df$PC1) df$PC2 <- as.numeric(df$PC2) From 5753a369cfe29b4aca49f4041f550007741d91e0 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 23 Sep 2023 09:46:31 -0500 Subject: [PATCH 054/142] Update internal linkage of functions --- NAMESPACE | 2 +- R/abundanceContig.R | 3 ++- R/alluvialClonotypes.R | 7 ++---- R/checkContig.R | 3 ++- R/clonalBias.R | 4 +-- R/clonalCompare.R | 4 +-- R/clonalDiversity.R | 3 ++- R/clonalHomeostasis.R | 3 ++- R/clonalNetwork.R | 2 +- R/clonalOverlap.R | 3 ++- R/clonalOverlay.R | 2 +- R/clonalProportion.R | 3 ++- R/clonalQuant.R | 3 ++- R/clonalScatter.R | 3 ++- R/clonalSizeDistribution.R | 4 +-- R/clusterTCR.R | 2 +- R/exportClones.R | 3 ++- R/expression2List.R | 46 ----------------------------------- R/getCircilize.R | 2 +- R/lengthContig.R | 3 ++- R/occupiedscRepertoire.R | 2 +- R/percentAA.R | 4 +-- R/percentGenes.R | 4 +-- R/percentVJ.R | 4 +-- R/vizGenes.R | 4 +-- man/abundanceContig.Rd | 3 ++- man/alluvialClonotypes.Rd | 7 ++---- man/checkContig.Rd | 3 ++- man/clonalCompare.Rd | 4 +-- man/clonalDiversity.Rd | 3 ++- man/clonalHomeostasis.Rd | 3 ++- man/clonalNetwork.Rd | 2 +- man/clonalOverlap.Rd | 3 ++- man/clonalOverlay.Rd | 2 +- man/clonalProportion.Rd | 3 ++- man/clonalQuant.Rd | 3 ++- man/clonalScatter.Rd | 3 ++- man/clonalSizeDistribution.Rd | 4 +-- man/clonotypeBias.Rd | 4 +-- man/clusterTCR.Rd | 2 +- man/exportClones.Rd | 7 +++++- man/expression2List.Rd | 38 ----------------------------- man/getCirclize.Rd | 2 +- man/lengthContig.Rd | 3 ++- man/occupiedscRepertoire.Rd | 2 +- man/percentAA.Rd | 4 +-- man/percentGenes.Rd | 4 +-- man/percentVJ.Rd | 4 +-- man/vizGenes.Rd | 4 +-- 49 files changed, 87 insertions(+), 153 deletions(-) delete mode 100644 R/expression2List.R delete mode 100644 man/expression2List.Rd diff --git a/NAMESPACE b/NAMESPACE index d45619f0..74c30e5d 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -22,7 +22,6 @@ export(combineExpression) export(combineTCR) export(createHTOContigList) export(exportClones) -export(expression2List) export(getCirclize) export(highlightClonotypes) export(lengthContig) @@ -102,4 +101,5 @@ importFrom(tidygraph,activate) importFrom(tidygraph,as_tbl_graph) importFrom(truncdist,qtrunc) importFrom(truncdist,rtrunc) +importFrom(utils,write.csv) useDynLib(scRepertoire, .registration = TRUE) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index fea29775..9780bdb4 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -14,7 +14,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' abundanceContig(combined, cloneCall = "gene", scale = FALSE) #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index 58d794a2..f54bf453 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -54,9 +54,7 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' y.axes = c("Patient", "ident"), #' color = "ident") #' -#' @param sc The seurat or SCE object to visualize after combineExpression(). -#' For SCE objects, the cluster variable must be in the meta data under -#' "cluster". +#' @param sc The single-cell object to visualize after \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt) or CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -74,8 +72,7 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' @importFrom dplyr %>% mutate #' #' @export -#' @return Alluvial ggplot comparing clonotype distribution across -#' selected parameters. +#' @return Alluvial ggplot comparing clonotype distribution. alluvialClonotypes <- function(sc, cloneCall = "strict", chain = "both", diff --git a/R/checkContig.R b/R/checkContig.R index fdef1a07..9877457c 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -13,7 +13,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' checkContig(combined, chain = "TRA", examine = "chain", group.by = "sample") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param examine indicate either "chain" for sequence-based NA or diff --git a/R/clonalBias.R b/R/clonalBias.R index f1c3777e..f53c8ed4 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -26,8 +26,8 @@ #' n.boots = 20, min.expand = 2) #' } #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), -#' or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clonalCompare.R b/R/clonalCompare.R index 3aa7e278..4e9cf43f 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -16,8 +16,8 @@ #' samples = c("P17B", "P17L"), #' cloneCall="aa") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), -#' or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 47b81ce8..3597cb28 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -14,7 +14,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalDiversity(combined, cloneCall = "gene") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 1ff4fb9a..19dd3a18 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -14,7 +14,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalHomeostasis(combined, cloneCall = "gene") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneTypes The cutpoints of the proportions. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index fd30ddb3..a309d61a 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -24,7 +24,7 @@ #' identity = "cluster") #' } #' -#' @param sc The Seurat or SingleCellExperiment (SCE) after combineExpression(). +#' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param reduction The name of the dimensional reduction of the single-cell object #' @param identity A variable in the meta data to use for the nodes. #' @param filter.clones Use to select the top n clones (filter.clones = 2000) or diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index be1fbd95..16554991 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -17,7 +17,8 @@ #' cloneCall = "gene", #' method = "jaccard") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index 474bd90e..c227949b 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -23,7 +23,7 @@ #' freq.cutpoint = 0.3, #' bins = 5) #' -#' @param sc The seurat or SCE object to visualize after combineExpression(). +#' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param reduction The dimensional reduction to visualize #' @param freq.cutpoint The overlay cutpoint to include, this corresponds to the #' Frequency variable in the single-cell objecter diff --git a/R/clonalProportion.R b/R/clonalProportion.R index b7f9b065..13ab553c 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -14,7 +14,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalProportion(combined, cloneCall = "gene") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param split The cutpoints for the specific clonotypes. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/R/clonalQuant.R b/R/clonalQuant.R index d3d70c1c..7d0e0b84 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -14,7 +14,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalQuant(combined, cloneCall="strict", scale = TRUE) #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 05208d24..4327ea8a 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -15,7 +15,8 @@ #' y.axis = "P17L", #' graph = "proportion") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index f2a8bc95..d7eaaf37 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -14,8 +14,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalSizeDistribution(combined, cloneCall = "strict", method="ward.D2") #' -#' @param df The product of combineTCR(), combineBCR(), -#' expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/clusterTCR.R b/R/clusterTCR.R index b339c351..6d0aa38d 100644 --- a/R/clusterTCR.R +++ b/R/clusterTCR.R @@ -17,7 +17,7 @@ #' #' sub_combined <- clusterTCR(combined[[2]], chain = "TRA", sequence = "aa") #' -#' @param df The product of combineTCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}} or \code{\link{combineExpression}}. #' @param chain The TCR to cluster - TRA, TRB, TRG, TRD #' @param sequence Clustering based on either "aa" or "nt" #' @param threshold The normalized edit distance to consider. The higher the number the more diff --git a/R/exportClones.R b/R/exportClones.R index 08445eea..b1990a1d 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -12,7 +12,8 @@ #' exportClones(combined) #' } #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. diff --git a/R/expression2List.R b/R/expression2List.R deleted file mode 100644 index e96c5bcc..00000000 --- a/R/expression2List.R +++ /dev/null @@ -1,46 +0,0 @@ -#' Allows users to take the meta data in Seurat/SCE and place it into a list -#' that will work with all the functions -#' -#' Allows users to perform more fundamental measures of clonotype analysis -#' using the meta data from the Seurat or SCE object. For Seurat objects the -#' active identity is automatically added as "cluster". Remaining grouping -#' parameters or SCE or Seurat objects must appear in the meta data. -#' -#' @examples -#' #Getting the combined contigs -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' -#' #Getting a sample of a Seurat object -#' scRep_example <- get(data("scRep_example")) -#' scRep_example <- combineExpression(combined, scRep_example) -#' -#' #Using expression2List -#' newList <- expression2List(scRep_example, split.by = "seurat_clusters") -#' -#' @param sc object after combineExpression(). -#' @param split.by The column header to group the new list. NULL will return clusters. -#' @importFrom stringr str_sort -#' @export -#' @return list derived from the meta data of single-cell object with -#' elements divided by the group parameter -expression2List <- function(sc, split.by) { - if (!inherits(x=sc, what ="Seurat") & - !inherits(x=sc, what ="SummarizedExperiment")) { - stop("Use a Seurat or SCE object to convert into a list") - } - meta <- grabMeta(sc) - if(is.null(split.by)){ - split.by <- "cluster" - } - unique <- str_sort(as.character(unique(meta[,split.by])), numeric = TRUE) - df <- NULL - for (i in seq_along(unique)) { - subset <- subset(meta, meta[,split.by] == unique[i]) - subset <- subset(subset, !is.na(cloneType)) - df[[i]] <- subset - } - names(df) <- unique - return(df) -} \ No newline at end of file diff --git a/R/getCircilize.R b/R/getCircilize.R index a030740e..8dc0a400 100644 --- a/R/getCircilize.R +++ b/R/getCircilize.R @@ -20,7 +20,7 @@ #' circles <- getCirclize(scRep_example, group.by = "seurat_clusters") #' #' -#' @param sc object after combineExpression(). +#' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). diff --git a/R/lengthContig.R b/R/lengthContig.R index 5ba60afc..47d58f4b 100644 --- a/R/lengthContig.R +++ b/R/lengthContig.R @@ -17,7 +17,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' lengthContig(combined, cloneCall="aa", chain = "both") #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), #' CDR3 amino acid (aa). #' @param group.by The group header for which you would like to analyze diff --git a/R/occupiedscRepertoire.R b/R/occupiedscRepertoire.R index 93190e5a..54fa2350 100644 --- a/R/occupiedscRepertoire.R +++ b/R/occupiedscRepertoire.R @@ -24,7 +24,7 @@ #' occupiedscRepertoire(sce, x.axis = "ident") #' table <- occupiedscRepertoire(sce, x.axis = "ident", exportTable = TRUE) #' -#' @param sc The Seurat or SCE object to visualize after combineExpression(). +#' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param x.axis The variable in the meta data to graph along the x.axis #' @param label Include the number of clonotype in each category by x.axis variable #' @param facet.by The column header used for faceting the graph diff --git a/R/percentAA.R b/R/percentAA.R index 61f66187..84066f95 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -10,8 +10,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' percentAA(combined, chain = "TRB", aa.length = 20) -#' @param df The product of combineTCR(), combineBCR(), -#' expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header diff --git a/R/percentGenes.R b/R/percentGenes.R index 77084d20..58824602 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -10,8 +10,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' percentGenes(combined, chain = "TRB", gene = "Vgene") #' -#' @param df The product of combineTCR(), combineBCR(), -#' expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" #' @param gene "V", "D" or "J" #' @param group.by The column header used for grouping. diff --git a/R/percentVJ.R b/R/percentVJ.R index 298defef..a51b5a76 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -10,8 +10,8 @@ #' "P19B","P19L", "P20B", "P20L")) #' percentVJ(combined, chain = "TRB") #' -#' @param df The product of combineTCR(), combineBCR(), -#' expression2List(), or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header diff --git a/R/vizGenes.R b/R/vizGenes.R index f33341f5..ca51a33a 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -14,8 +14,8 @@ #' #' vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) #' -#' @param df The product of combineTCR(), combineBCR(), expression2List(), -#' or combineExpression(). +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. #' @param gene Which part of the immune receptor to visualize - V, D, J, C #' @param chain indicate the specific chain should be used - #' e.g. "TRA", "TRG", "IGH", "IGL" (no both option here) diff --git a/man/abundanceContig.Rd b/man/abundanceContig.Rd index 66a6146a..c678963f 100644 --- a/man/abundanceContig.Rd +++ b/man/abundanceContig.Rd @@ -17,7 +17,8 @@ abundanceContig( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/alluvialClonotypes.Rd b/man/alluvialClonotypes.Rd index 44aa95a1..38cdddd1 100644 --- a/man/alluvialClonotypes.Rd +++ b/man/alluvialClonotypes.Rd @@ -16,9 +16,7 @@ alluvialClonotypes( ) } \arguments{ -\item{sc}{The seurat or SCE object to visualize after combineExpression(). -For SCE objects, the cluster variable must be in the meta data under -"cluster".} +\item{sc}{The single-cell object to visualize after \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt) or CDR3 amino acid (aa), or @@ -39,8 +37,7 @@ visualizations.} \item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ -Alluvial ggplot comparing clonotype distribution across -selected parameters. +Alluvial ggplot comparing clonotype distribution. } \description{ View the proportional contribution of clonotypes by seurat or SCE object diff --git a/man/checkContig.Rd b/man/checkContig.Rd index 0e2454d3..b9647e77 100644 --- a/man/checkContig.Rd +++ b/man/checkContig.Rd @@ -15,7 +15,8 @@ checkContig( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index d9038e51..843d20b1 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -20,8 +20,8 @@ clonalCompare( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), -or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index 5d9c6441..0957b930 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -20,7 +20,8 @@ clonalDiversity( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index b10e81f4..a310da84 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -17,7 +17,8 @@ clonalHomeostasis( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneTypes}{The cutpoints of the proportions.} diff --git a/man/clonalNetwork.Rd b/man/clonalNetwork.Rd index 646c781c..cd54978c 100644 --- a/man/clonalNetwork.Rd +++ b/man/clonalNetwork.Rd @@ -20,7 +20,7 @@ clonalNetwork( ) } \arguments{ -\item{sc}{The Seurat or SingleCellExperiment (SCE) after combineExpression().} +\item{sc}{The single-cell object after \code{\link{combineExpression}}.} \item{reduction}{The name of the dimensional reduction of the single-cell object} diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index ba640911..23b537e7 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -15,7 +15,8 @@ clonalOverlap( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clonalOverlay.Rd b/man/clonalOverlay.Rd index 7cb0509d..b6c78fce 100644 --- a/man/clonalOverlay.Rd +++ b/man/clonalOverlay.Rd @@ -13,7 +13,7 @@ clonalOverlay( ) } \arguments{ -\item{sc}{The seurat or SCE object to visualize after combineExpression().} +\item{sc}{The single-cell object after \code{\link{combineExpression}}.} \item{reduction}{The dimensional reduction to visualize} diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index aec03963..d4c2e18b 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -16,7 +16,8 @@ clonalProportion( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{split}{The cutpoints for the specific clonotypes.} diff --git a/man/clonalQuant.Rd b/man/clonalQuant.Rd index 52fbb35a..14c2af32 100644 --- a/man/clonalQuant.Rd +++ b/man/clonalQuant.Rd @@ -17,7 +17,8 @@ clonalQuant( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clonalScatter.Rd b/man/clonalScatter.Rd index c8f6cd17..1a7f4e0a 100644 --- a/man/clonalScatter.Rd +++ b/man/clonalScatter.Rd @@ -19,7 +19,8 @@ clonalScatter( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index 3f97f3c0..1bb3320c 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -18,8 +18,8 @@ clonalSizeDistribution( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), -expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clonotypeBias.Rd b/man/clonotypeBias.Rd index d4200b34..51c0d125 100644 --- a/man/clonotypeBias.Rd +++ b/man/clonotypeBias.Rd @@ -15,8 +15,8 @@ clonotypeBias( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), -or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/clusterTCR.Rd b/man/clusterTCR.Rd index 79248016..1b05c4f9 100644 --- a/man/clusterTCR.Rd +++ b/man/clusterTCR.Rd @@ -13,7 +13,7 @@ clusterTCR( ) } \arguments{ -\item{df}{The product of combineTCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineExpression}}.} \item{chain}{The TCR to cluster - TRA, TRB, TRG, TRD} diff --git a/man/exportClones.Rd b/man/exportClones.Rd index d0d54cdc..8a64deb1 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -13,12 +13,17 @@ exportClones( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{group.by}{The column header used for grouping.} \item{split.by}{If using a single-cell object, the column header to group the new list. NULL will return clusters.} + +\item{dir}{directory location to save the csv} + +\item{file.name}{the cav file name} } \value{ ggplot of percentage of V and J gene pairings as a heatmap diff --git a/man/expression2List.Rd b/man/expression2List.Rd deleted file mode 100644 index 89da8356..00000000 --- a/man/expression2List.Rd +++ /dev/null @@ -1,38 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/expression2List.R -\name{expression2List} -\alias{expression2List} -\title{Allows users to take the meta data in Seurat/SCE and place it into a list -that will work with all the functions} -\usage{ -expression2List(sc, split.by) -} -\arguments{ -\item{sc}{object after combineExpression().} - -\item{split.by}{The column header to group the new list. NULL will return clusters.} -} -\value{ -list derived from the meta data of single-cell object with -elements divided by the group parameter -} -\description{ -Allows users to perform more fundamental measures of clonotype analysis -using the meta data from the Seurat or SCE object. For Seurat objects the -active identity is automatically added as "cluster". Remaining grouping -parameters or SCE or Seurat objects must appear in the meta data. -} -\examples{ -#Getting the combined contigs -combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) - -#Getting a sample of a Seurat object -scRep_example <- get(data("scRep_example")) -scRep_example <- combineExpression(combined, scRep_example) - -#Using expression2List -newList <- expression2List(scRep_example, split.by = "seurat_clusters") - -} diff --git a/man/getCirclize.Rd b/man/getCirclize.Rd index cd8e6460..afe955cc 100644 --- a/man/getCirclize.Rd +++ b/man/getCirclize.Rd @@ -8,7 +8,7 @@ clonotypes as a chord diagram.} getCirclize(sc, cloneCall = "strict", group.by = NULL, proportion = FALSE) } \arguments{ -\item{sc}{object after combineExpression().} +\item{sc}{The single-cell object after \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or diff --git a/man/lengthContig.Rd b/man/lengthContig.Rd index a77963b3..60e8f4bd 100644 --- a/man/lengthContig.Rd +++ b/man/lengthContig.Rd @@ -17,7 +17,8 @@ lengthContig( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - CDR3 nucleotide (nt), CDR3 amino acid (aa).} diff --git a/man/occupiedscRepertoire.Rd b/man/occupiedscRepertoire.Rd index dd1b3e6b..f77c8501 100644 --- a/man/occupiedscRepertoire.Rd +++ b/man/occupiedscRepertoire.Rd @@ -16,7 +16,7 @@ occupiedscRepertoire( ) } \arguments{ -\item{sc}{The Seurat or SCE object to visualize after combineExpression().} +\item{sc}{The single-cell object after \code{\link{combineExpression}}.} \item{x.axis}{The variable in the meta data to graph along the x.axis} diff --git a/man/percentAA.Rd b/man/percentAA.Rd index 91ddf10d..75146fbf 100644 --- a/man/percentAA.Rd +++ b/man/percentAA.Rd @@ -15,8 +15,8 @@ percentAA( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), -expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd index 44488d6d..ad321210 100644 --- a/man/percentGenes.Rd +++ b/man/percentGenes.Rd @@ -15,8 +15,8 @@ percentGenes( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), -expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd index 25c66aa2..2ca9a38a 100644 --- a/man/percentVJ.Rd +++ b/man/percentVJ.Rd @@ -14,8 +14,8 @@ percentVJ( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), -expression2List(), or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index 220a9e6a..a7352a14 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -19,8 +19,8 @@ vizGenes( ) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), expression2List(), -or combineExpression().} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} \item{gene}{Which part of the immune receptor to visualize - V, D, J, C} From 59a7a328a4df9628ae5bc9ad5516f066bd2f1592 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 23 Sep 2023 10:00:22 -0500 Subject: [PATCH 055/142] remove expression2List() --- NEWS | 1 + R/addVariable.R | 14 +++++++------- R/checkContig.R | 9 ++++----- R/clonalOverlap.R | 13 ++++--------- R/clonalQuant.R | 7 ++----- R/lengthContig.R | 6 ++---- R/subsetContig.R | 19 +++++++++---------- R/utils.R | 22 +++++++++++++++++++++- vignettes/vignette.Rmd | 34 ---------------------------------- 9 files changed, 50 insertions(+), 75 deletions(-) diff --git a/NEWS b/NEWS index 92a6e2cf..22c9aedb 100644 --- a/NEWS +++ b/NEWS @@ -23,6 +23,7 @@ UNDERLYING CHANGES DEPRECATED AND DEFUNCT * Deprecate stripBarcodes() +* Deprecate expression2List() (now only an internal function). CHANGES IN VERSION 1.11.0 diff --git a/R/addVariable.R b/R/addVariable.R index 456d5bc2..f5bdbb2f 100644 --- a/R/addVariable.R +++ b/R/addVariable.R @@ -1,10 +1,10 @@ -#' Adding variables after the combination of contigs. +#' Adding variables after the combination of clonotypes. #' -#' This function adds variables to the product of combineTCR() combineBCR() or -#' expression2List() to be used in later visualizations. For each element, -#' the function will add a column (labled by name) with the variable. -#' The length of the variable paramater needs to match the length of -#' the combined object. +#' This function adds variables to the product of \code{\link{combineTCR}}, +#' or \code{\link{combineBCR}} to be used in later visualizations. +#' For each element, the function will add a column (labeled by name) +#' with the variable. The length of the variable paramater needs to match +#' the length of the combined object. #' #' @examples #' combined <- combineTCR(contig_list, @@ -14,7 +14,7 @@ #' name = "Type", #' variables = rep(c("B", "L"), 4)) #' -#' @param df The product of combineTCR() combineBCR() or expression2List(). +#' @param df The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. #' @param name The column header to add. #' @param variables The exact values to add to each element of the list. #' @export diff --git a/R/checkContig.R b/R/checkContig.R index 9877457c..fccb956c 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -1,10 +1,9 @@ #' Assess the number of NAs in clonotypes #' -#' This function takes the output from combineTCR(), combineBCR(), -#' combineExpression() or expression2List() and quantifies the -#' percent of NA values in either the sequence information of -#' the VDJC gene usage. If a data frame output for the raw numbers -#' and percentage of NAs are preferred, set exportTable = TRUE. +#' This function quantifies the percent of NA values in either the +#' sequence information of the VDJC gene usage. If a data frame +#' output for the raw numbers and percentage of NAs are preferred, +#' set exportTable = TRUE. #' #' @examples #' #Making combined contig data diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 16554991..4e4cf3d4 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -1,11 +1,7 @@ #' Examining the clonal overlap between groups or samples #' -#' This functions allows for the calculation and visualizations of the -#' overlap coefficient, morisita, or jaccard index for clonotypes -#' using the product of combineTCR(), combineBCR() or expression2list(). -#' The overlap coefficient is calculated using the intersection of clonotypes -#' divided by the length of the smallest component. -#' If a matrix output for the data is preferred, set exportTable = TRUE. +#' This functions allows for the calculation and visualizations of +#' various overlap metrics for clonotypes. #' #' @examples #' #Making combined contig data @@ -24,9 +20,8 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param method The method to calculate the overlap, -#' "overlap" -# "morisita", "jaccard" indices, "cosine" similarity or "raw" +#' @param method The method to calculate the "overlap", +# "morisita", "jaccard" indices, "cosine" similarity or "raw" #' for the base numbers. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 7d0e0b84..1795ce2e 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -1,11 +1,8 @@ #' Quantify the unique clonotypes in the filtered contigs. #' -#' This function takes the output from combineTCR(), combineBCR(), or -#' expression2List() and quantifies unique clonotypes. The unique clonotypes +#' This function quantifies unique clonotypes. The unique clonotypes #' can be either reported as a raw output or scaled to the total number of -#' clonotypes recovered using the scale parameter. Multiple sequencing -#' runs can be group together using the group parameter. If a matrix output -#' for the data is preferred, set exportTable = TRUE. +#' clonotypes recovered using the scale parameter. #' #' @examples #' #Making combined contig data diff --git a/R/lengthContig.R b/R/lengthContig.R index 47d58f4b..33940813 100644 --- a/R/lengthContig.R +++ b/R/lengthContig.R @@ -1,14 +1,12 @@ #' Demonstrate the distribution of lengths filtered contigs. #' -#' This function takes the output of combineTCR(), combineBCR(), or -#' expression2List() and displays either the nucleotide (nt) or amino +#' This function displays either the nucleotide (nt) or amino #' acid (aa) sequence length. The sequence length visualized can be #' selected using the chains parameter, either the combined clonotype #' (both chains) or across all single chains. Visualization can either #' be a histogram or if scale = TRUE, the output will be a density plot. #' Multiple sequencing runs can be group together using the -#' group parameter. If a matrix output for the data is preferred, set -#' exportTable = TRUE. +#' group parameter. #' #' @examples #' #Making combined contig data diff --git a/R/subsetContig.R b/R/subsetContig.R index 0f43d987..e78f7f90 100644 --- a/R/subsetContig.R +++ b/R/subsetContig.R @@ -1,19 +1,18 @@ -#' Subset the product of combineTCR() combineBCR() or expression2List() +#' Subset the product of combineTCR() combineBCR() #' -#' This function allows for the subsetting of the product of combineTCR() -#' combineBCR() or expression2List() by the name of the individual list -#' element. In general the names of are samples + _ + ID, allowing -#' for users to subset the product of combineTCR(), combineBCR(), -#' or expression2List() across a string or individual name. +#' This function allows for the subsetting of the product of +#' \code{\link{combineTCR}} or \code{\link{combineBCR}} +#' by the name of the individual list element. #' #' @examples #' x <- contig_list -#' combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) -#' subset <- subsetContig(combined, name = "sample", variables = c("PX")) +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' subset <- subsetContig(combined, name = "sample", variables = c("P17B")) #' -#' @param df The product of combineTCR(), combineBCR(), or expression2List(). +#' @param df The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. #' @param name The column header you'd like to use to subset. #' @param variables The values to subset by, must be in the names(df). diff --git a/R/utils.R b/R/utils.R index fcffa1fd..4a8996b1 100644 --- a/R/utils.R +++ b/R/utils.R @@ -98,7 +98,7 @@ list.input.return <- function(df, split.by) { if(is.null(split.by)){ split.by <- "cluster" } - df <- expression2List(df, split.by) + df <- .expression2List(df, split.by) } df } @@ -461,3 +461,23 @@ is_df_or_list_of_df <- function(x) { return(FALSE) } } + +.expression2List <- function(sc, split.by) { + if (!inherits(x=sc, what ="Seurat") & + !inherits(x=sc, what ="SummarizedExperiment")) { + stop("Use a Seurat or SCE object to convert into a list") + } + meta <- grabMeta(sc) + if(is.null(split.by)){ + split.by <- "cluster" + } + unique <- str_sort(as.character(unique(meta[,split.by])), numeric = TRUE) + df <- NULL + for (i in seq_along(unique)) { + subset <- subset(meta, meta[,split.by] == unique[i]) + subset <- subset(subset, !is.na(cloneType)) + df[[i]] <- subset + } + names(df) <- unique + return(df) +} diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 7c99d4b4..00b2c05c 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -846,40 +846,6 @@ clonotypeBias(scRep_example, min.expand =1) ``` - -# Working with clonotypes after clustering - -For users that would like the greater ability to use the meta data in the Seurat objects to perform the analysis that scRepertoire provides, there is also the option of using the *expression2List()* function that will take the meta data and output the data as a list by cluster. - -```{r} -combined2 <- expression2List(scRep_example, split.by = "seurat_clusters") -combined3 <- expression2List(sce, split.by = "seurat_clusters") -``` - -## 1. clonalDiversity -```{r} -clonalDiversity(combined2, cloneCall = "nt") -clonalDiversity(combined3, cloneCall = "nt") -``` - -## 2. clonalHomeostasis -```{r} -clonalHomeostasis(combined2, cloneCall = "nt") -clonalHomeostasis(combined3, cloneCall = "nt") -``` - -## 3. clonalProportion -```{r} -clonalProportion(combined2, cloneCall = "nt") -clonalProportion(combined3, cloneCall = "nt") -``` - -## 4. clonalOverlap -```{r} -clonalOverlap(combined2, cloneCall="aa", method="cosine") -clonalOverlap(combined3, cloneCall="aa", method="overlap") -``` - *** # Conclusion From 9b005c3453911bc9a9c05b6741c7b20d8e3529d5 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 24 Sep 2023 06:09:01 -0500 Subject: [PATCH 056/142] finalize exportClones --- R/exportClones.R | 8 ++++++-- vignettes/vignette.Rmd | 22 ++++++++++++++++++++++ 2 files changed, 28 insertions(+), 2 deletions(-) diff --git a/R/exportClones.R b/R/exportClones.R index b1990a1d..0e2a00ed 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -17,8 +17,9 @@ #' @param group.by The column header used for grouping. #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. +#' @param write.file TRUE, save the file, FALSE, return a data.frame #' @param dir directory location to save the csv -#' @param file.name the cav file name +#' @param file.name the csv file name #' @importFrom dplyr bind_rows #' @importFrom stringr str_split #' @importFrom utils write.csv @@ -28,6 +29,7 @@ exportClones <- function(df, group.by = NULL, split.by = NULL, + write.file = TRUE, dir = NULL, file.name = "clones.csv") { @@ -50,7 +52,9 @@ exportClones <- function(df, chain2_genes = genes[,2], group = df[,"group"]) mat[mat == "NA"] <- NA - + if(!write.file) { + return(mat) + } if(is.null(dir)) { dir <- "./" diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 00b2c05c..f8a5ca17 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -147,6 +147,28 @@ subset <- subsetContig(combined, variables = c("P18L", "P18B")) ``` + +## exportClones + +After assigning the clonotypes by barcode, we can export the paired clonotypes using `exportClones()` to save for later use or to use in other pipelines. + +**write.file**. +* TRUE, save the file. +* FALSE, return a data.frame. + +**dir** +directory location to save the csv + +**file.name** +the cav file name + +```{r, eval = FALSE} +exportClones(combined, + write.file = TRUE, + dir = "~/Documents/MyExperiment/Sample1/" + file.name = "clones.csv" +``` + *** # Visualizing Contigs From 5d0fbd4aaec1d9ec9fb38a3bb13ceadce26a1bda Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 24 Sep 2023 12:57:46 -0500 Subject: [PATCH 057/142] clean loadContigs --- NAMESPACE | 2 +- NEWS | 1 + R/clonalQuant.R | 2 +- ...ighlightClonotypes.R => highlightClones.R} | 10 ++--- R/loadContigs.R | 42 ++++++++----------- man/addVariable.Rd | 14 +++---- man/checkContig.Rd | 9 ++-- man/clonalOverlap.Rd | 11 ++--- man/clonalQuant.Rd | 9 ++-- man/exportClones.Rd | 5 ++- ...hlightClonotypes.Rd => highlightClones.Rd} | 16 +++---- man/lengthContig.Rd | 6 +-- man/subsetContig.Rd | 19 ++++----- 13 files changed, 66 insertions(+), 80 deletions(-) rename R/{highlightClonotypes.R => highlightClones.R} (87%) rename man/{highlightClonotypes.Rd => highlightClones.Rd} (77%) diff --git a/NAMESPACE b/NAMESPACE index 74c30e5d..a84b1ca6 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -23,7 +23,7 @@ export(combineTCR) export(createHTOContigList) export(exportClones) export(getCirclize) -export(highlightClonotypes) +export(highlightClones) export(lengthContig) export(loadContigs) export(occupiedscRepertoire) diff --git a/NEWS b/NEWS index 22c9aedb..290ae125 100644 --- a/NEWS +++ b/NEWS @@ -11,6 +11,7 @@ NEW FEATURES * Changed clonotypeSizeDistribution to clonalSizeDistribution * Changed scatterClonotypes to clonalScatter * Changed quantContig to clonalQuant +* Changed highlightClonotypes to highlightClones UNDERLYING CHANGES diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 1795ce2e..06c083dd 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -1,4 +1,4 @@ -#' Quantify the unique clonotypes in the filtered contigs. +#' Quantify the unique clonotypes #' #' This function quantifies unique clonotypes. The unique clonotypes #' can be either reported as a raw output or scaled to the total number of diff --git a/R/highlightClonotypes.R b/R/highlightClones.R similarity index 87% rename from R/highlightClonotypes.R rename to R/highlightClones.R index d6b90ffa..7500a52b 100644 --- a/R/highlightClonotypes.R +++ b/R/highlightClones.R @@ -1,10 +1,10 @@ #' Highlighting specific clonotypes in Seurat #' #' Use a specific clonotype sequence to highlight on top of the dimensional -#' reduction in seurat object. +#' reduction in Seurat object. #' #' @examples -#' #' #Getting the combined contigs +#' #Getting the combined contigs #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) @@ -17,9 +17,9 @@ #' #' #Using highlightClonotype() #' scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", -#' sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF")) +#' sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) #' -#' @param sc The Seurat object to attach +#' @param sc The Seurat object to attach after \code{\link{combineExpression}} #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -27,7 +27,7 @@ #' #' @export #' @return Seurat object with new meta data column for indicated clones -highlightClonotypes <- function(sc, +highlightClones <- function(sc, cloneCall = c("gene", "nt", "aa", "strict"), sequence = NULL){ if (!inherits(x=sc, what ="Seurat")) { diff --git a/R/loadContigs.R b/R/loadContigs.R index e5db0cc7..3f19cf4b 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -50,28 +50,22 @@ loadContigs <- function(dir, } else if (inherits(x=dir, what ="list") | inherits(x=dir, what ="data.frame")) { df <- checkList(dir) } - - if (format %in% c("10X", "WAT3R")) { - if (format =="WAT3R") { - df <- parseWAT3R(df) - } else { - df <- parse10x(df) - } - } else { - if (format =="TRUST4") { - df <- parseTRUST4(df) - } else if (format == "AIRR") { - df <- parseAIRR(df) - } else { - df <- parseBD(df) - } - } - return(df) + + loadFunc <- switch(method, + "10X" = .parse10x, + "AIRR" = .parseAIRR, + "TRUST4" = .parseTRUST4, + "BD" = .parseBD, + "WAT3R" = .parseWAT3R, + stop("Invalid format provided")) + + df <- loadFunc(df) + return(df) } #Formats TRUST4 data #' @importFrom stringr str_split -parseTRUST4 <- function(df) { +.parseTRUST4 <- function(df) { for (i in seq_along(df)) { colnames(df[[i]])[1] <- "barcode" df[[i]][df[[i]] == "*"] <- NA @@ -97,13 +91,13 @@ parseTRUST4 <- function(df) { data2[data2 == ""] <- NA df[[i]] <- data2 } - df <- chain.parser(df) + df <- .chain.parser(df) return(df) } #Formats wat3r data #' @author Kyle Romine, Nick Borcherding -parseWAT3R <- function(df) { +.parseWAT3R <- function(df) { for (i in seq_along(df)) { colnames(df[[i]])[1] <- "barcode" df[[i]][df[[i]] == ""] <- NA @@ -131,7 +125,7 @@ parseWAT3R <- function(df) { } #Formats AIRR data -parseAIRR <- function(df) { +.parseAIRR <- function(df) { for (i in seq_along(df)) { df[[i]] <- df[[i]][,c("cell_id", "locus", "consensus_count", "v_call", "d_call", "j_call", "c_call", "junction", "junction_aa")] colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") @@ -141,7 +135,7 @@ parseAIRR <- function(df) { } #Loads 10x data -parse10x <- function(df) { +.parse10x <- function(df) { for (i in seq_along(df)) { df[[i]] <- subset(df[[i]], chain != "Multi") df[[i]] <- subset(df[[i]], productive %in% c(TRUE, "TRUE", "True", "true")) @@ -156,7 +150,7 @@ parse10x <- function(df) { return(df) } #Loads BD AIRR -parseBD <- function(df) { +.parseBD <- function(df) { for (i in seq_along(df)) { df[[i]] <- df[[i]][,c(1,2,20,25,30, 35, 48,49,4)] colnames(df[[i]]) <- c("barcode", "chain", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3", "reads") @@ -165,7 +159,7 @@ parseBD <- function(df) { return(df) } #Grabs the chain info from v_gene -chain.parser <- function(df) { +.chain.parser <- function(df) { for (i in seq_along(df)) { df[[i]]$chain <- substr(df[[i]][,"v_gene"],1,3) } diff --git a/man/addVariable.Rd b/man/addVariable.Rd index 111e4289..0119b1b4 100644 --- a/man/addVariable.Rd +++ b/man/addVariable.Rd @@ -2,12 +2,12 @@ % Please edit documentation in R/addVariable.R \name{addVariable} \alias{addVariable} -\title{Adding variables after the combination of contigs.} +\title{Adding variables after the combination of clonotypes.} \usage{ addVariable(df, name = NULL, variables = NULL) } \arguments{ -\item{df}{The product of combineTCR() combineBCR() or expression2List().} +\item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} \item{name}{The column header to add.} @@ -17,11 +17,11 @@ addVariable(df, name = NULL, variables = NULL) list of contigs with a new column (name). } \description{ -This function adds variables to the product of combineTCR() combineBCR() or -expression2List() to be used in later visualizations. For each element, -the function will add a column (labled by name) with the variable. -The length of the variable paramater needs to match the length of -the combined object. +This function adds variables to the product of \code{\link{combineTCR}}, +or \code{\link{combineBCR}} to be used in later visualizations. +For each element, the function will add a column (labeled by name) +with the variable. The length of the variable paramater needs to match +the length of the combined object. } \examples{ combined <- combineTCR(contig_list, diff --git a/man/checkContig.Rd b/man/checkContig.Rd index b9647e77..caaa8ccd 100644 --- a/man/checkContig.Rd +++ b/man/checkContig.Rd @@ -37,11 +37,10 @@ to group the new list. NULL will return clusters.} ggplot of the percentage of NA values } \description{ -This function takes the output from combineTCR(), combineBCR(), -combineExpression() or expression2List() and quantifies the -percent of NA values in either the sequence information of -the VDJC gene usage. If a data frame output for the raw numbers -and percentage of NAs are preferred, set exportTable = TRUE. +This function quantifies the percent of NA values in either the +sequence information of the VDJC gene usage. If a data frame +output for the raw numbers and percentage of NAs are preferred, +set exportTable = TRUE. } \examples{ #Making combined contig data diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 23b537e7..a996fb83 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -22,8 +22,7 @@ clonalOverlap( CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} -\item{method}{The method to calculate the overlap, - "overlap" +\item{method}{The method to calculate the "overlap", for the base numbers.} \item{chain}{indicate if both or a specific chain should be used - @@ -40,12 +39,8 @@ to group the new list. NULL will return clusters.} ggplot of the clonotypic overlap between elements of a list } \description{ -This functions allows for the calculation and visualizations of the -overlap coefficient, morisita, or jaccard index for clonotypes -using the product of combineTCR(), combineBCR() or expression2list(). -The overlap coefficient is calculated using the intersection of clonotypes -divided by the length of the smallest component. -If a matrix output for the data is preferred, set exportTable = TRUE. +This functions allows for the calculation and visualizations of +various overlap metrics for clonotypes. } \examples{ #Making combined contig data diff --git a/man/clonalQuant.Rd b/man/clonalQuant.Rd index 14c2af32..27ff330f 100644 --- a/man/clonalQuant.Rd +++ b/man/clonalQuant.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/clonalQuant.R \name{clonalQuant} \alias{clonalQuant} -\title{Quantify the unique clonotypes in the filtered contigs.} +\title{Quantify the unique clonotypes} \usage{ clonalQuant( df, @@ -44,12 +44,9 @@ to group the new list. NULL will return clusters.} ggplot of the total or relative unique clonotypes } \description{ -This function takes the output from combineTCR(), combineBCR(), or -expression2List() and quantifies unique clonotypes. The unique clonotypes +This function quantifies unique clonotypes. The unique clonotypes can be either reported as a raw output or scaled to the total number of -clonotypes recovered using the scale parameter. Multiple sequencing -runs can be group together using the group parameter. If a matrix output -for the data is preferred, set exportTable = TRUE. +clonotypes recovered using the scale parameter. } \examples{ #Making combined contig data diff --git a/man/exportClones.Rd b/man/exportClones.Rd index 8a64deb1..72ffd34e 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -8,6 +8,7 @@ exportClones( df, group.by = NULL, split.by = NULL, + write.file = TRUE, dir = NULL, file.name = "clones.csv" ) @@ -21,9 +22,11 @@ exportClones( \item{split.by}{If using a single-cell object, the column header to group the new list. NULL will return clusters.} +\item{write.file}{TRUE, save the file, FALSE, return a data.frame} + \item{dir}{directory location to save the csv} -\item{file.name}{the cav file name} +\item{file.name}{the csv file name} } \value{ ggplot of percentage of V and J gene pairings as a heatmap diff --git a/man/highlightClonotypes.Rd b/man/highlightClones.Rd similarity index 77% rename from man/highlightClonotypes.Rd rename to man/highlightClones.Rd index 6336929b..930545e7 100644 --- a/man/highlightClonotypes.Rd +++ b/man/highlightClones.Rd @@ -1,17 +1,17 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/highlightClonotypes.R -\name{highlightClonotypes} -\alias{highlightClonotypes} +% Please edit documentation in R/highlightClones.R +\name{highlightClones} +\alias{highlightClones} \title{Highlighting specific clonotypes in Seurat} \usage{ -highlightClonotypes( +highlightClones( sc, cloneCall = c("gene", "nt", "aa", "strict"), sequence = NULL ) } \arguments{ -\item{sc}{The Seurat object to attach} +\item{sc}{The Seurat object to attach after \code{\link{combineExpression}}} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -24,10 +24,10 @@ Seurat object with new meta data column for indicated clones } \description{ Use a specific clonotype sequence to highlight on top of the dimensional -reduction in seurat object. +reduction in Seurat object. } \examples{ -#' #Getting the combined contigs +#Getting the combined contigs combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) @@ -40,6 +40,6 @@ scRep_example <- combineExpression(combined, scRep_example) #Using highlightClonotype() scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", -sequence = c("CAVNGGSQGNLIF_CSAEREDTDTQYF")) +sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) } diff --git a/man/lengthContig.Rd b/man/lengthContig.Rd index 60e8f4bd..207e33cf 100644 --- a/man/lengthContig.Rd +++ b/man/lengthContig.Rd @@ -46,15 +46,13 @@ ggplot of the discrete or relative length distributions of clonotype sequences } \description{ -This function takes the output of combineTCR(), combineBCR(), or -expression2List() and displays either the nucleotide (nt) or amino +This function displays either the nucleotide (nt) or amino acid (aa) sequence length. The sequence length visualized can be selected using the chains parameter, either the combined clonotype (both chains) or across all single chains. Visualization can either be a histogram or if scale = TRUE, the output will be a density plot. Multiple sequencing runs can be group together using the -group parameter. If a matrix output for the data is preferred, set -exportTable = TRUE. +group parameter. } \examples{ #Making combined contig data diff --git a/man/subsetContig.Rd b/man/subsetContig.Rd index 005bd16c..eb645fa7 100644 --- a/man/subsetContig.Rd +++ b/man/subsetContig.Rd @@ -2,12 +2,12 @@ % Please edit documentation in R/subsetContig.R \name{subsetContig} \alias{subsetContig} -\title{Subset the product of combineTCR() combineBCR() or expression2List()} +\title{Subset the product of combineTCR() combineBCR()} \usage{ subsetContig(df, name, variables = NULL) } \arguments{ -\item{df}{The product of combineTCR(), combineBCR(), or expression2List().} +\item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} \item{name}{The column header you'd like to use to subset.} @@ -17,16 +17,15 @@ subsetContig(df, name, variables = NULL) list of contigs that have been filtered for the name parameter } \description{ -This function allows for the subsetting of the product of combineTCR() -combineBCR() or expression2List() by the name of the individual list -element. In general the names of are samples + _ + ID, allowing -for users to subset the product of combineTCR(), combineBCR(), -or expression2List() across a string or individual name. +This function allows for the subsetting of the product of +\code{\link{combineTCR}} or \code{\link{combineBCR}} +by the name of the individual list element. } \examples{ x <- contig_list -combined <- combineTCR(x, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) -subset <- subsetContig(combined, name = "sample", variables = c("PX")) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +subset <- subsetContig(combined, name = "sample", variables = c("P17B")) } From ad45d963ec8a18a73b77616f1954038263f7b815 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 24 Sep 2023 13:46:14 -0500 Subject: [PATCH 058/142] update global.R In depth check of global variables --- LICENSE.md => LICENSE | 6 +-- NAMESPACE | 3 ++ R/alluvialClonotypes.R | 6 ++- R/checkContig.R | 2 +- R/clonalBias.R | 25 ++++++----- R/clonalCompare.R | 28 ++++++------ R/clonalNetwork.R | 2 +- R/clonalSizeDistribution.R | 3 +- R/global.R | 88 ++++++++++++-------------------------- R/highlightClones.R | 2 +- R/loadContigs.R | 3 +- R/startracDiversity.R | 2 +- R/vizGenes.R | 1 + man/clonalCompare.Rd | 2 +- man/clonotypeBias.Rd | 22 ++++++---- man/highlightClones.Rd | 2 +- vignettes/vignette.Rmd | 4 +- 17 files changed, 94 insertions(+), 107 deletions(-) rename LICENSE.md => LICENSE (93%) diff --git a/LICENSE.md b/LICENSE similarity index 93% rename from LICENSE.md rename to LICENSE index a68bbe78..e597f202 100644 --- a/LICENSE.md +++ b/LICENSE @@ -1,6 +1,6 @@ -The MIT License (MIT) +MIT License -Copyright (c) 2023 Nick Borcherding +Copyright (c) 2023 Nick Borcherding Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal @@ -18,4 +18,4 @@ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE -SOFTWARE. \ No newline at end of file +SOFTWARE. diff --git a/NAMESPACE b/NAMESPACE index a84b1ca6..5181f698 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -83,6 +83,7 @@ importFrom(plyr,llply) importFrom(reshape2,dcast) importFrom(reshape2,melt) importFrom(rlang,"%||%") +importFrom(stats,as.dist) importFrom(stats,hclust) importFrom(stats,na.omit) importFrom(stats,optim) @@ -101,5 +102,7 @@ importFrom(tidygraph,activate) importFrom(tidygraph,as_tbl_graph) importFrom(truncdist,qtrunc) importFrom(truncdist,rtrunc) +importFrom(utils,read.csv) +importFrom(utils,read.delim) importFrom(utils,write.csv) useDynLib(scRepertoire, .registration = TRUE) diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index f54bf453..be04d025 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -81,6 +81,7 @@ alluvialClonotypes <- function(sc, alpha = NULL, facet = NULL, palette = "inferno") { + x <- alluvium <- stratum <- NULL checkSingleObject(sc) cloneCall <- theCall(cloneCall) if (length(y.axes) == 0) { @@ -104,7 +105,8 @@ alluvialClonotypes <- function(sc, meta2 <- unique(na.omit(meta2[!duplicated(as.list(meta2))])) lodes <- makingLodes(meta2, color, alpha, facet, set.axes) - plot <- ggplot(data = lodes, aes(x = x, stratum = stratum, + plot <- ggplot(data = lodes, aes(x = x, + stratum = stratum, alluvium = alluvium, label = stratum)) + geom_stratum(width = 0.2) @@ -137,7 +139,7 @@ alluvialClonotypes <- function(sc, plot <- plot + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) + scale_fill_manual(values = .colorizer(palette, length(levels(lodes[,color])))) + - scale_x_discrete(expand = c(0.025,0.025)) + + scale_x_discrete(expand = c(0.025,0.025)) + theme_classic() + theme(axis.title.x = element_blank(), axis.ticks.x = element_blank(), diff --git a/R/checkContig.R b/R/checkContig.R index fccb956c..ac286728 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -35,7 +35,7 @@ checkContig <- function(df, split.by = NULL, exportTable = FALSE, palette = "inferno") { - + NA.values <- NA x.axis <- c(split.by, group.by) #condensing for ploting. x.axis <- x.axis[1] if(is.null(x.axis)) { diff --git a/R/clonalBias.R b/R/clonalBias.R index f53c8ed4..b90f2d15 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -9,22 +9,26 @@ #' bias of 0 matches the background subtype distribution. #' #' @examples -#' \dontrun{ -#' Getting the combined contigs -#' combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -#' rep(c("P", "T"), 3)) +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #' #' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) +#' scRep_example <- combineExpression(combined, scRep_example) +#' scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #' #' #Using occupiedscRepertoire() -#' clonotypeBias(sce, cloneCall = "CTaa", split.by = "Patient", group.by = "seurat_clusters", -#' n.boots = 20, min.expand = 2) -#' } +#' clonotypeBias(scRep_example, +#' cloneCall = "aa", +#' split.by = "Patient", +#' group.by = "seurat_clusters", +#' n.boots = 20, +#' min.expand = 2) +#' #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. @@ -142,7 +146,8 @@ get_clono_bias <- function(df, freq_diff=double(), bias=double()) - bg <- get_clono_bg(df, split.by=split.by, + bg <- get_clono_bg(df, + split.by=split.by, min.expand = min.expand, group.by = group.by, cloneCall = cloneCall) diff --git a/R/clonalCompare.R b/R/clonalCompare.R index 4e9cf43f..0f3bd290 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -1,7 +1,7 @@ #' Demonstrate the difference in clonal proportion between clonotypes #' #' This function produces an alluvial or area graph of the proportion of -#' the indicated clonotypes for all or selected samples. Clonotypes can be +#' the indicated clonotypes for all or selected samples. clonotypes can be #' selected using the clonotypes parameter with the specific sequence of #' interest or using the number parameter with the top n clonotypes by #' proportion to be visualized. @@ -70,7 +70,7 @@ clonalCompare <- function(df, } tbl <- as.data.frame(table(df[[i]][,cloneCall])) tbl[,2] <- tbl[,2]/sum(tbl[,2]) - colnames(tbl) <- c("Clonotypes", "Proportion") + colnames(tbl) <- c("clonotypes", "Proportion") tbl$Sample <- names(df[i]) Con.df <- rbind.data.frame(Con.df, tbl) } @@ -80,19 +80,19 @@ clonalCompare <- function(df, Con.df <- Con.df[Con.df$Sample %in% samples,] } if (!is.null(clonotypes)) { - Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] + Con.df <- Con.df[Con.df$clonotypes %in% clonotypes,] } else if (!is.null(top.clones)) { top <- Con.df %>% group_by(Con.df[,3]) %>% slice_max(n = top.clones, order_by = Proportion, with_ties = FALSE) - Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] + Con.df <- Con.df[Con.df$clonotypes %in% top$clonotypes,] } if (nrow(Con.df) < length(unique(Con.df$Sample))) { stop("Reasses the filtering strategies here, there is not enough clonotypes to examine.") } #Clonotype relabeling - clones.returned <- as.vector(unique(Con.df[,"Clonotypes"])) + clones.returned <- as.vector(unique(Con.df[,"clonotypes"])) if (relabel.clones) { new.clones <- paste0("Clonotype: ", seq_len(length(clones.returned))) names(new.clones) <- clones.returned @@ -100,17 +100,21 @@ clonalCompare <- function(df, if(!is.null(highlight.clones)) { highlight.clones <- unname(new.clones[which(names(new.clones) %in% highlight.clones)]) } - Con.df[,"Clonotypes"] <- new.clones[as.vector(Con.df[,"Clonotypes"])] - clones.returned <- as.vector(unique(Con.df[,"Clonotypes"])) + Con.df[,"clonotypes"] <- new.clones[as.vector(Con.df[,"clonotypes"])] + clones.returned <- as.vector(unique(Con.df[,"clonotypes"])) } if (exportTable == TRUE) { return(Con.df) } #Plotting Functions - plot <- ggplot(Con.df, aes(x = Sample, fill = Clonotypes, group = Clonotypes, - stratum = Clonotypes, alluvium = Clonotypes, - y = Proportion, label = Clonotypes)) + + plot <- ggplot(Con.df, aes(x = Sample, + fill = clonotypes, + group = clonotypes, + stratum = clonotypes, + alluvium = clonotypes, + y = Proportion, + label = clonotypes)) + theme_classic() + theme(axis.title.x = element_blank(), legend.text=element_text(size=rel(0.5)), @@ -119,7 +123,7 @@ clonalCompare <- function(df, plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") } else if (graph == "area") { plot <- plot + - geom_area(aes(group = Clonotypes), color = "black") + geom_area(aes(group = clonotypes), color = "black") } #Highlighting specific clones @@ -130,7 +134,7 @@ clonalCompare <- function(df, names(clone.colors) <- clones.returned plot <- plot + scale_fill_manual(values = clone.colors) } else { - plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"Clonotypes"])))) + plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"clonotypes"])))) } return(plot) } diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index a309d61a..1079b369 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -66,7 +66,7 @@ clonalNetwork <- function(sc, exportTable = FALSE, palette = "inferno", exportClones = FALSE) { - + to <- from <- weight <- y <- NULL cloneCall <- theCall(cloneCall) meta <- grabMeta(sc) coord <- data.frame(get.coord(sc, reduction), identity = meta[,identity]) diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index d7eaaf37..94a41a84 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -33,6 +33,7 @@ #' @importFrom dplyr bind_rows #' @importFrom ggdendro dendro_data segment label #' @importFrom stats hclust optim pgamma +#' @importFrom stats as.dist #' @export #' @return ggplot dendrogram of the clone size distribution #' @author Hillary Koch @@ -46,7 +47,7 @@ clonalSizeDistribution <- function(df, split.by = NULL, exportTable = FALSE, palette = "inferno") { - xend <- yend <- mpg_div_hp <- NULL + x <- xend <- yend <- mpg_div_hp <- NULL df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) df <- checkBlanks(df, cloneCall) diff --git a/R/global.R b/R/global.R index 2738bbc5..6ff246f2 100644 --- a/R/global.R +++ b/R/global.R @@ -1,78 +1,44 @@ .onLoad <- function (libname, pkgname) { - utils::globalVariables ("na.omit") - utils::globalVariables ("aid") - utils::globalVariables ("majorCluster") - utils::globalVariables ("value") + utils::globalVariables (".") + utils::globalVariables ("AA") utils::globalVariables ("Abundance") - utils::globalVariables ("scaled") - utils::globalVariables ("values") - utils::globalVariables ("df") - utils::globalVariables ("reorder") - utils::globalVariables ("Frequency") - utils::globalVariables ("Var1") - utils::globalVariables ("Var2") - utils::globalVariables ("as.dist") utils::globalVariables ("barcode") - utils::globalVariables ("reads") - utils::globalVariables ("Freq") utils::globalVariables ("cdr3") + utils::globalVariables ("cdr3_nt") utils::globalVariables ("cdr3_nt1") utils::globalVariables ("cdr3_nt2") - utils::globalVariables ("indicator") - utils::globalVariables ("cloneType") - utils::globalVariables ("CTnt") - utils::globalVariables ("Proportion") - utils::globalVariables ("Clonotypes") - utils::globalVariables ("..count..") utils::globalVariables ("CT") + utils::globalVariables ("CTaa") + utils::globalVariables ("CTnt") + utils::globalVariables ("clone") + utils::globalVariables ("CloneType") + utils::globalVariables ("cloneType") + utils::globalVariables ("cluster") + utils::globalVariables ("cdr3") + utils::globalVariables ("Freq") + utils::globalVariables ("include") + utils::globalVariables ("indicator") utils::globalVariables ("is") - utils::globalVariables ("Sample") + utils::globalVariables ("method") + utils::globalVariables ("Na.values") + utils::globalVariables ("ncells") + utils::globalVariables ("Proportion") utils::globalVariables ("productive") - utils::globalVariables ("variable") - utils::globalVariables ("stratum") - utils::globalVariables ("alluvium") - utils::globalVariables ("x") - utils::globalVariables ("colData") - utils::globalVariables ("colData<-") - utils::globalVariables ("as.dendrogram") - utils::globalVariables (".get.empirical.p") - utils::globalVariables ("patient") - utils::globalVariables ("combn") - utils::globalVariables ("include") - utils::globalVariables ("cell") - utils::globalVariables ("chain1") - utils::globalVariables ("chain2") - utils::globalVariables ("total") utils::globalVariables ("prop") - utils::globalVariables ("var") - utils::globalVariables ("contig") - utils::globalVariables ("contigs") - utils::globalVariables ("group.by") - utils::globalVariables ("split.by") - utils::globalVariables ("cluster") - utils::globalVariables ("threshold") - utils::globalVariables ("ncells") - utils::globalVariables ("Top_State") - utils::globalVariables ("mat") - utils::globalVariables ("element.names") - utils::globalVariables ("y") - utils::globalVariables ("to") - utils::globalVariables ("from") - utils::globalVariables ("weight") + utils::globalVariables ("reads") + utils::globalVariables ("Sample") + utils::globalVariables ("scaled") utils::globalVariables ("size") utils::globalVariables ("Top_state") - utils::globalVariables ("read.csv") - utils::globalVariables ("read.delim") - utils::globalVariables ("parse10x") - utils::globalVariables ("parseAIRR") - utils::globalVariables (".") - utils::globalVariables ("cdr3_nt") - utils::globalVariables ("CTaa") - utils::globalVariables ("NA.values") + utils::globalVariables ("total") utils::globalVariables ("total.cells") - utils::globalVariables ("clone") utils::globalVariables ("v.gene") - utils::globalVariables ("AA") + utils::globalVariables ("value") + utils::globalVariables ("values") + utils::globalVariables ("Var1") + utils::globalVariables ("Var2") + utils::globalVariables ("variable") + utils::globalVariables ("weights") invisible () } diff --git a/R/highlightClones.R b/R/highlightClones.R index 7500a52b..1ce456ef 100644 --- a/R/highlightClones.R +++ b/R/highlightClones.R @@ -16,7 +16,7 @@ #' scRep_example <- combineExpression(combined, scRep_example) #' #' #Using highlightClonotype() -#' scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", +#' scRep_example <- highlightClones(scRep_example , cloneCall= "aa", #' sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) #' #' @param sc The Seurat object to attach after \code{\link{combineExpression}} diff --git a/R/loadContigs.R b/R/loadContigs.R index 3f19cf4b..028b4c17 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -28,6 +28,7 @@ #' @param dir The directory in which contigs are located or a list with contig elements #' @param format The format of the single-cell contig, currently supporting: #' "10X", "AIRR", "TRUST4", "BD", and "WAT3R" +#' @importFrom utils read.csv read.delim #' @export #' @return List of contigs for further processing in scRepertoire loadContigs <- function(dir, @@ -43,7 +44,7 @@ loadContigs <- function(dir, contig.files <- list.files(dir, file.pattern, recursive = TRUE, full.names = TRUE) if (format %in% c("10X", "WAT3R")) { df <- lapply(contig.files, read.csv) - } else if (format %!in% c("10X", "WAT3R")) { + } else { df <- lapply(contig.files, read.delim) } #Already loaded list or data frame diff --git a/R/startracDiversity.R b/R/startracDiversity.R index 6feaadd4..08b485c5 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -51,7 +51,7 @@ StartracDiversity <- function(sc, group.by = NULL, exportTable = FALSE, palette = "inferno") { - + majorCluster <- NULL cloneCall <- theCall(cloneCall) df <- grabMeta(sc) barcodes <- rownames(df) diff --git a/R/vizGenes.R b/R/vizGenes.R index ca51a33a..6caf260c 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -48,6 +48,7 @@ vizGenes <- function(df, split.by = NULL, exportTable = FALSE, palette = "inferno") { + element.names <- NULL df <- list.input.return(df, split.by = split.by) if(!is.null(group.by)) { df <- groupList(df, group.by) diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index 843d20b1..4e2ba184 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -57,7 +57,7 @@ selecting clonotypes } \description{ This function produces an alluvial or area graph of the proportion of -the indicated clonotypes for all or selected samples. Clonotypes can be +the indicated clonotypes for all or selected samples. clonotypes can be selected using the clonotypes parameter with the specific sequence of interest or using the number parameter with the top n clonotypes by proportion to be visualized. diff --git a/man/clonotypeBias.Rd b/man/clonotypeBias.Rd index 51c0d125..f253de79 100644 --- a/man/clonotypeBias.Rd +++ b/man/clonotypeBias.Rd @@ -46,21 +46,25 @@ is composed of cells from a single compartment or cluster, while a clonotype bias of 0 matches the background subtype distribution. } \examples{ -\dontrun{ -Getting the combined contigs -combined <- combineTCR(contig_list, rep(c("PX", "PY", "PZ"), each=2), -rep(c("P", "T"), 3)) +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #Using combineExpresion() -sce <- combineExpression(combined, sce) +scRep_example <- combineExpression(combined, scRep_example) +scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #Using occupiedscRepertoire() -clonotypeBias(sce, cloneCall = "CTaa", split.by = "Patient", group.by = "seurat_clusters", -n.boots = 20, min.expand = 2) -} +clonotypeBias(scRep_example, + cloneCall = "aa", + split.by = "Patient", + group.by = "seurat_clusters", + n.boots = 20, + min.expand = 2) + } diff --git a/man/highlightClones.Rd b/man/highlightClones.Rd index 930545e7..4fee86c5 100644 --- a/man/highlightClones.Rd +++ b/man/highlightClones.Rd @@ -39,7 +39,7 @@ scRep_example <- get(data("scRep_example")) scRep_example <- combineExpression(combined, scRep_example) #Using highlightClonotype() -scRep_example <- highlightClonotypes(scRep_example , cloneCall= "aa", +scRep_example <- highlightClones(scRep_example , cloneCall= "aa", sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index f8a5ca17..fc90e720 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -724,12 +724,12 @@ shared.clones <- clonalNetwork(scRep_example, head(shared.clones) ``` -## highlightClonotypes +## highlightClones We can also look at the clonotypes by calling specific sequences in the `highlightClonotypes()` below. In order to highlight the clonotypes, we first need to use the **cloneCall**, the type of sequence we will be using, and then the specific sequences themselves using **sequence**. Below, we can see the steps to highlight the two most prominent sequences *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*. ```{r} -scRep_example <- highlightClonotypes(scRep_example, +scRep_example <- highlightClones(scRep_example, cloneCall= "aa", sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF")) From c2f4d909fee33ce8928fa76295af7cae9b051d56 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 24 Sep 2023 19:06:12 -0500 Subject: [PATCH 059/142] clonalRarefaction draft --- DESCRIPTION | 5 +-- NAMESPACE | 2 ++ R/clonalBias.R | 11 ++----- R/clonalCompare.R | 4 +-- R/clonalDiversity.R | 8 +---- R/clonalOverlap.R | 3 +- R/clonalRarefaction.R | 67 +++++++++++++++++++++++++++++++++++++--- R/clonalScatter.R | 2 +- R/exportClones.R | 3 +- R/percentAA.R | 7 ++--- R/percentGenes.R | 7 ++--- R/percentVJ.R | 8 ++--- R/utils.R | 11 +++++++ man/clonalRarefaction.Rd | 61 ++++++++++++++++++++++++++++++++++++ 14 files changed, 153 insertions(+), 46 deletions(-) create mode 100644 man/clonalRarefaction.Rd diff --git a/DESCRIPTION b/DESCRIPTION index f20117ea..e969b944 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -5,7 +5,7 @@ Authors@R: c( person(given = "Nick", family = "Borcherding", role = c("aut", "cre"), email = "ncborch@gmail.com"), person(given = "Qile", family = "Yang", role = c("aut"), email = "qile0317@gmail.com")) Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. -License: MIT +License: MIT + file LICENSE Encoding: UTF-8 LazyData: true RoxygenNote: 7.2.3 @@ -35,7 +35,8 @@ Imports: ggdendro, truncdist, VGAM, - cubature + cubature, + iNEXT Suggests: knitr, rmarkdown, diff --git a/NAMESPACE b/NAMESPACE index 5181f698..923f2b4f 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -73,6 +73,8 @@ importFrom(ggraph,ggraph) importFrom(ggraph,guide_edge_colourbar) importFrom(ggraph,scale_edge_colour_gradientn) importFrom(grDevices,hcl.colors) +importFrom(iNEXT,ggiNEXT) +importFrom(iNEXT,iNEXT) importFrom(igraph,V) importFrom(igraph,`V<-`) importFrom(igraph,components) diff --git a/R/clonalBias.R b/R/clonalBias.R index b90f2d15..cee5c9fb 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -52,6 +52,7 @@ clonotypeBias <- function(df, n.boots = 20, min.expand=10, exportTable = FALSE) { + cloneCall <- theCall(cloneCall) bias <- get_clono_bias(df, split.by = split.by, @@ -102,15 +103,7 @@ get_clono_bg <- function(df, group.by=group.by, cloneCall=cloneCall, min.expand=10) { - if (!is.null(split.by)) { - df <- list.input.return(df, split.by) - } else { - if (inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { - df <- list("Object" = grabMeta(df)) - } - } - cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) bg <- list() for (s in seq_along(df)) { diff --git a/R/clonalCompare.R b/R/clonalCompare.R index 0f3bd290..1d23328d 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -57,10 +57,8 @@ clonalCompare <- function(df, if(!is.null(top.clones) & !is.null(clonotypes)) { top.clones <- NULL } - - df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) Con.df <- NULL #Loop through the list to get a proportional summary diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 3597cb28..73787c8e 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -56,14 +56,8 @@ clonalDiversity <- function(df, if(return.boots) { exportTable <- TRUE } - df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } + df <- .data.wrangle(df, split.by, cloneCall, chain) mat <- NULL sample <- c() if (!is.null(group.by) || !is.null(x.axis)) { diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 4e4cf3d4..e7cb8d5d 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -44,9 +44,8 @@ clonalOverlap <- function(df, } else { return_type = "unique" } - df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) df <- df[order(names(df))] values <- str_sort(as.character(unique(names(df))), numeric = TRUE) df <- df[quiet(dput(values))] diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index 3fe2a130..cd2f7330 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -1,6 +1,65 @@ -clonalRarefaction <- function(cloneCall = "strict", - method = NULL, +#' Calculate rarefaction based on the abundance of clones +#' +#' This functions uses the Hill numbers of order q: species richness (q = 0), +#' Shannon diversity (q = 1, the exponential of Shannon entropy) and Simpson +#' diversity (q = 2, the inverse of Simpson concentration) to compute diversity +#' estimates for rarefaction and extrapolation. The function relies on +#' \code{\link[iNEXT]{iNEXT}} and read/please cite the +#' \href{https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12613}{manuscript} +#' if using this function. +#' +#' #' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' clonalRarefaction(combined[c(1,2)], cloneCall = "gene", n.boots = 3) +#' +#' +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. +#' @param cloneCall How to call the clonotype - VDJC gene (gene), +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or +#' VDJC gene + CDR3 nucleotide (strict). +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param group.by Variable in which to group the diversity calculation +#' @param x.axis Additional variable in which to split the x.axis +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param plot.type sample-size-based rarefaction/extrapolation curve (\code{type = 1}); +#' sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}). +#' @param hill.numbers The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson) +#' @param n.boots The number of bootstraps to downsample in order to get mean diversity +#' +#' @importFrom iNEXT iNEXT ggiNEXT +#' @import ggplot2 +clonalRarefaction <- function(df, + cloneCall = "strict", chain = "both", - split.by = NULL) { + group.by = NULL, + split.by = NULL, + plot.type = 1, + hill.numbers = 0, + n.boots = 20, + exportTable = FALSE, + palette = "inferno") { + cloneCall <- theCall(cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) + mat.list <- lapply(df, function(x) { + table(x[,cloneCall]) + }) + col <- length(df) + mat <- iNEXT(mat.list, q=hill.numbers, datatype="abundance",nboot = n.boots) + plot <- ggiNEXT(mat, type=plot.type) + + scale_shape_manual(values = rep(16,col)) + + scale_fill_manual(values = rep("white", col)) + + scale_color_manual(values = c(.colorizer(palette,col))) + + theme_classic() + if (exportTable == TRUE) { + return(plot["data"]) + } else { + return(plot) + } -} \ No newline at end of file +} diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 4327ea8a..45a35a33 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -52,8 +52,8 @@ clonalScatter <- function(df, if (!is.null(seed)) { set.seed(seed) } - df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) if (chain != "both") { for (i in axes) { diff --git a/R/exportClones.R b/R/exportClones.R index 0e2a00ed..82376026 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -33,8 +33,7 @@ exportClones <- function(df, dir = NULL, file.name = "clones.csv") { - df <- list.input.return(df, split.by) - df <- checkBlanks(df, "CTgene") + df <- .data.wrangle(df, split.by, "CTgene", "both") df <- bind_rows(df, .id = "group") diff --git a/R/percentAA.R b/R/percentAA.R index 84066f95..41a3c8f1 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -30,11 +30,8 @@ percentAA <- function(df, aa.length = 20, exportTable = FALSE, palette = "inferno") { - df <- list.input.return(df, split.by) - df <- checkBlanks(df, "CTaa") - for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, "CTaa") - } + cloneCall <- theCall(cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) res.list <- list() for (i in seq_along(df)) { strings <- df[[i]][,"CTaa"] diff --git a/R/percentGenes.R b/R/percentGenes.R index 58824602..fb776026 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -33,11 +33,8 @@ percentGenes <- function(df, exportTable = FALSE, palette = "inferno") { - df <- list.input.return(df, split.by) - df <- checkBlanks(df, "CTgene") - for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") - } + cloneCall <- theCall(cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) #Parsing gene input if (gene %in% c("Vgene", "V", "v", "v.gene")) { gene.loci <- paste0(chain, "V") diff --git a/R/percentVJ.R b/R/percentVJ.R index a51b5a76..2cabb7a7 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -30,12 +30,8 @@ percentVJ <- function(df, split.by = NULL, exportTable = FALSE, palette = "inferno") { - - df <- list.input.return(df, split.by) - df <- checkBlanks(df, "CTgene") - for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") - } + cloneCall <- theCall(cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) if(chain %in% c("TRA", "TRG", "IGL")) { positions <- c(1,2) diff --git a/R/utils.R b/R/utils.R index 4a8996b1..8aa9371a 100644 --- a/R/utils.R +++ b/R/utils.R @@ -481,3 +481,14 @@ is_df_or_list_of_df <- function(x) { names(df) <- unique return(df) } + +.data.wrangle <- function(df, split.by, cloneCall, chain) { + df <- list.input.return(df, split.by) + df <- checkBlanks(df, cloneCall) + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + } + return(df) +} diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd new file mode 100644 index 00000000..573427c6 --- /dev/null +++ b/man/clonalRarefaction.Rd @@ -0,0 +1,61 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/clonalRarefaction.R +\name{clonalRarefaction} +\alias{clonalRarefaction} +\title{Calculate rarefaction based on the abundance of clones} +\usage{ +clonalRarefaction( + df, + cloneCall = "strict", + chain = "both", + group.by = NULL, + split.by = NULL, + plot.type = 1, + hill.numbers = 0, + n.boots = 20, + exportTable = FALSE, + palette = "inferno" +) +} +\arguments{ +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} + +\item{cloneCall}{How to call the clonotype - VDJC gene (gene), +CDR3 nucleotide (nt), CDR3 amino acid (aa), or +VDJC gene + CDR3 nucleotide (strict).} + +\item{chain}{indicate if both or a specific chain should be used - +e.g. "both", "TRA", "TRG", "IGH", "IGL"} + +\item{group.by}{Variable in which to group the diversity calculation} + +\item{split.by}{If using a single-cell object, the column header +to group the new list. NULL will return clusters.} + +\item{plot.type}{sample-size-based rarefaction/extrapolation curve (\code{type = 1}); +sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}).} + +\item{hill.numbers}{The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson)} + +\item{n.boots}{The number of bootstraps to downsample in order to get mean diversity} + +\item{x.axis}{Additional variable in which to split the x.axis} +} +\description{ +This functions uses the Hill numbers of order q: species richness (q = 0), +Shannon diversity (q = 1, the exponential of Shannon entropy) and Simpson +diversity (q = 2, the inverse of Simpson concentration) to compute diversity +estimates for rarefaction and extrapolation. The function relies on +\code{\link[iNEXT]{iNEXT}} and read/please cite the +\href{https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12613}{manuscript} +if using this function. +} +\details{ +#' @examples +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +clonalRarefaction(combined[c(1,2)], cloneCall = "gene", n.boots = 3) +} From 4bf4c0f9d97511041c693f6b91684abc2f7eb0c0 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 25 Sep 2023 14:03:53 -0500 Subject: [PATCH 060/142] organize clonalLength() --- NAMESPACE | 5 +- NEWS | 1 + R/clonalBias.R | 6 +- R/clonalLength.R | 128 +++++++++++++++++++++++ R/clonalRarefaction.R | 2 +- R/lengthContig.R | 103 ------------------ R/percentAA.R | 3 +- R/percentGenes.R | 3 +- R/percentVJ.R | 3 +- man/{clonotypeBias.Rd => clonalBias.Rd} | 8 +- man/{lengthContig.Rd => clonalLength.Rd} | 12 +-- man/clonalRarefaction.Rd | 2 - vignettes/vignette.Rmd | 35 ++++++- 13 files changed, 180 insertions(+), 131 deletions(-) create mode 100644 R/clonalLength.R delete mode 100644 R/lengthContig.R rename man/{clonotypeBias.Rd => clonalBias.Rd} (96%) rename man/{lengthContig.Rd => clonalLength.Rd} (88%) diff --git a/NAMESPACE b/NAMESPACE index 923f2b4f..434c91d0 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,17 +5,19 @@ export(abundanceContig) export(addVariable) export(alluvialClonotypes) export(checkContig) +export(clonalBias) export(clonalCompare) export(clonalDiversity) export(clonalHomeostasis) +export(clonalLength) export(clonalNetwork) export(clonalOverlap) export(clonalOverlay) export(clonalProportion) export(clonalQuant) +export(clonalRarefaction) export(clonalScatter) export(clonalSizeDistribution) -export(clonotypeBias) export(clusterTCR) export(combineBCR) export(combineExpression) @@ -24,7 +26,6 @@ export(createHTOContigList) export(exportClones) export(getCirclize) export(highlightClones) -export(lengthContig) export(loadContigs) export(occupiedscRepertoire) export(percentAA) diff --git a/NEWS b/NEWS index 290ae125..1a35180f 100644 --- a/NEWS +++ b/NEWS @@ -12,6 +12,7 @@ NEW FEATURES * Changed scatterClonotypes to clonalScatter * Changed quantContig to clonalQuant * Changed highlightClonotypes to highlightClones +* Changed lengthContigs to clonalLength UNDERLYING CHANGES diff --git a/R/clonalBias.R b/R/clonalBias.R index cee5c9fb..4dc252a2 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -22,7 +22,7 @@ #' scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #' #' #Using occupiedscRepertoire() -#' clonotypeBias(scRep_example, +#' clonalBias(scRep_example, #' cloneCall = "aa", #' split.by = "Patient", #' group.by = "seurat_clusters", @@ -45,7 +45,7 @@ #' @importFrom stringr str_sort #' @export #' @return Returns ggplot of the clonotype bias -clonotypeBias <- function(df, +clonalBias <- function(df, cloneCall="strict", split.by=NULL, group.by=NULL, @@ -103,7 +103,7 @@ get_clono_bg <- function(df, group.by=group.by, cloneCall=cloneCall, min.expand=10) { - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, split.by, cloneCall, "both") bg <- list() for (s in seq_along(df)) { diff --git a/R/clonalLength.R b/R/clonalLength.R new file mode 100644 index 00000000..d2a9e315 --- /dev/null +++ b/R/clonalLength.R @@ -0,0 +1,128 @@ +#' Demonstrate the distribution of lengths filtered clones +#' +#' This function displays either the nucleotide (nt) or amino +#' acid (aa) sequence length. The sequence length visualized can be +#' selected using the chains parameter, either the combined clonotype +#' (both chains) or across all single chains. Visualization can either +#' be a histogram or if scale = TRUE, the output will be a density plot. +#' Multiple sequencing runs can be group together using the +#' group parameter. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' clonalLength(combined, cloneCall="aa", chain = "both") +#' +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. +#' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), +#' CDR3 amino acid (aa). +#' @param group.by The group header for which you would like to analyze +#' the data. +#' @param split.by If using a single-cell object, the column header +#' to group the new list. NULL will return clusters. +#' @param scale Converts the graphs into density plots in order to show +#' relative distributions. +#' @param order Maintain the order of the list when plotting +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any hcl.pals() +#' @importFrom stringr str_split +#' @importFrom ggplot2 ggplot +#' @export +#' @return ggplot of the discrete or relative length distributions of +#' clonotype sequences +clonalLength <- function(df, + cloneCall = "aa", + chain = "both", + group.by = NULL, + split.by = NULL, + order = TRUE, + scale = FALSE, + exportTable = FALSE, + palette = "inferno") { + + cloneCall <- theCall(cloneCall) + df <- list.input.return(df, split.by) + + #Sorting out graphing parameters + xlab <- "Length" + if(cloneCall == "CTnt") { + ylab <- "CDR3 (NT)" + } else if (cloneCall == "CTaa") { + ylab <- "CDR3 (AA)" + } else { + stop("Please make a selection of the type of + CDR3 sequence to analyze by using `cloneCall`") + } + + #Identifying and assigning chains + chain.pos <- which(colnames(df[[1]]) == "cdr3_aa1")-1 + c1 <- na.omit(unique(substr(df[[1]][1:10,chain.pos], 1,3))) + c2 <- switch(c1, + "TRA" = "TRB", + "IGH" = "IGL", + "TRG" = "TRD") + + #Calculating Length + Con.df <- NULL + Con.df <- lengthDF(df, cloneCall, chain, group.by, c1, c2) + + if(is.null(group.by) & order) { + Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) + } + names <- names(df) + + #Skip plotting if want to export table + if (exportTable == TRUE) { + return(Con.df) + } + + #Plotting + if (!is.null(group.by)) { + fill <- group.by + col <- length(unique(Con.df[,group.by])) + if (scale == TRUE) { + yplus <- "Percent of " + plot <- ggplot(Con.df, aes(fill=Con.df[,group.by], + x = length, + y = (after_stat(count))/sum(after_stat(count))*100)) + + geom_density(aes(y=after_stat(scaled)), + alpha=.5, lwd=.25, color="black") + } else { + yplus <- "Number of " + plot <- ggplot(Con.df,aes(x = as.factor(length), + fill=Con.df[,group.by]))+ + geom_bar(position = position_dodge2(preserve = "single"), + color="black", lwd=0.25, width=0.9) + + scale_x_discrete(breaks = round(seq(min(Con.df$length), + max(Con.df$length), by = 5),10)) } + } else if (is.null(group.by)){ + fill <- "Samples" + col <- length(unique(Con.df$values)) + if (scale == TRUE) { + yplus <- "Percent of " + plot <- ggplot(Con.df, aes(x = length, + y = (after_stat(count))/sum(after_stat(count))*100, + fill=values)) + + geom_density(aes(y=after_stat(scaled)), + alpha=0.5, lwd=0.25, color="black") + } else { + yplus <- "Number of " + plot <- ggplot(Con.df, aes(as.factor(length), fill=values)) + + geom_bar(position = position_dodge2(preserve = "single"), + color="black", lwd=0.25) + + scale_x_discrete(breaks = round(seq(min(Con.df$length), + max(Con.df$length), by = 5),10))} } + plot <- plot + + scale_fill_manual(values = .colorizer(palette,col)) + + labs(fill = fill) + + ylab(paste(yplus, ylab, sep="")) + + xlab(xlab) + + theme_classic() + + return(plot) +} \ No newline at end of file diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index cd2f7330..a278861b 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -24,7 +24,6 @@ #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by Variable in which to group the diversity calculation -#' @param x.axis Additional variable in which to split the x.axis #' @param split.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param plot.type sample-size-based rarefaction/extrapolation curve (\code{type = 1}); @@ -34,6 +33,7 @@ #' #' @importFrom iNEXT iNEXT ggiNEXT #' @import ggplot2 +#' @export clonalRarefaction <- function(df, cloneCall = "strict", chain = "both", diff --git a/R/lengthContig.R b/R/lengthContig.R deleted file mode 100644 index 33940813..00000000 --- a/R/lengthContig.R +++ /dev/null @@ -1,103 +0,0 @@ -#' Demonstrate the distribution of lengths filtered contigs. -#' -#' This function displays either the nucleotide (nt) or amino -#' acid (aa) sequence length. The sequence length visualized can be -#' selected using the chains parameter, either the combined clonotype -#' (both chains) or across all single chains. Visualization can either -#' be a histogram or if scale = TRUE, the output will be a density plot. -#' Multiple sequencing runs can be group together using the -#' group parameter. -#' -#' @examples -#' #Making combined contig data -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' lengthContig(combined, cloneCall="aa", chain = "both") -#' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -#' \code{\link{combineExpression}}. -#' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), -#' CDR3 amino acid (aa). -#' @param group.by The group header for which you would like to analyze -#' the data. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param scale Converts the graphs into density plots in order to show -#' relative distributions. -#' @param order Maintain the order of the list when plotting -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() -#' @importFrom stringr str_split -#' @importFrom ggplot2 ggplot -#' @export -#' @return ggplot of the discrete or relative length distributions of -#' clonotype sequences -lengthContig <- function(df, - cloneCall = "aa", - chain = "both", - group.by = NULL, - split.by = NULL, - order = TRUE, - scale = FALSE, - exportTable = FALSE, - palette = "inferno") { - df <- list.input.return(df, split.by) - if(cloneCall == "nt") { cloneCall <- "CTnt" - ylab <- "CDR3 (NT)" - } else if (cloneCall == "aa") { cloneCall <- "CTaa" - ylab <- "CDR3 (AA)" - } else { stop("Please make a selection of the type of - CDR3 sequence to analyze by using `cloneCall`") } - AB <- grep("TRA", df[[1]]$TCR1) - IG <- grep("IGH", df[[1]]$TCR1) - if (length(AB) > 1 && length(IG) == 0) { - c1 <- "TRA" - c2 <- "TRB" - } else if (length(AB) > 1) { - c1 <- "IGH" - c2 <- "IGL" - } else { - c1 <- "TRG" - c2 <- "TRD" - } - xlab <- "Length" - Con.df <- NULL - Con.df <- lengthDF(df, cloneCall, chain, group.by, c1, c2) - if(is.null(group.by) & order) { - Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) - } - names <- names(df) - if (!is.null(group.by)) { - fill <- group.by - col <- length(unique(Con.df[,group.by])) - if (scale == TRUE) { yplus <- "Percent of " - plot <- ggplot(Con.df, aes(fill=Con.df[,group.by], - length,(after_stat(count))/sum(after_stat(count))*100)) + - geom_density(aes(y=after_stat(scaled)),alpha=.5,lwd=.25,color="black") - } else { yplus <- "Number of " - plot<-ggplot(Con.df,aes(as.factor(length),fill=Con.df[,group.by]))+ - geom_bar(position = position_dodge2(preserve = "single"), - color="black", lwd=0.25, width=0.9) + - scale_x_discrete(breaks = round(seq(min(Con.df$length), - max(Con.df$length), by = 5),10)) } - } else if (is.null(group.by)){ - fill <- "Samples" - col <- length(unique(Con.df$values)) - if (scale == TRUE) { yplus <- "Percent of " - plot <- ggplot(Con.df, aes(length, (after_stat(count))/sum(after_stat(count))*100, - fill=values)) + geom_density(aes(y=after_stat(scaled)), alpha=0.5, - lwd=0.25, color="black") - } else { yplus <- "Number of " - plot <- ggplot(Con.df, aes(as.factor(length), fill=values)) + - geom_bar(position = position_dodge2(preserve = "single"), - color="black", lwd=0.25) + - scale_x_discrete(breaks = round(seq(min(Con.df$length), - max(Con.df$length), by = 5),10))} } - plot <- plot + scale_fill_manual(values = .colorizer(palette,col)) + - labs(fill = fill) + ylab(paste(yplus, ylab, sep="")) + - xlab(xlab) + theme_classic() - if (exportTable == TRUE) { return(Con.df) } - return(plot)} \ No newline at end of file diff --git a/R/percentAA.R b/R/percentAA.R index 41a3c8f1..9b8b4ed9 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -30,8 +30,7 @@ percentAA <- function(df, aa.length = 20, exportTable = FALSE, palette = "inferno") { - cloneCall <- theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, split.by, "CTaa", chain) res.list <- list() for (i in seq_along(df)) { strings <- df[[i]][,"CTaa"] diff --git a/R/percentGenes.R b/R/percentGenes.R index fb776026..4aa8c736 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -33,8 +33,7 @@ percentGenes <- function(df, exportTable = FALSE, palette = "inferno") { - cloneCall <- theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, split.by, "CTgene", chain) #Parsing gene input if (gene %in% c("Vgene", "V", "v", "v.gene")) { gene.loci <- paste0(chain, "V") diff --git a/R/percentVJ.R b/R/percentVJ.R index 2cabb7a7..3aefeae8 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -30,8 +30,7 @@ percentVJ <- function(df, split.by = NULL, exportTable = FALSE, palette = "inferno") { - cloneCall <- theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, split.by, "CTgene", chain) if(chain %in% c("TRA", "TRG", "IGL")) { positions <- c(1,2) diff --git a/man/clonotypeBias.Rd b/man/clonalBias.Rd similarity index 96% rename from man/clonotypeBias.Rd rename to man/clonalBias.Rd index f253de79..ceb94731 100644 --- a/man/clonotypeBias.Rd +++ b/man/clonalBias.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/clonalBias.R -\name{clonotypeBias} -\alias{clonotypeBias} +\name{clonalBias} +\alias{clonalBias} \title{Examine clonotype bias} \usage{ -clonotypeBias( +clonalBias( df, cloneCall = "strict", split.by = NULL, @@ -59,7 +59,7 @@ scRep_example <- combineExpression(combined, scRep_example) scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #Using occupiedscRepertoire() -clonotypeBias(scRep_example, +clonalBias(scRep_example, cloneCall = "aa", split.by = "Patient", group.by = "seurat_clusters", diff --git a/man/lengthContig.Rd b/man/clonalLength.Rd similarity index 88% rename from man/lengthContig.Rd rename to man/clonalLength.Rd index 207e33cf..36ab365f 100644 --- a/man/lengthContig.Rd +++ b/man/clonalLength.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/lengthContig.R -\name{lengthContig} -\alias{lengthContig} -\title{Demonstrate the distribution of lengths filtered contigs.} +% Please edit documentation in R/clonalLength.R +\name{clonalLength} +\alias{clonalLength} +\title{Demonstrate the distribution of lengths filtered clones} \usage{ -lengthContig( +clonalLength( df, cloneCall = "aa", chain = "both", @@ -59,6 +59,6 @@ group parameter. combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -lengthContig(combined, cloneCall="aa", chain = "both") +clonalLength(combined, cloneCall="aa", chain = "both") } diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd index 573427c6..f0f36256 100644 --- a/man/clonalRarefaction.Rd +++ b/man/clonalRarefaction.Rd @@ -39,8 +39,6 @@ sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapol \item{hill.numbers}{The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson)} \item{n.boots}{The number of bootstraps to downsample in order to get mean diversity} - -\item{x.axis}{Additional variable in which to split the x.axis} } \description{ This functions uses the Hill numbers of order q: species richness (q = 0), diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index fc90e720..b05ee7e9 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -275,7 +275,7 @@ abundanceContig(combined, scale = TRUE) ``` -## lengthContig +## clonalLength We can look at the length distribution of the CDR3 sequences by calling the `lengtheContig()` function. Importantly, unlike the other basic visualizations, the **cloneCall** can only be "nt" or "aa". Due to the method of calling clonotypes as outlined above, the length should reveal a multimodal curve, this is a product of using the **NA** for the unreturned chain sequence and multiple chains within a single barcode. @@ -284,11 +284,11 @@ We can look at the length distribution of the CDR3 sequences by calling the `len + "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" to select single chain ```{r} -lengthContig(combined, +clonalLength(combined, cloneCall="aa", chain = "both") -lengthContig(combined, +clonalLength(combined, cloneCall="aa", chain = "TRA", scale = TRUE) @@ -405,7 +405,7 @@ vizGenes(combined[c(2,4,6,8)], Quantify the proportion of amino acids along the cdr3 sequence with `percentAA()`. By default, the function will pad the sequences with NAs up to the maximum of **aa.length**. Sequences longer than **aa.length** will be removed before visualization. -```{r} +```{r, message = FALSE} percentAA(combined, chain = "TRB", aa.length = 20) @@ -563,6 +563,33 @@ clonalDiversity(combined, clonalDiversity(combined, cloneCall = "gene") ``` +## clonaRarefaction + +We can also use Hill numbers to estimate the rarefaction using the the abundance of clones across groupings. This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). + +**plot.type** ++ 1 - sample-size-based rarefaction/extrapolation ++ 2 - sample completeness curve ++ 3 - coverage-based rarefaction/extrapolation curve + +**hill.numbers** ++ 0 - species richness ++ 1 - Shannon ++ 2 - Simpson + +```{r} +clonalRarefaction(combined, + plot.type = 1, + n.boots = 1) + +clonalRarefaction(combined, + plot.type = 2, + n.boots = 1) + +clonalRarefaction(combined, + plot.type = 3, + n.boots = 1) +``` ## clonalScatter A number of users requested a visualization from the work of [Wu, et al 2020, PMID: 32103181](https://pubmed.ncbi.nlm.nih.gov/32103181/) that allows for the direct comparison of clonotypes between 2 samples. ```clonalScatter()``` will organize two samples from the combineTCR/BCR product, count the relative clonotypes, and produce a scatter plot comparing the two samples. From 9eabd126ca0d5222673833f13816c09a41775567 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 26 Sep 2023 05:37:56 -0500 Subject: [PATCH 061/142] clean clonalHomeostasis/clonalProportion --- R/clonalHomeostasis.R | 22 ++++++++++++---------- R/clonalProportion.R | 39 +++++++++++++++++---------------------- R/clonalRarefaction.R | 3 +++ R/loadContigs.R | 2 +- man/clonalProportion.Rd | 12 ++++++------ vignettes/vignette.Rmd | 26 +++++++++++++------------- 6 files changed, 52 insertions(+), 52 deletions(-) diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 19dd3a18..ad36538e 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -44,22 +44,20 @@ clonalHomeostasis <- function(df, exportTable = FALSE, palette = "inferno") { cloneTypes <- c(None = 0, cloneTypes) - df <- list.input.return(df, split.by = split.by) + cloneCall <- theCall(cloneCall) - df <- checkList(df) - df <- checkBlanks(df, cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) + if(!is.null(group.by)) { df <- groupList(df, group.by) } - + + #Generating data matrix to store value mat <- matrix(0, length(df), length(cloneTypes) - 1, dimnames = list(names(df), names(cloneTypes)[-1])) - if (chain != "both") { - for (x in seq_along(df)) { - df[[x]] <- off.the.chain(df[[x]], chain, cloneCall) - } - } + + #Assigning the clonal grouping df <- lapply(df, '[[', cloneCall) df <- lapply(df, na.omit) fun <- function(x) { table(x)/length(x) } @@ -70,7 +68,11 @@ clonalHomeostasis <- function(df, colnames(mat)[i-1] <- paste0(names(cloneTypes[i]), ' (', cloneTypes[i-1], ' < X <= ', cloneTypes[i], ')') } - if (exportTable == TRUE) { return(mat) } + if (exportTable) { + return(mat) + } + + #Plotting mat_melt <- melt(mat) col <- length(unique(mat_melt$Var2)) plot <- ggplot(mat_melt, aes(x=as.factor(Var1), y=value, fill=Var2)) + diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 13ab553c..f9159e30 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -2,10 +2,10 @@ #' #' This function calculates the relative clonal space occupied by the #' clonotypes. The grouping of these clonotypes is based on the parameter -#' split, at default, split will group the clonotypes into bins of 1:10, -#' 11:100, 101:1001, etc. To adjust the clonotypes selected, change the -#' numbers in the variable split. If a matrix output for the data is -#' preferred, set exportTable = TRUE. +#' **clonal.split**, at default, **clonal.split** will group the clonotypes +#' into bins of 1:10, 11:100, 101:1001, etc. To adjust the clonotypes +#' selected, change the numbers in the variable split. If a matrix output +#' for the data is preferred, set exportTable = TRUE. #' #' @examples #' #Making combined contig data @@ -16,7 +16,7 @@ #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param split The cutpoints for the specific clonotypes. +#' @param clonal.split The cut points for the specific clonotypes. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -37,7 +37,7 @@ #' @export #' @return ggplot of the space occupied by the specific rank of clonotypes clonalProportion <- function(df, - split = c(10, 100, 1000, 10000, 30000, 100000), + clonal.split = c(10, 100, 1000, 10000, 30000, 100000), cloneCall = "strict", chain = "both", group.by = NULL, @@ -45,34 +45,32 @@ clonalProportion <- function(df, exportTable = FALSE, palette = "inferno") { Con.df <- NULL - df <- list.input.return(df, split.by = split.by) cloneCall <- theCall(cloneCall) - df <- checkList(df) - df <- checkBlanks(df, cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) if(!is.null(group.by)) { df <- groupList(df, group.by) } - mat <- matrix(0, length(df), length(split), dimnames = list(names(df), - paste0('[', c(1, split[-length(split)] + 1), ':', split, ']'))) - if (chain != "both") { - for (x in seq_along(df)) { - df[[x]] <- off.the.chain(df[[x]], chain, cloneCall) - } - } + + #Generating data matrix to store value + mat <- matrix(0, length(df), length(clonal.split), dimnames = list(names(df), + paste0('[', c(1, clonal.split[-length(clonal.split)] + 1), ':', clonal.split, ']'))) + + #Assigning the clonal grouping df <- lapply(df, '[[', cloneCall) df <- lapply(df, na.omit) df <- lapply(df, as.data.frame(table)) for (i in seq_along(df)) { df[[i]] <- rev(sort(as.numeric(df[[i]][,2]))) } - cut <- c(1, split[-length(split)] + 1) - for (i in seq_along(split)) { - mat[,i] <- vapply(df, function (x) sum(na.omit(x[cut[i]:split[i]])), + cut <- c(1, clonal.split[-length(clonal.split)] + 1) + for (i in seq_along(clonal.split)) { + mat[,i] <- vapply(df, function (x) sum(na.omit(x[cut[i]:clonal.split[i]])), FUN.VALUE = numeric(1)) } if (exportTable == TRUE) { return(mat) } + #Plotting mat_melt <- melt(mat) col <- length(unique(mat_melt$Var2)) plot <- ggplot(mat_melt, aes(x=as.factor(Var1), y=value, fill=Var2)) + @@ -84,7 +82,4 @@ clonalProportion <- function(df, ylab("Occupied Repertoire Space") + theme_classic() return(plot) - - - } diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index a278861b..d40981d4 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -30,6 +30,9 @@ #' sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}). #' @param hill.numbers The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson) #' @param n.boots The number of bootstraps to downsample in order to get mean diversity +#' @param exportTable Exports a table of the data into the global +#' environment in addition to the visualization +#' @param palette Colors to use in visualization - input any hcl.pals() #' #' @importFrom iNEXT iNEXT ggiNEXT #' @import ggplot2 diff --git a/R/loadContigs.R b/R/loadContigs.R index 028b4c17..d958e5ad 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -52,7 +52,7 @@ loadContigs <- function(dir, df <- checkList(dir) } - loadFunc <- switch(method, + loadFunc <- switch(format, "10X" = .parse10x, "AIRR" = .parseAIRR, "TRUST4" = .parseTRUST4, diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index d4c2e18b..e2cd4130 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -6,7 +6,7 @@ \usage{ clonalProportion( df, - split = c(10, 100, 1000, 10000, 30000, 1e+05), + clonal.split = c(10, 100, 1000, 10000, 30000, 1e+05), cloneCall = "strict", chain = "both", group.by = NULL, @@ -19,7 +19,7 @@ clonalProportion( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{split}{The cutpoints for the specific clonotypes.} +\item{clonal.split}{The cut points for the specific clonotypes.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -44,10 +44,10 @@ ggplot of the space occupied by the specific rank of clonotypes \description{ This function calculates the relative clonal space occupied by the clonotypes. The grouping of these clonotypes is based on the parameter -split, at default, split will group the clonotypes into bins of 1:10, -11:100, 101:1001, etc. To adjust the clonotypes selected, change the -numbers in the variable split. If a matrix output for the data is -preferred, set exportTable = TRUE. +**clonal.split**, at default, **clonal.split** will group the clonotypes +into bins of 1:10, 11:100, 101:1001, etc. To adjust the clonotypes +selected, change the numbers in the variable split. If a matrix output +for the data is preferred, set exportTable = TRUE. } \examples{ #Making combined contig data diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index b05ee7e9..9d87eceb 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -503,9 +503,9 @@ clonalHomeostasis(combined, Like clonal space homeostasis above, clonal proportion places clones into separate bins. The key difference is that instead of looking at the relative proportion of the clone to the total, the `clonalProportion()` function will rank the clones by total number and place them into bins. -The **split** represents the ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **split** variable in the function and can be adjusted, but they are as follows at baseline. +The **clonal.split** represents the ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **clonal.split** variable in the function and can be adjusted, but they are as follows at baseline. -**split** +**clonal.split** * 10 * 100 * 1000 @@ -843,8 +843,8 @@ The nucleotide or amino acid sequences of the chains can be used to cluster clon Clonotype cluster by distance: -* TCRA:LD.1 [chain:] + :LD + [number] -* TCRA.123 [chain:] + [number] +* TCRA:LD.1 [chain:] + :LD + [number] +* TCRA.123 [chain:] + [number] LD denotes if the cluster was called using the normalized Levenshtein distance. @@ -861,7 +861,7 @@ sub_combined <- clusterTCR(combined[[2]], ## StartracDiversity -From the excellent work by Lei Zhang, et al. in [Lineage tracking reveals dynamic relationships of T cells in colorectal cancer](https://www.nature.com/articles/s41586-018-0694-x), the authors introduce new methods for looking at clonotypes by cellular origins and cluster identification. Their [startrac](https://github.com/Japrin/STARTRAC) software has been incorporated into scRepertoire and please cite their excellent work. +From the excellent work by Lei Zhang, et al. in [Lineage tracking reveals dynamic relationships of T cells in colorectal cancer](https://www.nature.com/articles/s41586-018-0694-x), the authors introduce new methods for looking at clonotypes by cellular origins and cluster identification. Their [startrac](https://github.com/Japrin/STARTRAC) software has been adapted to work with scRepertoire and please cite their excellent work. In order to use the ```StartracDiversity()``` function, you will need to include the product of the ```combinedExpression()``` function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data,**type** corresponds to the column "Type", which includes the "P" and "T" classifiers. The indices can be subsetted for a specific patient or examined overall using the **by** variable. Importantly, the function uses only the strict definition of a clonotype of the VDJC genes and the CDR3 nucleotide sequence. @@ -886,13 +886,13 @@ A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/3582969 **group.by** * The compartment/variable used for the purpose of the comparison -```{r} -clonotypeBias(scRep_example, - cloneCall = "aa", - split.by = "Patient", - group.by = "seurat_clusters", - n.boots = 20, - min.expand =1) +```{r, message = FALSE} +clonalBias(scRep_example, + cloneCall = "aa", + split.by = "Patient", + group.by = "seurat_clusters", + n.boots = 10, + min.expand =1) ``` *** @@ -900,7 +900,7 @@ clonotypeBias(scRep_example, This has been a general overview of the capabilities of scRepertoire from the initial processing and visualization to attach to the mRNA expression values in a single-cell object. If you have any questions, comments, or suggestions, please visit the GitHub repository or [email me](mailto:ncborch@gmail.com). -###Session Info +### Session Info ```{r} sessionInfo() From 8daacdf319266f0e3aae2b9b7008999d13873565 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 27 Sep 2023 11:31:46 -0500 Subject: [PATCH 062/142] clean clonalOverlay --- R/abundanceContig.R | 9 ++------- R/checkContig.R | 5 ++--- R/clonalBias.R | 2 +- R/clonalDiversity.R | 2 +- R/clonalOverlay.R | 28 ++++++++++++++++---------- R/clonalQuant.R | 9 ++------- R/clonalSizeDistribution.R | 4 ++-- R/utils.R | 41 ++++++++++++++++++++++---------------- R/vizGenes.R | 4 ++-- man/clonalOverlay.Rd | 8 ++++---- man/clonalRarefaction.Rd | 5 +++++ vignettes/vignette.Rmd | 4 ++-- 12 files changed, 64 insertions(+), 57 deletions(-) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index 9780bdb4..ddc8ea5d 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -47,14 +47,9 @@ abundanceContig <- function(df, Con.df <- NULL xlab <- "Abundance" cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - names <- names(df) - for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } + df <- .data.wrangle(df, split.by, cloneCall, chain) + names <- names(df) if (!is.null(group.by)) { for (i in seq_along(df)) { data1 <- parseContigs(df, i, names, cloneCall) diff --git a/R/checkContig.R b/R/checkContig.R index ac286728..1c4e1404 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -46,12 +46,11 @@ checkContig <- function(df, } else { genes <- c("v_gene", "d_genes", "j_gene", "c_gene") } - df <- list.input.return(df,split.by) - df <- checkBlanks(df, "CTaa") + df <- .data.wrangle(df, split.by, "CTgene", chain) + for (i in seq_along(df)) { df[[i]] <- off.the.chain(df[[i]], chain, "CTaa") df[[i]] <- off.the.chain(df[[i]], chain, "CTnt") - df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") df[[i]][df[[i]] == "NA"] <- NA } diff --git a/R/clonalBias.R b/R/clonalBias.R index 4dc252a2..5b5fbb1f 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -152,7 +152,7 @@ get_clono_bias <- function(df, } } cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) + df <- .checkBlanks(df, cloneCall) for (s in names(bg)) { diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 73787c8e..5c9161d2 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -66,7 +66,7 @@ clonalDiversity <- function(df, #group.element.uniq <- unique(df$group.element) df <- split(df, f = df[,"group.element"]) } - min <- short.check(df, cloneCall) + min <- .short.check(df, cloneCall) for (i in seq_along(df)) { data <- as.data.frame(table(df[[i]][,cloneCall])) mat_a <- NULL diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index c227949b..8eefa2f6 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -25,10 +25,10 @@ #' #' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param reduction The dimensional reduction to visualize -#' @param freq.cutpoint The overlay cutpoint to include, this corresponds to the -#' Frequency variable in the single-cell objecter +#' @param freq.cutpoint The overlay cut point to include, this corresponds to the +#' Frequency variable in the single-cell object #' @param bins The number of contours to the overlay -#' @param facet meta data variable to facet the comparison +#' @param facet.by meta data variable to facet the comparison #' #' @import ggplot2 #' @importFrom SeuratObject Embeddings @@ -41,23 +41,29 @@ clonalOverlay <- function(sc, reduction = NULL, freq.cutpoint = 30, bins = 25, - facet = NULL) { + facet.by = NULL) { checkSingleObject(sc) + + #Forming the data frame to plot tmp <- data.frame(grabMeta(sc), identity = sc@active.ident, get.coord(sc, reduction)) - if (!is.null(facet)) { - facet <- tmp[,facet] - tmp <- data.frame(facet, tmp) + #Add facet variable if present + if (!is.null(facet.by)) { + facet.by <- tmp[,facet.by] + tmp <- data.frame(facet.by, tmp) } tmp$include <- ifelse(tmp$Frequency >= freq.cutpoint, "Yes", NA) tmp2 <- subset(tmp, include == "Yes") + + #Plotting plot <- ggplot(tmp2, mapping = aes(x=tmp2[,(ncol(tmp2)-2)], y = tmp2[,(ncol(tmp2)-1)])) + geom_point(tmp, mapping = aes(x=as.numeric(tmp[,(ncol(tmp)-2)]), y = as.numeric(tmp[,(ncol(tmp)-1)]), color = tmp[,"identity"]), size= 0.5) + geom_density_2d(color = "black", lwd=0.25, bins = bins) + theme_classic() + labs(color = "Active Identity") + - xlab("Dimension 1") + ylab("Dimension 2") - if (!is.null(facet)) { - plot <- plot + facet_wrap(~facet) + xlab("Dimension 1") + + ylab("Dimension 2") + if (!is.null(facet.by)) { + plot <- plot + facet_wrap(~facet.by) } - plot + return(plot) } \ No newline at end of file diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 06c083dd..3b4a4c54 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -41,14 +41,9 @@ clonalQuant <- function(df, if (length(group.by) > 1) { stop("Only one item in the group.by variable can be listed.") } - df <- list.input.return(df, split.by) + cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) - if (chain != "both") { - for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } + df <- .data.wrangle(df, split.by, cloneCall, chain) #Set up mat to store and selecting graph parameters if (!is.null(group.by)) { diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 94a41a84..17a30ff9 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -48,9 +48,9 @@ clonalSizeDistribution <- function(df, exportTable = FALSE, palette = "inferno") { x <- xend <- yend <- mpg_div_hp <- NULL - df <- list.input.return(df, split.by) cloneCall <- theCall(cloneCall) - df <- checkBlanks(df, cloneCall) + df <- .data.wrangle(df, split.by, cloneCall, chain) + if(!is.null(group.by)) { df <- groupList(df, group.by) } diff --git a/R/utils.R b/R/utils.R index 8aa9371a..b41be379 100644 --- a/R/utils.R +++ b/R/utils.R @@ -30,7 +30,7 @@ off.the.chain <- function(dat, chain, cloneCall) { #Remove list elements that contain all NA values -checkBlanks <- function(df, cloneCall) { +.checkBlanks <- function(df, cloneCall) { count <- NULL for (i in seq_along(df)) { if (length(df[[i]][,cloneCall]) == length(which(is.na(df[[i]][,cloneCall]))) | @@ -224,8 +224,9 @@ filteringNA <- function(sc) { #Organizing list of contigs for visualization parseContigs <- function(df, i, names, cloneCall) { data <- df[[i]] - data1 <- data %>% group_by(data[,cloneCall]) %>% - summarise(Abundance=n()) + data1 <- data %>% + group_by(data[,cloneCall]) %>% + summarise(Abundance=n()) colnames(data1)[1] <- cloneCall data1$values <- names[i] return(data1) @@ -238,19 +239,22 @@ quiet <- function(x) { invisible(force(x)) } + # This is to help sort the type of clonotype data to use theCall <- function(x) { - if (x %in% c("CTnt", "CTgene", "CTaa", "CTstrict")) { - x <- x - }else if (x == "gene" | x == "genes") { - x <- "CTgene" - } else if(x == "nt" | x == "nucleotide") { - x <- "CTnt" - } else if (x == "aa" | x == "amino") { - x <- "CTaa" - } else if (x == "gene+nt" | x == "strict") { - x <- "CTstrict" - } + x <- switch(x, + "gene" = "CTgene", + "genes" = "CTgene", + "CTgene" = "CTgene", + "nt" = "CTnt", + "nucleotides" = "CTnt", + "CTnt" = "CTnt", + "aa" = "CTaa", + "amino" = "CTaa", + "CTaa" = "CTaa", + "strict" = "CTstrict", + "gene+nt" = "CTstrict", + "CTstrict" = "CTstrict") return(x) } @@ -412,7 +416,8 @@ makeGenes <- function(cellType, data2, chain1, chain2) { data2 } -short.check <- function(df, cloneCall) { +#Getting the minimum repertoire size for diversity boots +.short.check <- function(df, cloneCall) { min <- c() for (x in seq_along(df)) { min.tmp <- length(which(!is.na(unique(df[[x]][,cloneCall])))) @@ -422,7 +427,7 @@ short.check <- function(df, cloneCall) { return(min) } -select.gene <- function(df, chain, gene, label) { +.select.gene <- function(df, chain, gene, label) { if (chain %in% c("TRB", "TRD", "IGH")) { gene <- unname(c(V = 1, D = 2, J = 3, C = 4)[gene]) } else { @@ -462,6 +467,7 @@ is_df_or_list_of_df <- function(x) { } } +#Pulling meta data .expression2List <- function(sc, split.by) { if (!inherits(x=sc, what ="Seurat") & !inherits(x=sc, what ="SummarizedExperiment")) { @@ -482,9 +488,10 @@ is_df_or_list_of_df <- function(x) { return(df) } +#Making lists for single-cell object, check blanks and apply chain filter .data.wrangle <- function(df, split.by, cloneCall, chain) { df <- list.input.return(df, split.by) - df <- checkBlanks(df, cloneCall) + df <- .checkBlanks(df, cloneCall) for (i in seq_along(df)) { if (chain != "both") { df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) diff --git a/R/vizGenes.R b/R/vizGenes.R index 6caf260c..26eb854c 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -61,11 +61,11 @@ vizGenes <- function(df, if (y.axis %!in% c("V", "D", "J", "C")) { y.axis <- "element.names" } else { - df <- select.gene(df, chain, y.axis) + df <- .select.gene(df, chain, y.axis) colnames(df)[ncol(df)] <- y.axis } } - df <- select.gene(df, chain, gene) + df <- .select.gene(df, chain, gene) df <- subset(df, !is.na(df[,ncol(df)])) #remove NA values df <- subset(df, df[,ncol(df)] != "NA") #remove values that are character "NA" df <- subset(df, df[,ncol(df)] != "") #remove rows with non genes diff --git a/man/clonalOverlay.Rd b/man/clonalOverlay.Rd index b6c78fce..836da39e 100644 --- a/man/clonalOverlay.Rd +++ b/man/clonalOverlay.Rd @@ -9,7 +9,7 @@ clonalOverlay( reduction = NULL, freq.cutpoint = 30, bins = 25, - facet = NULL + facet.by = NULL ) } \arguments{ @@ -17,12 +17,12 @@ clonalOverlay( \item{reduction}{The dimensional reduction to visualize} -\item{freq.cutpoint}{The overlay cutpoint to include, this corresponds to the -Frequency variable in the single-cell objecter} +\item{freq.cutpoint}{The overlay cut point to include, this corresponds to the +Frequency variable in the single-cell object} \item{bins}{The number of contours to the overlay} -\item{facet}{meta data variable to facet the comparison} +\item{facet.by}{meta data variable to facet the comparison} } \value{ ggplot object diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd index f0f36256..2b968e75 100644 --- a/man/clonalRarefaction.Rd +++ b/man/clonalRarefaction.Rd @@ -39,6 +39,11 @@ sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapol \item{hill.numbers}{The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson)} \item{n.boots}{The number of bootstraps to downsample in order to get mean diversity} + +\item{exportTable}{Exports a table of the data into the global +environment in addition to the visualization} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} } \description{ This functions uses the Hill numbers of order q: species richness (q = 0), diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 9d87eceb..d2ff193c 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -692,14 +692,14 @@ Seurat::DimPlot(scRep_example, group.by = "cloneType") + ## clonalOverlay -Using the dimensional reduction graphs as a reference, we can also generate an overlay of the position of clonally expanded cells using `clonalOverlay()`. Select the **reduction** for the visualization, default is "PCA" and the **freq.cutpoint** or lowest clonal frequency or proportion to generate the contour plot. We can modify the contours by selecting the number of **bins** or the number of contours drawn. `clonalOverlay()` can be used to look across all cells or faceted by a meta data variable using **facet**. The overall dimensional reduction will be maintained as we facet, while the contour plots will adjust based on the **facet** variable. The coloring of the dot plot is based on the active identity of the single-cell object. This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs). +Using the dimensional reduction graphs as a reference, we can also generate an overlay of the position of clonally expanded cells using `clonalOverlay()`. Select the **reduction** for the visualization, default is "PCA" and the **freq.cutpoint** or lowest clonal frequency or proportion to generate the contour plot. We can modify the contours by selecting the number of **bins** or the number of contours drawn. `clonalOverlay()` can be used to look across all cells or faceted by a meta data variable using **facet.by**. The overall dimensional reduction will be maintained as we facet, while the contour plots will adjust based on the **facet.by** variable. The coloring of the dot plot is based on the active identity of the single-cell object. This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs), which is a great pipeline to annotated T cell subtypes. ```{r} clonalOverlay(scRep_example, reduction = "umap", freq.cutpoint = 1, bins = 10, - facet = "Patient") + + facet.by = "Patient") + guides(color = "none") ``` From 62bd552c807ac0d4d71f6b52355e9395e32e84fe Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 28 Sep 2023 07:02:05 -0500 Subject: [PATCH 063/142] converting utils internal functions to dot --- R/abundanceContig.R | 6 ++-- R/alluvialClonotypes.R | 8 +++--- R/checkContig.R | 4 +-- R/clonalBias.R | 8 +++--- R/clonalCompare.R | 4 +-- R/clonalDiversity.R | 4 +-- R/clonalHomeostasis.R | 4 +-- R/clonalLength.R | 4 +-- R/clonalNetwork.R | 10 +++---- R/clonalOverlap.R | 6 ++-- R/clonalOverlay.R | 4 +-- R/clonalProportion.R | 4 +-- R/clonalQuant.R | 2 +- R/clonalRarefaction.R | 2 +- R/clonalScatter.R | 7 +---- R/clonalSizeDistribution.R | 7 ++--- R/clusterTCR.R | 2 +- R/combineContigs.R | 32 ++++++++++----------- R/combineExpression.R | 10 +++---- R/createHTOContigList.R | 4 +-- R/getCircilize.R | 4 +-- R/highlightClones.R | 4 +-- R/loadContigs.R | 2 +- R/occupiedscRepertoire.R | 4 +-- R/startracDiversity.R | 10 +++---- R/utils.R | 57 +++++++++++++++++--------------------- R/vizGenes.R | 4 +-- vignettes/vignette.Rmd | 8 +++--- 28 files changed, 106 insertions(+), 119 deletions(-) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index ddc8ea5d..db106a72 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -46,13 +46,13 @@ abundanceContig <- function(df, df <- list.input.return(df,split.by) Con.df <- NULL xlab <- "Abundance" - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) names <- names(df) if (!is.null(group.by)) { for (i in seq_along(df)) { - data1 <- parseContigs(df, i, names, cloneCall) + data1 <- .parseContigs(df, i, names, cloneCall) label <- df[[i]][1,group.by] data1[,paste(group.by)] <- label Con.df<- rbind.data.frame(Con.df, data1) } @@ -76,7 +76,7 @@ abundanceContig <- function(df, } } else { for (i in seq_along(df)) { - data1 <- parseContigs(df, i, names, cloneCall) + data1 <- .parseContigs(df, i, names, cloneCall) Con.df<- rbind.data.frame(Con.df, data1) } Con.df <- data.frame(Con.df) diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index be04d025..f49dc32a 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -82,14 +82,14 @@ alluvialClonotypes <- function(sc, facet = NULL, palette = "inferno") { x <- alluvium <- stratum <- NULL - checkSingleObject(sc) - cloneCall <- theCall(cloneCall) + .checkSingleObject(sc) + cloneCall <- .theCall(cloneCall) if (length(y.axes) == 0) { stop("Make sure you have selected the variable(s) to visualize") } - meta <- grabMeta(sc) + meta <- .grabMeta(sc) if (chain != "both") { - meta <- off.the.chain(meta, chain, cloneCall) + meta <- .off.the.chain(meta, chain, cloneCall) } meta$barcodes <- rownames(meta) check <- colnames(meta) == color diff --git a/R/checkContig.R b/R/checkContig.R index 1c4e1404..4721bd55 100644 --- a/R/checkContig.R +++ b/R/checkContig.R @@ -49,8 +49,8 @@ checkContig <- function(df, df <- .data.wrangle(df, split.by, "CTgene", chain) for (i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, "CTaa") - df[[i]] <- off.the.chain(df[[i]], chain, "CTnt") + df[[i]] <- .off.the.chain(df[[i]], chain, "CTaa") + df[[i]] <- .off.the.chain(df[[i]], chain, "CTnt") df[[i]][df[[i]] == "NA"] <- NA } diff --git a/R/clonalBias.R b/R/clonalBias.R index 5b5fbb1f..ebf8b3e0 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -52,7 +52,7 @@ clonalBias <- function(df, n.boots = 20, min.expand=10, exportTable = FALSE) { - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) bias <- get_clono_bias(df, split.by = split.by, @@ -88,7 +88,7 @@ clonalBias <- function(df, plot <- ggplot(bias, aes(x=ncells,y=bias)) + geom_point(aes(colour=Top_state)) + - quiet(stat_quantile(data=df_shuffle, quantiles = c(corrected_p), method = "rqss", lambda=3)) + + .quiet(stat_quantile(data=df_shuffle, quantiles = c(corrected_p), method = "rqss", lambda=3)) + theme_classic() + xlab("Clone Size") + ylab("Clonotype Bias") @@ -148,10 +148,10 @@ get_clono_bias <- function(df, df <- list.input.return(df, split.by) } else { if (inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { - df <- list("Object" = grabMeta(df)) + df <- list("Object" = .grabMeta(df)) } } - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .checkBlanks(df, cloneCall) for (s in names(bg)) { diff --git a/R/clonalCompare.R b/R/clonalCompare.R index 1d23328d..b000eab0 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -57,14 +57,14 @@ clonalCompare <- function(df, if(!is.null(top.clones) & !is.null(clonotypes)) { top.clones <- NULL } - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) Con.df <- NULL #Loop through the list to get a proportional summary for (i in seq_along(df)) { if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) } tbl <- as.data.frame(table(df[[i]][,cloneCall])) tbl[,2] <- tbl[,2]/sum(tbl[,2]) diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 5c9161d2..b88197ab 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -56,7 +56,7 @@ clonalDiversity <- function(df, if(return.boots) { exportTable <- TRUE } - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) mat <- NULL sample <- c() @@ -117,7 +117,7 @@ clonalDiversity <- function(df, mat_melt <- suppressMessages(melt(mat, id.vars = c(group.by, x.axis))) values <- str_sort(as.character(unique(mat_melt[,group.by])), numeric = TRUE) - values <- quiet(dput(values)) + values <- .quiet(dput(values)) mat_melt[,group.by] <- factor(mat_melt[,group.by], levels = values) if (x.axis == "x.axis") { plot <- ggplot(mat_melt, aes(x=1, y=as.numeric(value))) diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index ad36538e..356a764e 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -45,11 +45,11 @@ clonalHomeostasis <- function(df, palette = "inferno") { cloneTypes <- c(None = 0, cloneTypes) - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) if(!is.null(group.by)) { - df <- groupList(df, group.by) + df <- .groupList(df, group.by) } #Generating data matrix to store value diff --git a/R/clonalLength.R b/R/clonalLength.R index d2a9e315..b29bc9b0 100644 --- a/R/clonalLength.R +++ b/R/clonalLength.R @@ -45,7 +45,7 @@ clonalLength <- function(df, exportTable = FALSE, palette = "inferno") { - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- list.input.return(df, split.by) #Sorting out graphing parameters @@ -69,7 +69,7 @@ clonalLength <- function(df, #Calculating Length Con.df <- NULL - Con.df <- lengthDF(df, cloneCall, chain, group.by, c1, c2) + Con.df <- .lengthDF(df, cloneCall, chain, group.by, c1, c2) if(is.null(group.by) & order) { Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index 1079b369..6f02b969 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -67,13 +67,13 @@ clonalNetwork <- function(sc, palette = "inferno", exportClones = FALSE) { to <- from <- weight <- y <- NULL - cloneCall <- theCall(cloneCall) - meta <- grabMeta(sc) - coord <- data.frame(get.coord(sc, reduction), identity = meta[,identity]) + cloneCall <- .theCall(cloneCall) + meta <- .grabMeta(sc) + coord <- data.frame(.get.coord(sc, reduction), identity = meta[,identity]) min <- c() if (!is.null(filter.clones)) { if(filter.clones == "min") { - meta <- grabMeta(sc) + meta <- .grabMeta(sc) id.meta <- split(meta, meta[,identity]) for (x in seq_along(id.meta)) { min.tmp <- length(which(!is.na(unique(id.meta[[x]][,cloneCall])))) @@ -100,7 +100,7 @@ clonalNetwork <- function(sc, clones.to.filter <- table[seq_len(cut),1] } } - meta <- grabMeta(sc) + meta <- .grabMeta(sc) if (!is.null(filter.clones)) { meta <- meta[meta[,cloneCall] %in% clones.to.filter,] } diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index e7cb8d5d..0e078e30 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -44,18 +44,18 @@ clonalOverlap <- function(df, } else { return_type = "unique" } - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) df <- df[order(names(df))] values <- str_sort(as.character(unique(names(df))), numeric = TRUE) - df <- df[quiet(dput(values))] + df <- df[.quiet(dput(values))] num_samples <- length(df[]) names_samples <- names(df) length <- seq_len(num_samples) if (chain != "both") { for (i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) } } diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index 8eefa2f6..c1333b87 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -42,10 +42,10 @@ clonalOverlay <- function(sc, freq.cutpoint = 30, bins = 25, facet.by = NULL) { - checkSingleObject(sc) + .checkSingleObject(sc) #Forming the data frame to plot - tmp <- data.frame(grabMeta(sc), identity = sc@active.ident, get.coord(sc, reduction)) + tmp <- data.frame(.grabMeta(sc), identity = sc@active.ident, .get.coord(sc, reduction)) #Add facet variable if present if (!is.null(facet.by)) { facet.by <- tmp[,facet.by] diff --git a/R/clonalProportion.R b/R/clonalProportion.R index f9159e30..4b08ff49 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -45,10 +45,10 @@ clonalProportion <- function(df, exportTable = FALSE, palette = "inferno") { Con.df <- NULL - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) if(!is.null(group.by)) { - df <- groupList(df, group.by) + df <- .groupList(df, group.by) } #Generating data matrix to store value diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 3b4a4c54..2b55b5b0 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -42,7 +42,7 @@ clonalQuant <- function(df, stop("Only one item in the group.by variable can be listed.") } - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) #Set up mat to store and selecting graph parameters diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index d40981d4..ff36e999 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -47,7 +47,7 @@ clonalRarefaction <- function(df, n.boots = 20, exportTable = FALSE, palette = "inferno") { - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) mat.list <- lapply(df, function(x) { table(x[,cloneCall]) diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 45a35a33..2b38f1e1 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -52,14 +52,9 @@ clonalScatter <- function(df, if (!is.null(seed)) { set.seed(seed) } - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) - if (chain != "both") { - for (i in axes) { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } - } #Making new data frame and adding variables to graph x.df <- as.data.frame(table(df[[x.axis]][,cloneCall])) diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 17a30ff9..a63c2c21 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -48,11 +48,11 @@ clonalSizeDistribution <- function(df, exportTable = FALSE, palette = "inferno") { x <- xend <- yend <- mpg_div_hp <- NULL - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) if(!is.null(group.by)) { - df <- groupList(df, group.by) + df <- .groupList(df, group.by) } data <- bind_rows(df) unique_df <- unique(data[,cloneCall]) @@ -60,9 +60,6 @@ clonalSizeDistribution <- function(df, Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) colnames(Con.df)[1] <- "clonotype" for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) - } data <- df[[i]] data <- data.frame(table(data[,cloneCall]), stringsAsFactors = FALSE) diff --git a/R/clusterTCR.R b/R/clusterTCR.R index 6d0aa38d..058de07f 100644 --- a/R/clusterTCR.R +++ b/R/clusterTCR.R @@ -50,7 +50,7 @@ clusterTCR <- function(df, dat[[y]]$cdr3_nt2 <- str_split(dat[[y]]$CTnt, "_", simplify = TRUE)[,2] } } - dat <- checkList(dat) + dat <- .checkList(dat) if(chain %in% c("TRA", "TRG")) { ref <- 1 } else if(chain %in% c("TRB", "TRD")) { diff --git a/R/combineContigs.R b/R/combineContigs.R index 49e7d864..57767abd 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -56,7 +56,7 @@ combineTCR <- function(df, removeNA = FALSE, removeMulti = FALSE, filterMulti = FALSE) { - df <- checkList(df) + df <- .checkList(df) df <- checkContigs(df) out <- NULL final <- NULL @@ -70,7 +70,7 @@ combineTCR <- function(df, df[[i]]$sample <- samples[i] df[[i]]$ID <- ID[i] if (filterMulti) { - df[[i]] <- filteringMulti(df[[i]]) + df[[i]] <- .filteringMulti(df[[i]]) } } #Prevents error caused by list containing elements with 0 rows @@ -85,19 +85,19 @@ combineTCR <- function(df, } } if (!is.null(samples)) { - out <- modifyBarcodes(df, samples, ID) + out <- .modifyBarcodes(df, samples, ID) } else { out <- df } for (i in seq_along(out)) { # ideally the nested code could be in a function for a better development/testing experience data2 <- out[[i]] - data2 <- makeGenes(cellType = "T", data2) + data2 <- .makeGenes(cellType = "T", data2) unique_df <- unique(data2$barcode) # could potentially display % here Con.df <- data.frame(matrix(NA, length(unique_df), 7)) colnames(Con.df) <- c("barcode",tcr1_lines, tcr2_lines) Con.df$barcode <- unique_df - Con.df <- parseTCR(Con.df, unique_df, data2) - Con.df <- assignCT(cellType = "T", Con.df) + Con.df <- .parseTCR(Con.df, unique_df, data2) + Con.df <- .assignCT(cellType = "T", Con.df) Con.df[Con.df == "NA_NA" | Con.df == "NA_NA_NA_NA"] <- NA data3 <- merge(data2[,-which(names(data2) %in% c("TCR1","TCR2"))], Con.df, by = "barcode") @@ -124,8 +124,8 @@ combineTCR <- function(df, final[[i]]<-final[[i]][!duplicated(final[[i]]$barcode),] final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ] } - if (removeNA) { final <- removingNA(final)} - if (removeMulti) { final <- removingMulti(final) } + if (removeNA) { final <- .removingNA(final)} + if (removeMulti) { final <- .removingMulti(final) } final } @@ -173,7 +173,7 @@ combineBCR <- function(df, removeNA = FALSE, removeMulti = FALSE, filterMulti = TRUE) { - df <- checkList(df) + df <- .checkList(df) df <- checkContigs(df) out <- NULL final <- list() @@ -189,26 +189,26 @@ combineBCR <- function(df, df[[i]]$save_chain <- df[[i]]$chain # Collapse IGK and IGL chains df[[i]]$chain <- ifelse(df[[i]]$chain=="IGH","IGH","IGLC") - df[[i]] <- filteringMulti(df[[i]]) + df[[i]] <- .filteringMulti(df[[i]]) # Get back IGK / IGL distinction df[[i]]$chain <- df[[i]]$save_chain df[[i]]$save_chain <- NULL } } if (!is.null(samples)) { - out <- modifyBarcodes(df, samples, ID) + out <- .modifyBarcodes(df, samples, ID) } else { out <- df } for (i in seq_along(out)) { data2 <- data.frame(out[[i]]) - data2 <- makeGenes(cellType = "B", data2) + data2 <- .makeGenes(cellType = "B", data2) unique_df <- unique(data2$barcode) Con.df <- data.frame(matrix(NA, length(unique_df), 9)) colnames(Con.df) <- c("barcode", heavy_lines, light_lines) Con.df$barcode <- unique_df - Con.df <- parseBCR(Con.df, unique_df, data2) - Con.df <- assignCT(cellType = "B", Con.df) + Con.df <- .parseBCR(Con.df, unique_df, data2) + Con.df <- .assignCT(cellType = "B", Con.df) data3<-Con.df %>% mutate(length1 = nchar(cdr3_nt1)) %>% mutate(length2 = nchar(cdr3_nt2)) final[[i]] <- data3 @@ -253,8 +253,8 @@ combineBCR <- function(df, for (i in seq_along(final)) { final[[i]] <- final[[i]][!duplicated(final[[i]]$barcode),] final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ]} - if (removeNA) { final <- removingNA(final) } - if (removeMulti) { final <- removingMulti(final) } + if (removeNA) { final <- .removingNA(final) } + if (removeMulti) { final <- .removingMulti(final) } return(final) } diff --git a/R/combineExpression.R b/R/combineExpression.R index 9dfcb2e4..68f86e6d 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -68,15 +68,15 @@ combineExpression <- function( options( dplyr.summarise.inform = FALSE ) cloneTypes <- c(None = 0, cloneTypes) - df <- checkList(df) - cloneCall <- theCall(cloneCall) + df <- .checkList(df) + cloneCall <- .theCall(cloneCall) Con.df <- NULL - meta <- grabMeta(sc) + meta <- .grabMeta(sc) cell.names <- rownames(meta) if (group.by == "none" | !is.null(group.by)) { for (i in seq_along(df)) { if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) } data <- data.frame(df[[i]], stringsAsFactors = FALSE) data2 <- unique(data[,c("barcode", cloneCall)]) @@ -170,7 +170,7 @@ combineExpression <- function( colData(sc) <- cbind(colData(sc), PreMeta[rownames,])[, union(colnames(colData(sc)), colnames(PreMeta))] rownames(colData(sc)) <- rownames } - if (filterNA) { sc <- filteringNA(sc) } + if (filterNA) { sc <- .filteringNA(sc) } sc$cloneType <- factor(sc$cloneType, levels = rev(names(cloneTypes))) if(is_seurat_object(sc)) { diff --git a/R/createHTOContigList.R b/R/createHTOContigList.R index 507bfd0c..db38cd7d 100644 --- a/R/createHTOContigList.R +++ b/R/createHTOContigList.R @@ -33,8 +33,8 @@ createHTOContigList <- function(contig, group.by = NULL, multi.run = NULL){ contig.list <- NULL - checkSingleObject(sc) - meta <- grabMeta(sc) + .checkSingleObject(sc) + meta <- .grabMeta(sc) if (length(group.by) > 1) { meta["group.by"] <- apply(meta[ , group.by] , 1 , paste , collapse = "." ) } else { diff --git a/R/getCircilize.R b/R/getCircilize.R index 8dc0a400..db1059bd 100644 --- a/R/getCircilize.R +++ b/R/getCircilize.R @@ -36,8 +36,8 @@ #' @author Dillon Corvino, Nick Borcherding getCirclize <- function(sc, cloneCall = "strict", group.by = NULL, proportion = FALSE) { - meta <- grabMeta(sc) - cloneCall <- theCall(cloneCall) + meta <- .grabMeta(sc) + cloneCall <- .theCall(cloneCall) test <- meta[, c(cloneCall, group.by)] test <- test[!is.na(test[,cloneCall]),] dTest <- suppressMessages(dcast(test, test[,cloneCall] ~ test[,group.by])) diff --git a/R/highlightClones.R b/R/highlightClones.R index 1ce456ef..625e15d9 100644 --- a/R/highlightClones.R +++ b/R/highlightClones.R @@ -33,8 +33,8 @@ highlightClones <- function(sc, if (!inherits(x=sc, what ="Seurat")) { stop("Object indicated is not of class 'Seurat', make sure you are using the correct data.") } - cloneCall <- theCall(cloneCall) - meta <- grabMeta(sc) + cloneCall <- .theCall(cloneCall) + meta <- .grabMeta(sc) meta$highlight <- NA for(i in seq_along(sequence)) { meta$highlight <- ifelse(meta[,cloneCall] == sequence[i], diff --git a/R/loadContigs.R b/R/loadContigs.R index d958e5ad..7dead48c 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -49,7 +49,7 @@ loadContigs <- function(dir, } #Already loaded list or data frame } else if (inherits(x=dir, what ="list") | inherits(x=dir, what ="data.frame")) { - df <- checkList(dir) + df <- .checkList(dir) } loadFunc <- switch(format, diff --git a/R/occupiedscRepertoire.R b/R/occupiedscRepertoire.R index 54fa2350..39e641df 100644 --- a/R/occupiedscRepertoire.R +++ b/R/occupiedscRepertoire.R @@ -47,8 +47,8 @@ occupiedscRepertoire <- function(sc, na.include = FALSE, exportTable = FALSE, palette = "inferno") { - checkSingleObject(sc) - meta <- grabMeta(sc) + .checkSingleObject(sc) + meta <- .grabMeta(sc) meta <- melt(table(meta[!is.na(meta$Frequency), c(x.axis, facet.by, "cloneType")], useNA = "ifany")) if (!na.include) { diff --git a/R/startracDiversity.R b/R/startracDiversity.R index 08b485c5..e5adc19b 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -52,8 +52,8 @@ StartracDiversity <- function(sc, exportTable = FALSE, palette = "inferno") { majorCluster <- NULL - cloneCall <- theCall(cloneCall) - df <- grabMeta(sc) + cloneCall <- .theCall(cloneCall) + df <- .grabMeta(sc) barcodes <- rownames(df) colnames(df)[ncol(df)] <- "majorCluster" @@ -65,9 +65,9 @@ StartracDiversity <- function(sc, } group.levels = unique(df[,group.by]) - cloneCall <- theCall(cloneCall) + cloneCall <- .theCall(cloneCall) if (chain != "both") { - df <- off.the.chain(df, chain, cloneCall) + df <- .off.the.chain(df, chain, cloneCall) } df <- df %>% @@ -102,7 +102,7 @@ StartracDiversity <- function(sc, mat_melt <- melt(mat, id = c("group", "majorCluster")) values <- as.character(unique(mat_melt$majorCluster)) - values <- quiet(dput(values)) + values <- .quiet(dput(values)) mat_melt$majorCluster <- factor(mat_melt$majorCluster, levels = values) mat_melt$value <- as.numeric(mat_melt$value) col <- length(unique(mat_melt[,"majorCluster"])) diff --git a/R/utils.R b/R/utils.R index b41be379..8811c8bb 100644 --- a/R/utils.R +++ b/R/utils.R @@ -7,7 +7,7 @@ is_seurat_or_se_object <- function(obj) { } #Use to shuffle between chains -off.the.chain <- function(dat, chain, cloneCall) { +.off.the.chain <- function(dat, chain, cloneCall) { chain1 <- toupper(chain) #to just make it easier if (chain1 %in% c("TRA", "TRG", "IGH")) { x <- 1 @@ -19,10 +19,11 @@ off.the.chain <- function(dat, chain, cloneCall) { dat[,cloneCall] <- str_split(dat[,cloneCall], "_", simplify = TRUE)[,x] return(dat) } + +#Pulling a color palette for visualizations #' @importFrom grDevices hcl.colors .colorizer <- function(palette = "inferno", n= NULL) { - colors <- hcl.colors(n=n, palette = palette, fixup = TRUE) return(colors) } @@ -47,14 +48,14 @@ off.the.chain <- function(dat, chain, cloneCall) { return(df) } -groupList <- function(df, group.by) { +.groupList <- function(df, group.by) { df <- bind_rows(df) df <- split(df, df[,group.by]) return(df) } # Ensure df is in list format -checkList <- function(df) { +.checkList <- function(df) { df <- tryCatch( { if (is(df)[1] != "list") { @@ -71,11 +72,6 @@ checkList <- function(df) { df } -#checkList <- function(df) { -# df <- if(is(df)[1] != "list") list(df) else df -# return(df) -#} - checkContigs <- function(df) { df <- lapply(seq_len(length(df)), function(x) { df[[x]] <- if(!is.data.frame(df[[x]])) as.data.frame(df[[x]]) else df[[x]] @@ -88,7 +84,7 @@ checkContigs <- function(df) { #' @importFrom dplyr bind_rows bound.input.return <- function(df) { if (is_seurat_or_se_object(df)) { - return(grabMeta(df)) + return(.grabMeta(df)) } bind_rows(df, .id = "element.names") } @@ -105,7 +101,7 @@ list.input.return <- function(df, split.by) { #Get UMAP or other coordinates #' @importFrom SingleCellExperiment reducedDim -get.coord <- function(sc, reduction) { +.get.coord <- function(sc, reduction) { if (is.null(reduction)) { reduction <- "pca" } @@ -118,7 +114,7 @@ get.coord <- function(sc, reduction) { } #This is to check the single-cell expression object -checkSingleObject <- function(sc) { +.checkSingleObject <- function(sc) { if (!is_seurat_or_se_object(sc)){ stop("Object indicated is not of class 'Seurat' or 'SummarizedExperiment', make sure you are using @@ -130,7 +126,7 @@ checkSingleObject <- function(sc) { #' @importFrom SingleCellExperiment colData #' @importFrom SeuratObject Idents #' @importFrom methods slot -grabMeta <- function(sc) { +.grabMeta <- function(sc) { if (is_seurat_object(sc)) { meta <- data.frame(sc[[]], slot(sc, "active.ident")) colnames(meta)[length(meta)] <- "ident" @@ -145,7 +141,7 @@ grabMeta <- function(sc) { } #This is to add the sample and ID prefixes for combineBCR()/TCR() -modifyBarcodes <- function(df, samples, ID) { +.modifyBarcodes <- function(df, samples, ID) { out <- NULL for (x in seq_along(df)) { data <- df[[x]] @@ -159,14 +155,14 @@ modifyBarcodes <- function(df, samples, ID) { } #Removing barcodes with NA recovered -removingNA <- function(final) { +.removingNA <- function(final) { for(i in seq_along(final)) { final[[i]] <- na.omit(final[[i]])} return(final) } #Removing barcodes with > 2 clones recovered -removingMulti <- function(final){ +.removingMulti <- function(final){ for(i in seq_along(final)) { final[[i]] <- filter(final[[i]], !grepl(";",CTnt))} return(final) @@ -174,8 +170,7 @@ removingMulti <- function(final){ #Removing extra clonotypes in barcodes with > 2 productive contigs #' @import dplyr - -filteringMulti <- function(x) { +.filteringMulti <- function(x) { x <- x %>% group_by(barcode, chain) %>% slice_max(n = 1, order_by = reads) @@ -201,8 +196,8 @@ filteringMulti <- function(x) { #Filtering NA contigs out of single-cell expression object #' @import dplyr #' @importFrom SingleCellExperiment colData -filteringNA <- function(sc) { - meta <- grabMeta(sc) +.filteringNA <- function(sc) { + meta <- .grabMeta(sc) evalNA <- data.frame(meta[,"cloneType"]) colnames(evalNA) <- "indicator" evalNA <- evalNA %>% @@ -222,7 +217,7 @@ filteringNA <- function(sc) { #Organizing list of contigs for visualization -parseContigs <- function(df, i, names, cloneCall) { +.parseContigs <- function(df, i, names, cloneCall) { data <- df[[i]] data1 <- data %>% group_by(data[,cloneCall]) %>% @@ -233,7 +228,7 @@ parseContigs <- function(df, i, names, cloneCall) { } # This suppressing outputs for using dput() -quiet <- function(x) { +.quiet <- function(x) { sink(tempfile()) on.exit(sink()) invisible(force(x)) @@ -241,7 +236,7 @@ quiet <- function(x) { # This is to help sort the type of clonotype data to use -theCall <- function(x) { +.theCall <- function(x) { x <- switch(x, "gene" = "CTgene", "genes" = "CTgene", @@ -260,7 +255,7 @@ theCall <- function(x) { # Assigning positions for TCR contig data #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding -parseTCR <- function(Con.df, unique_df, data2) { +.parseTCR <- function(Con.df, unique_df, data2) { data2 <- data2 %>% arrange(., chain, cdr3_nt) for (y in seq_along(unique_df)){ barcode.i <- Con.df$barcode[y] @@ -297,7 +292,7 @@ parseTCR <- function(Con.df, unique_df, data2) { #Assigning positions for BCR contig data #Now assumes lambda over kappa in the context of only 2 light chains #' @author Gloria Kraus, Nick Bormann, Nick Borcherding -parseBCR <- function (Con.df, unique_df, data2) { +.parseBCR <- function (Con.df, unique_df, data2) { for (y in seq_along(unique_df)) { barcode.i <- Con.df$barcode[y] location.i <- which(barcode.i == data2$barcode) @@ -336,7 +331,7 @@ parseBCR <- function (Con.df, unique_df, data2) { #Producing a data frame to visualize for lengthContig() -lengthDF <- function(df, cloneCall, chain, group, c1, c2){ +.lengthDF <- function(df, cloneCall, chain, group, c1, c2){ Con.df <- NULL names <- names(df) if (identical(chain, "both")) { @@ -354,7 +349,7 @@ lengthDF <- function(df, cloneCall, chain, group, c1, c2){ Con.df<- rbind.data.frame(Con.df, data) }} } else { for (x in seq_along(df)) { - df[[x]] <- off.the.chain(df[[x]], chain, cloneCall) + df[[x]] <- .off.the.chain(df[[x]], chain, cloneCall) strings <- df[[x]][,cloneCall] val1 <- strings for (i in seq_along(val1)) { @@ -377,7 +372,7 @@ lengthDF <- function(df, cloneCall, chain, group, c1, c2){ } #General combination of nucleotide, aa, and gene sequences for T/B cells -assignCT <- function(cellType, Con.df) { +.assignCT <- function(cellType, Con.df) { if (cellType == "T") { Con.df$CTgene <- paste(Con.df$TCR1, Con.df$TCR2, sep="_") Con.df$CTnt <- paste(Con.df$cdr3_nt1, Con.df$cdr3_nt2, sep="_") @@ -396,7 +391,7 @@ assignCT <- function(cellType, Con.df) { #Sorting the V/D/J/C gene sequences for T and B cells #' @importFrom stringr str_c str_replace_na #' @importFrom dplyr bind_rows -makeGenes <- function(cellType, data2, chain1, chain2) { +.makeGenes <- function(cellType, data2, chain1, chain2) { if(cellType %in% c("T")) { data2 <- data2 %>% mutate(TCR1 = ifelse(chain %in% c("TRA", "TRG"), @@ -473,7 +468,7 @@ is_df_or_list_of_df <- function(x) { !inherits(x=sc, what ="SummarizedExperiment")) { stop("Use a Seurat or SCE object to convert into a list") } - meta <- grabMeta(sc) + meta <- .grabMeta(sc) if(is.null(split.by)){ split.by <- "cluster" } @@ -494,7 +489,7 @@ is_df_or_list_of_df <- function(x) { df <- .checkBlanks(df, cloneCall) for (i in seq_along(df)) { if (chain != "both") { - df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) } } return(df) diff --git a/R/vizGenes.R b/R/vizGenes.R index 26eb854c..711de0cd 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -51,10 +51,10 @@ vizGenes <- function(df, element.names <- NULL df <- list.input.return(df, split.by = split.by) if(!is.null(group.by)) { - df <- groupList(df, group.by) + df <- .groupList(df, group.by) } for(i in seq_along(df)) { - df[[i]] <- off.the.chain(df[[i]], chain, "CTgene") + df[[i]] <- .off.the.chain(df[[i]], chain, "CTgene") } df <- bound.input.return(df) if (y.axis %!in% colnames(df) | is.null(y.axis)) { diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index d2ff193c..b75ad385 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -577,18 +577,18 @@ We can also use Hill numbers to estimate the rarefaction using the the abundance + 1 - Shannon + 2 - Simpson -```{r} +```{r, message=FALSE} clonalRarefaction(combined, plot.type = 1, - n.boots = 1) + n.boots = 2) clonalRarefaction(combined, plot.type = 2, - n.boots = 1) + n.boots = 2) clonalRarefaction(combined, plot.type = 3, - n.boots = 1) + n.boots = 2) ``` ## clonalScatter From 9097b6233be56229335de925f203ff069a5a1527 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 28 Sep 2023 11:04:48 -0500 Subject: [PATCH 064/142] final scrub of .utils --- R/abundanceContig.R | 2 +- R/clonalBias.R | 2 +- R/clonalLength.R | 2 +- R/clusterTCR.R | 2 +- R/combineContigs.R | 6 ++---- R/utils.R | 12 ++++++------ R/vizGenes.R | 4 ++-- 7 files changed, 14 insertions(+), 16 deletions(-) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index db106a72..ec953f23 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -43,7 +43,7 @@ abundanceContig <- function(df, order = TRUE, exportTable = FALSE, palette = "inferno") { - df <- list.input.return(df,split.by) + df <- .list.input.return(df,split.by) Con.df <- NULL xlab <- "Abundance" cloneCall <- .theCall(cloneCall) diff --git a/R/clonalBias.R b/R/clonalBias.R index ebf8b3e0..be530314 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -145,7 +145,7 @@ get_clono_bias <- function(df, group.by = group.by, cloneCall = cloneCall) if (!is.null(split.by)) { - df <- list.input.return(df, split.by) + df <- .list.input.return(df, split.by) } else { if (inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { df <- list("Object" = .grabMeta(df)) diff --git a/R/clonalLength.R b/R/clonalLength.R index b29bc9b0..1c460af4 100644 --- a/R/clonalLength.R +++ b/R/clonalLength.R @@ -46,7 +46,7 @@ clonalLength <- function(df, palette = "inferno") { cloneCall <- .theCall(cloneCall) - df <- list.input.return(df, split.by) + df <- .list.input.return(df, split.by) #Sorting out graphing parameters xlab <- "Length" diff --git a/R/clusterTCR.R b/R/clusterTCR.R index 058de07f..e858032b 100644 --- a/R/clusterTCR.R +++ b/R/clusterTCR.R @@ -40,7 +40,7 @@ clusterTCR <- function(df, threshold = 0.85, group.by = NULL) { output.list <- list() - dat <- list.input.return(df, group.by) + dat <- .list.input.return(df, group.by) if (inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { for(y in seq_along(dat)) { diff --git a/R/combineContigs.R b/R/combineContigs.R index 57767abd..592b0598 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -57,7 +57,7 @@ combineTCR <- function(df, removeMulti = FALSE, filterMulti = FALSE) { df <- .checkList(df) - df <- checkContigs(df) + df <- .checkContigs(df) out <- NULL final <- NULL for (i in seq_along(df)) { @@ -174,15 +174,13 @@ combineBCR <- function(df, removeMulti = FALSE, filterMulti = TRUE) { df <- .checkList(df) - df <- checkContigs(df) + df <- .checkContigs(df) out <- NULL final <- list() chain1 <- "heavy" chain2 <- "light" for (i in seq_along(df)) { df[[i]] <- subset(df[[i]], chain %in% c("IGH", "IGK", "IGL")) - #df[[i]] <- df[[i]] %>% group_by(barcode,chain) %>% slice_max(n=1,order_by=reads, with_ties = FALSE) - df[[i]]$sample <- samples[i] df[[i]]$ID <- ID[i] if (filterMulti) { # Keep IGH / IGK / IGL info in save_chain diff --git a/R/utils.R b/R/utils.R index 8811c8bb..12b9c946 100644 --- a/R/utils.R +++ b/R/utils.R @@ -48,6 +48,7 @@ is_seurat_or_se_object <- function(obj) { return(df) } +#reshuffling df .groupList <- function(df, group.by) { df <- bind_rows(df) df <- split(df, df[,group.by]) @@ -72,7 +73,7 @@ is_seurat_or_se_object <- function(obj) { df } -checkContigs <- function(df) { +.checkContigs <- function(df) { df <- lapply(seq_len(length(df)), function(x) { df[[x]] <- if(!is.data.frame(df[[x]])) as.data.frame(df[[x]]) else df[[x]] df[[x]][df[[x]] == ""] <- NA @@ -82,14 +83,14 @@ checkContigs <- function(df) { } #' @importFrom dplyr bind_rows -bound.input.return <- function(df) { +.bound.input.return <- function(df) { if (is_seurat_or_se_object(df)) { return(.grabMeta(df)) } bind_rows(df, .id = "element.names") } -list.input.return <- function(df, split.by) { +.list.input.return <- function(df, split.by) { if (is_seurat_or_se_object(df)) { if(is.null(split.by)){ split.by <- "cluster" @@ -214,8 +215,6 @@ list.input.return <- function(df, split.by) { } } - - #Organizing list of contigs for visualization .parseContigs <- function(df, i, names, cloneCall) { data <- df[[i]] @@ -422,6 +421,7 @@ list.input.return <- function(df, split.by) { return(min) } +#Parsing the CTgene .select.gene <- function(df, chain, gene, label) { if (chain %in% c("TRB", "TRD", "IGH")) { gene <- unname(c(V = 1, D = 2, J = 3, C = 4)[gene]) @@ -485,7 +485,7 @@ is_df_or_list_of_df <- function(x) { #Making lists for single-cell object, check blanks and apply chain filter .data.wrangle <- function(df, split.by, cloneCall, chain) { - df <- list.input.return(df, split.by) + df <- .list.input.return(df, split.by) df <- .checkBlanks(df, cloneCall) for (i in seq_along(df)) { if (chain != "both") { diff --git a/R/vizGenes.R b/R/vizGenes.R index 711de0cd..0a556655 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -49,14 +49,14 @@ vizGenes <- function(df, exportTable = FALSE, palette = "inferno") { element.names <- NULL - df <- list.input.return(df, split.by = split.by) + df <- .list.input.return(df, split.by = split.by) if(!is.null(group.by)) { df <- .groupList(df, group.by) } for(i in seq_along(df)) { df[[i]] <- .off.the.chain(df[[i]], chain, "CTgene") } - df <- bound.input.return(df) + df <- .bound.input.return(df) if (y.axis %!in% colnames(df) | is.null(y.axis)) { if (y.axis %!in% c("V", "D", "J", "C")) { y.axis <- "element.names" From 3207794554d92c83251e94612495709d25a5e562 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 1 Oct 2023 11:47:49 -0500 Subject: [PATCH 065/142] add equations to function manual --- R/clonalDiversity.R | 23 +++++++++++++++++++++++ R/clonalOverlap.R | 18 ++++++++++++++++++ man/clonalDiversity.Rd | 23 +++++++++++++++++++++++ man/clonalOverlap.Rd | 18 ++++++++++++++++++ 4 files changed, 82 insertions(+) diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index b88197ab..01c570d9 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -6,6 +6,29 @@ #' The function automatically down samples the diversity metrics using #' 100 boot straps The group parameter can be used to condense the individual #' samples. If a matrix output for the data is preferred, set exportTable = TRUE. +#' +#' @details +#' The formulas for the indices and estimators are as follows: +#' +#' \deqn{Shannon Index: H = - \sum p_i \cdot \log(p_i)} +#' +#' \deqn{Inverse Simpson Index: D^{-1} = 1 / \sum p_i^2} +#' +#' \deqn{Normalized Entropy: E^H = H / \log(S)} +#' +#' \deqn{Gini-Simpson Index: 1 - D = 1 - \sum p_i^2} +#' +#' \deqn{Chao1 Index: \hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} +#' +#' \deqn{Abundance-based Coverage Estimator (ACE): \hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} +#' +#' Where: +#' \itemize{ +#' \item{ \( p_i \) is the proportion of species \( i \) in the dataset.} +#' \item{ \( S \) is the total number of species.} +#' \item{ \( n_1 \) and \( n_2 \) are the number of singletons and doubletons, respectively.} +#' \item{ \( S_{abundant} \), \( S_{rare} \), \( C_{rare} \), and \( F_1 \) are parameters derived from the data.} +#' } #' #' @examples #' #Making combined contig data diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 0e078e30..6997e0b0 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -2,6 +2,24 @@ #' #' This functions allows for the calculation and visualizations of #' various overlap metrics for clonotypes. +#' +#' @details +#' The formulas for the indices are as follows: +#' +#' \deqn{Overlap Coefficient: OC = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} +#' +#' \deqn{Raw Count Overlap: RCO = \sum \min(a_i, b_i)} +#' +#' \deqn{Morisita Index: MI = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} +#' +#' \deqn{Jaccard Index: JI = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} +#' +#' \deqn{Cosine Similarity: CS = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} +#' +#' Where: +#' \itemize{ +#' \item{ \( a_i \) and \( b_i \) are the abundances of species \( i \) in groups A and B, respectively.} +#' } #' #' @examples #' #Making combined contig data diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index 0957b930..0d3e3466 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -63,6 +63,29 @@ The function automatically down samples the diversity metrics using 100 boot straps The group parameter can be used to condense the individual samples. If a matrix output for the data is preferred, set exportTable = TRUE. } +\details{ +The formulas for the indices and estimators are as follows: + +\deqn{Shannon Index: H = - \sum p_i \cdot \log(p_i)} + +\deqn{Inverse Simpson Index: D^{-1} = 1 / \sum p_i^2} + +\deqn{Normalized Entropy: E^H = H / \log(S)} + +\deqn{Gini-Simpson Index: 1 - D = 1 - \sum p_i^2} + +\deqn{Chao1 Index: \hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} + +\deqn{Abundance-based Coverage Estimator (ACE): \hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} + +Where: +\itemize{ + \item{ \( p_i \) is the proportion of species \( i \) in the dataset.} + \item{ \( S \) is the total number of species.} + \item{ \( n_1 \) and \( n_2 \) are the number of singletons and doubletons, respectively.} + \item{ \( S_{abundant} \), \( S_{rare} \), \( C_{rare} \), and \( F_1 \) are parameters derived from the data.} +} +} \examples{ #Making combined contig data combined <- combineTCR(contig_list, diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index a996fb83..ab856b5c 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -42,6 +42,24 @@ ggplot of the clonotypic overlap between elements of a list This functions allows for the calculation and visualizations of various overlap metrics for clonotypes. } +\details{ +The formulas for the indices are as follows: + +\deqn{Overlap Coefficient: OC = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} + +\deqn{Raw Count Overlap: RCO = \sum \min(a_i, b_i)} + +\deqn{Morisita Index: MI = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} + +\deqn{Jaccard Index: JI = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} + +\deqn{Cosine Similarity: CS = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} + +Where: +\itemize{ + \item{ \( a_i \) and \( b_i \) are the abundances of species \( i \) in groups A and B, respectively.} +} +} \examples{ #Making combined contig data combined <- combineTCR(contig_list, From dce9dc890bda0247696150f35881302191a64d61 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 1 Oct 2023 12:07:16 -0500 Subject: [PATCH 066/142] clean combineContigs --- R/clusterTCR.R | 2 +- R/combineContigs.R | 71 +++++++++++++++++++++++++--------------------- man/combineBCR.Rd | 32 +++++++++++---------- man/combineTCR.Rd | 13 +++------ 4 files changed, 61 insertions(+), 57 deletions(-) diff --git a/R/clusterTCR.R b/R/clusterTCR.R index e858032b..88c2e917 100644 --- a/R/clusterTCR.R +++ b/R/clusterTCR.R @@ -68,7 +68,7 @@ clusterTCR <- function(df, } #Defining clusters by edit distance output.list <- lapply(bound, function(x) { - cluster <- lvCompare(x, gene = paste0("TCR", ref), chain = ref2, threshold = threshold) + cluster <- .lvCompare(x, gene = paste0("TCR", ref), chain = ref2, threshold = threshold) cluster }) #Reformating output for compatibility diff --git a/R/combineContigs.R b/R/combineContigs.R index 592b0598..8ae58c82 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -18,7 +18,7 @@ utils::globalVariables(c( "tcr2_lines", "data1_lines", "data2_lines", "CT_lines" )) -#' @title Combining the list of T Cell Receptor contigs +#' @title Combining the list of T cell receptor contigs into clonotypes #' #' @description This function consolidates a list of TCR sequencing results to #' the level of the individual cell barcodes. Using the samples and ID @@ -29,23 +29,19 @@ utils::globalVariables(c( #' `filterMulti` are parameters that control how the function deals with #' barcodes with multiple chains recovered. #' -#' @details For single-sample TCR sequencing experiments, where the input is -#' just a single data.frame, the function will add the consolidated information -#' columns after the existing raw clonotype data -#' #' @examples #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' -#' @param df List of filtered contig annotations from 10x Genomics. -#' @param samples The labels of samples (required). -#' @param ID The additional sample labeling (optional). +#' @param df List of filtered contig annotations or outputs from \code{\link{loadContig}} +#' @param samples The labels of samples +#' @param ID The additional sample labeling #' @param removeNA This will remove any chain without values. #' @param removeMulti This will remove barcodes with greater than 2 chains. #' @param filterMulti This option will allow for the selection of the 2 #' corresponding chains with the highest expression for a single barcode. - +#' #' @import dplyr #' @export #' @return List of clonotypes for individual cell barcodes @@ -124,36 +120,42 @@ combineTCR <- function(df, final[[i]]<-final[[i]][!duplicated(final[[i]]$barcode),] final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ] } - if (removeNA) { final <- .removingNA(final)} - if (removeMulti) { final <- .removingMulti(final) } + if (removeNA) { + final <- .removingNA(final) + } + if (removeMulti) { + final <- .removingMulti(final) + } final } -#' Combining the list of B Cell Receptor contigs +#' Combining the list of B cell receptor contigs into clonotypes #' #' This function consolidates a list of BCR sequencing results to the level #' of the individual cell barcodes. Using the samples and ID parameters, #' the function will add the strings as prefixes to prevent issues with #' repeated barcodes. The resulting new barcodes will need to match the -#' seurat or SCE object in order to use, -#' \code{\link{combineExpression}}. Unlike combineTCR(), -#' combineBCR produces a column CTstrict of an index of nucleotide sequence -#' and the corresponding v-gene. This index automatically calculates -#' the Levenshtein distance between sequences with the same V gene and will -#' index sequences with <= 0.15 normalized Levenshtein distance with the same -#' ID. After which, clonotype clusters are called using the -#' `igraph:: component()` function. Clonotype that are clustered across multiple -#' sequences will then be labeled with "LD" in the CTstrict header. +#' Seurat or SCE object in order to use, \code{\link{combineExpression}}. +#' Unlike \code{\link{combineTCR}}, combineBCR produces a column **CTstrict** +#' of an index of nucleotide sequence and the corresponding V gene. +#' This index automatically calculates the Levenshtein distance between +#' sequences with the same V gene and will index sequences using a normalized +#' Levenshtein distance with the same ID. After which, clonotype clusters +#' are called using the \code{\link[igraph]{component}} function. Clonotype +#' that are clustered across multiple sequences will then be labeled +#' with "LD" in the CTstrict header. #' #' @examples #' #Data derived from the 10x Genomics intratumoral NSCLC B cells #' BCR <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv") -#' combined <- combineBCR(BCR, samples = "Patient1", -#' ID = "Time1", threshold = 0.85) +#' combined <- combineBCR(BCR, +#' samples = "Patient1", +#' ID = "Time1", +#' threshold = 0.85) #' -#' @param df List of filtered contig annotations from 10x Genomics. -#' @param samples The labels of samples (required). -#' @param ID The additional sample labeling (optional). +#' @param df List of filtered contig annotations or outputs from \code{\link{loadContig}}. +#' @param samples The labels of samples +#' @param ID The additional sample labeling #' @param call.related.clones Use the nucleotide sequence and V gene to call related clones. #' Default is set to TRUE. FALSE will return a CTstrict or strict clonotype as V gene + amino acid sequence #' @param threshold The normalized edit distance to consider. The higher the number the more @@ -213,8 +215,8 @@ combineBCR <- function(df, } dictionary <- bind_rows(final) if(call.related.clones) { - IGH <- lvCompare(dictionary, "IGH", "cdr3_nt1", threshold) - IGLC <- lvCompare(dictionary, "IGLC", "cdr3_nt2", threshold) + IGH <- .lvCompare(dictionary, "IGH", "cdr3_nt1", threshold) + IGLC <- .lvCompare(dictionary, "IGLC", "cdr3_nt2", threshold) } for(i in seq_along(final)) { if(call.related.clones) { @@ -250,9 +252,14 @@ combineBCR <- function(df, names(final) <- names for (i in seq_along(final)) { final[[i]] <- final[[i]][!duplicated(final[[i]]$barcode),] - final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ]} - if (removeNA) { final <- .removingNA(final) } - if (removeMulti) { final <- .removingMulti(final) } + final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ] + } + if (removeNA) { + final <- .removingNA(final) + } + if (removeMulti) { + final <- .removingMulti(final) + } return(final) } @@ -261,7 +268,7 @@ combineBCR <- function(df, #' @importFrom stringdist stringdistmatrix #' @importFrom igraph graph_from_data_frame components #' @importFrom dplyr bind_rows -lvCompare <- function(dictionary, gene, chain, threshold) { +.lvCompare <- function(dictionary, gene, chain, threshold) { overlap <- NULL out <- NULL dictionary[dictionary == "None"] <- NA diff --git a/man/combineBCR.Rd b/man/combineBCR.Rd index ac7474ea..897cacc4 100644 --- a/man/combineBCR.Rd +++ b/man/combineBCR.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/combineContigs.R \name{combineBCR} \alias{combineBCR} -\title{Combining the list of B Cell Receptor contigs} +\title{Combining the list of B cell receptor contigs into clonotypes} \usage{ combineBCR( df, @@ -16,11 +16,11 @@ combineBCR( ) } \arguments{ -\item{df}{List of filtered contig annotations from 10x Genomics.} +\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContig}}.} -\item{samples}{The labels of samples (required).} +\item{samples}{The labels of samples} -\item{ID}{The additional sample labeling (optional).} +\item{ID}{The additional sample labeling} \item{call.related.clones}{Use the nucleotide sequence and V gene to call related clones. Default is set to TRUE. FALSE will return a CTstrict or strict clonotype as V gene + amino acid sequence} @@ -43,20 +43,22 @@ This function consolidates a list of BCR sequencing results to the level of the individual cell barcodes. Using the samples and ID parameters, the function will add the strings as prefixes to prevent issues with repeated barcodes. The resulting new barcodes will need to match the -seurat or SCE object in order to use, -\code{\link{combineExpression}}. Unlike combineTCR(), -combineBCR produces a column CTstrict of an index of nucleotide sequence -and the corresponding v-gene. This index automatically calculates -the Levenshtein distance between sequences with the same V gene and will -index sequences with <= 0.15 normalized Levenshtein distance with the same -ID. After which, clonotype clusters are called using the -`igraph:: component()` function. Clonotype that are clustered across multiple -sequences will then be labeled with "LD" in the CTstrict header. +Seurat or SCE object in order to use, \code{\link{combineExpression}}. +Unlike \code{\link{combineTCR}}, combineBCR produces a column **CTstrict** +of an index of nucleotide sequence and the corresponding V gene. +This index automatically calculates the Levenshtein distance between +sequences with the same V gene and will index sequences using a normalized +Levenshtein distance with the same ID. After which, clonotype clusters +are called using the \code{\link[igraph]{component}} function. Clonotype +that are clustered across multiple sequences will then be labeled +with "LD" in the CTstrict header. } \examples{ #Data derived from the 10x Genomics intratumoral NSCLC B cells BCR <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv") -combined <- combineBCR(BCR, samples = "Patient1", -ID = "Time1", threshold = 0.85) +combined <- combineBCR(BCR, + samples = "Patient1", + ID = "Time1", + threshold = 0.85) } diff --git a/man/combineTCR.Rd b/man/combineTCR.Rd index 110cc075..8621fcd9 100644 --- a/man/combineTCR.Rd +++ b/man/combineTCR.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/combineContigs.R \name{combineTCR} \alias{combineTCR} -\title{Combining the list of T Cell Receptor contigs} +\title{Combining the list of T cell receptor contigs into clonotypes} \usage{ combineTCR( df, @@ -14,11 +14,11 @@ combineTCR( ) } \arguments{ -\item{df}{List of filtered contig annotations from 10x Genomics.} +\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContig}}} -\item{samples}{The labels of samples (required).} +\item{samples}{The labels of samples} -\item{ID}{The additional sample labeling (optional).} +\item{ID}{The additional sample labeling} \item{removeNA}{This will remove any chain without values.} @@ -40,11 +40,6 @@ Several levels of filtering exist - `removeNA`, `removeMulti`, or `filterMulti` are parameters that control how the function deals with barcodes with multiple chains recovered. } -\details{ -For single-sample TCR sequencing experiments, where the input is -just a single data.frame, the function will add the consolidated information -columns after the existing raw clonotype data -} \examples{ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", From c63da1ab8afc1396ecfa27df7de555a2bff9e3ba Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 2 Oct 2023 08:14:01 -0500 Subject: [PATCH 067/142] housekeeping --- NAMESPACE | 3 +- NEWS | 2 + R/checkContig.R | 123 ------------------ R/clonalBias.R | 8 +- R/clonalHomeostasis.R | 30 ++--- R/{occupiedscRepertoire.R => clonalOccupy.R} | 46 +++---- R/clonalQuant.R | 64 +++++---- R/clonalSizeDistribution.R | 8 +- R/combineContigs.R | 6 +- R/combineExpression.R | 45 +++---- R/global.R | 3 +- R/utils.R | 8 +- man/checkContig.Rd | 52 -------- man/clonalBias.Rd | 2 +- man/clonalHomeostasis.Rd | 10 +- ...ccupiedscRepertoire.Rd => clonalOccupy.Rd} | 21 ++- man/combineBCR.Rd | 4 +- man/combineExpression.Rd | 4 +- man/combineTCR.Rd | 2 +- tests/testthat/test-combineContigs.R | 5 +- tests/testthat/test-seuratFunctions.R | 11 +- tests/testthat/test-seurat_command.R | 12 +- tests/testthat/test-utils.R | 16 +-- tests/testthat/test-viz.R | 69 +++++----- .../combineTCR_list_expected.rds | Bin 1192 -> 1344 bytes vignettes/vignette.Rmd | 39 ++---- 26 files changed, 196 insertions(+), 397 deletions(-) delete mode 100644 R/checkContig.R rename R/{occupiedscRepertoire.R => clonalOccupy.R} (69%) delete mode 100644 man/checkContig.Rd rename man/{occupiedscRepertoire.Rd => clonalOccupy.Rd} (73%) diff --git a/NAMESPACE b/NAMESPACE index 434c91d0..187aeb47 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,13 +4,13 @@ export(StartracDiversity) export(abundanceContig) export(addVariable) export(alluvialClonotypes) -export(checkContig) export(clonalBias) export(clonalCompare) export(clonalDiversity) export(clonalHomeostasis) export(clonalLength) export(clonalNetwork) +export(clonalOccupy) export(clonalOverlap) export(clonalOverlay) export(clonalProportion) @@ -27,7 +27,6 @@ export(exportClones) export(getCirclize) export(highlightClones) export(loadContigs) -export(occupiedscRepertoire) export(percentAA) export(percentGenes) export(percentVJ) diff --git a/NEWS b/NEWS index 1a35180f..783d6712 100644 --- a/NEWS +++ b/NEWS @@ -13,6 +13,7 @@ NEW FEATURES * Changed quantContig to clonalQuant * Changed highlightClonotypes to highlightClones * Changed lengthContigs to clonalLength +* Changed occupiedscRepertoire to clonalOccupy UNDERLYING CHANGES @@ -26,6 +27,7 @@ DEPRECATED AND DEFUNCT * Deprecate stripBarcodes() * Deprecate expression2List() (now only an internal function). +* Deprecate checkContigs() CHANGES IN VERSION 1.11.0 diff --git a/R/checkContig.R b/R/checkContig.R deleted file mode 100644 index 4721bd55..00000000 --- a/R/checkContig.R +++ /dev/null @@ -1,123 +0,0 @@ -#' Assess the number of NAs in clonotypes -#' -#' This function quantifies the percent of NA values in either the -#' sequence information of the VDJC gene usage. If a data frame -#' output for the raw numbers and percentage of NAs are preferred, -#' set exportTable = TRUE. -#' -#' @examples -#' #Making combined contig data -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' checkContig(combined, chain = "TRA", examine = "chain", group.by = "sample") -#' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -#' \code{\link{combineExpression}}. -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param examine indicate either "chain" for sequence-based NA or -#' "gene" for NA percentages across the VDJC genes. -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. -#' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() -#' @import dplyr -#' @importFrom stringr str_split -#' @export -#' @return ggplot of the percentage of NA values -#' -checkContig <- function(df, - chain = "TRA", - examine = "gene", - group.by = NULL, - split.by = NULL, - exportTable = FALSE, - palette = "inferno") { - NA.values <- NA - x.axis <- c(split.by, group.by) #condensing for ploting. - x.axis <- x.axis[1] - if(is.null(x.axis)) { - stop("Please select a group.by or split.by variable.") - } - if(chain %in% c("TRA", "TRG", "IGL")) { - genes <- c("v_gene", "j_gene", "c_gene") - } else { - genes <- c("v_gene", "d_genes", "j_gene", "c_gene") - } - df <- .data.wrangle(df, split.by, "CTgene", chain) - - for (i in seq_along(df)) { - df[[i]] <- .off.the.chain(df[[i]], chain, "CTaa") - df[[i]] <- .off.the.chain(df[[i]], chain, "CTnt") - df[[i]][df[[i]] == "NA"] <- NA - } - - names <- names(df) - - dat <- bind_rows(df) - if (examine == "chain") { - if(!is.null(split.by)) { - na.by.chain <- dat %>% - group_by(dat[,split.by]) %>% - summarise(NA.values = sum(is.na(CTaa)), - total.cells = n()) - colnames(na.by.chain)[1] <- split.by - } else if(!is.null(group.by)) { - na.by.chain <- dat %>% - group_by(dat[,group.by]) %>% - summarise(NA.values = sum(is.na(CTaa)), - total.cells = n()) - colnames(na.by.chain)[1] <- group.by - } else { - stop("Please select a split.by or group.by variable") - } - if(exportTable) { - return(na.by.chain) - } - na.by.chain <- na.by.chain %>% - mutate(value = round((NA.values/total.cells)*100, 2)) %>% - as.data.frame() - - na.by.chain$variable<- "chain" - plot.data <- na.by.chain - } else if (examine == "gene") { - out <- str_split(dat[,"CTgene"], ";", simplify = TRUE)[,1] - out <- data.frame(str_split(out, "[.]", simplify = TRUE)) - out [out ==""] <- NA - out [out =="None"] <- NA - dat <- cbind(dat[,c(x.axis)], out) - colnames(dat)[1] <- x.axis - ############# - #Summarise the NAs by column/group - na.by.gene <- dat %>% - group_by(dat[,x.axis]) %>% - summarise(across(colnames(dat)[seq_len(ncol(dat))[-1]], list(~ sum(is.na(.))))) - #Summarise number by column/group - colnames(na.by.gene) <- c(x.axis, genes) - total.value <- dat %>% - group_by(dat[,x.axis]) %>% - summarise(total.cells = n()) - na.by.gene <- data.frame(na.by.gene, total = total.value[,ncol(total.value)]) - if(exportTable) { - return(na.by.gene) - } - na.by.gene <- na.by.gene %>% - mutate(across(genes, ~round((./total.cells)*100,2))) - na.by.gene <- melt(na.by.gene[,seq_len(length(genes) + 1)], - id.vars = x.axis) - plot.data <- na.by.gene - - } - col <- length(unique(plot.data[,x.axis])) - plot <- ggplot(plot.data, aes(x = variable, y = value)) + - geom_boxplot(width = 0.5, fill = "grey", outlier.alpha = 0) + - geom_jitter(aes(color = plot.data[,x.axis]), size = 3) + - labs(color = x.axis) + - theme_classic() + - ylab("Percent of NAs") + - scale_color_manual(values = .colorizer(palette, col)) + - theme(axis.title.x = element_blank()) - return(plot) -} diff --git a/R/clonalBias.R b/R/clonalBias.R index be530314..598232ad 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -21,7 +21,7 @@ #' scRep_example <- combineExpression(combined, scRep_example) #' scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #' -#' #Using occupiedscRepertoire() +#' #Using clonalBias() #' clonalBias(scRep_example, #' cloneCall = "aa", #' split.by = "Patient", @@ -53,13 +53,14 @@ clonalBias <- function(df, min.expand=10, exportTable = FALSE) { cloneCall <- .theCall(cloneCall) - + #Calculating bias bias <- get_clono_bias(df, split.by = split.by, group.by = group.by , cloneCall=cloneCall, min.expand=min.expand) df_shuffle.list <- list() + #Bootstrapping for (ii in seq_len(n.boots)) { df_shuffle.list[[ii]] <- get_clono_bias(df, split.by = split.by, group.by = group.by, @@ -70,6 +71,7 @@ clonalBias <- function(df, } df_shuffle <- Reduce(rbind, df_shuffle.list) + #Sumarrising boot straps stat.summary <- df_shuffle %>% group_by(ncells) %>% summarise(mean = mean(bias), @@ -85,7 +87,7 @@ clonalBias <- function(df, z.score <- (bias[i,]$bias - row.pull$mean)/row.pull$std bias$Z.score[i] <- z.score } - + #Plotting plot <- ggplot(bias, aes(x=ncells,y=bias)) + geom_point(aes(colour=Top_state)) + .quiet(stat_quantile(data=df_shuffle, quantiles = c(corrected_p), method = "rqss", lambda=3)) + diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 356a764e..2d2e19be 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -1,10 +1,10 @@ #' Examining the clonal homeostasis #' #' This function calculates the space occupied by clonotype proportions. -#' The grouping of these clonotypes is based on the parameter cloneTypes, -#' at default, cloneTypes will group the clonotypes into bins of Rare = 0 +#' The grouping of these clonotypes is based on the parameter cloneSize, +#' at default, cloneSize will group the clonotypes into bins of Rare = 0 #' to 0.0001, Small = 0.0001 to 0.001, etc. To adjust the proportions, -#' change the number or labeling of the cloneTypes paramter. If a matrix +#' change the number or labeling of the cloneSize paramter. If a matrix #' output for the data is preferred, set exportTable = TRUE. #' #' @examples @@ -16,7 +16,7 @@ #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param cloneTypes The cutpoints of the proportions. +#' @param cloneSize The cutpoints of the proportions. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -35,15 +35,15 @@ #' @export #' @return ggplot of the space occupied by the specific proportion of clonotypes clonalHomeostasis <- function(df, - cloneTypes = c(Rare = .0001, Small = .001, - Medium = .01, Large = .1, Hyperexpanded = 1), + cloneSize = + c(Rare = .0001, Small = .001, Medium = .01, Large = .1, Hyperexpanded = 1), cloneCall = "strict", chain = "both", group.by = NULL, split.by = NULL, exportTable = FALSE, palette = "inferno") { - cloneTypes <- c(None = 0, cloneTypes) + cloneSize <- c(None = 0, cloneSize) cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) @@ -53,21 +53,21 @@ clonalHomeostasis <- function(df, } #Generating data matrix to store value - mat <- matrix(0, length(df), length(cloneTypes) - 1, + mat <- matrix(0, length(df), length(cloneSize) - 1, dimnames = list(names(df), - names(cloneTypes)[-1])) + names(cloneSize)[-1])) #Assigning the clonal grouping df <- lapply(df, '[[', cloneCall) df <- lapply(df, na.omit) fun <- function(x) { table(x)/length(x) } df <- lapply(df, fun) - for (i in 2:length(cloneTypes)) { - mat[,i-1] <- vapply(df, function (x) sum(x[x > cloneTypes[i-1] & x <= - cloneTypes[i]]), FUN.VALUE = numeric(1)) - colnames(mat)[i-1] <- paste0(names(cloneTypes[i]), ' (', - cloneTypes[i-1], ' < X <= ', - cloneTypes[i], ')') } + for (i in 2:length(cloneSize)) { + mat[,i-1] <- vapply(df, function (x) sum(x[x > cloneSize[i-1] & x <= + cloneSize[i]]), FUN.VALUE = numeric(1)) + colnames(mat)[i-1] <- paste0(names(cloneSize[i]), ' (', + cloneSize[i-1], ' < X <= ', + cloneSize[i], ')') } if (exportTable) { return(mat) } diff --git a/R/occupiedscRepertoire.R b/R/clonalOccupy.R similarity index 69% rename from R/occupiedscRepertoire.R rename to R/clonalOccupy.R index 39e641df..ad2c40cf 100644 --- a/R/occupiedscRepertoire.R +++ b/R/clonalOccupy.R @@ -1,8 +1,8 @@ -#' Visualize the number of single cells with clonotype frequencies by cluster +#' Visualize the number of single cells with cloneSizes by cluster #' #' View the count of clonotypes frequency group in seurat or SCE object -#' meta data after combineExpression(). The visualization will take the -#' new meta data variable "cloneType" and plot the number of cells with +#' meta data after \code{\link{combineExpression}}. The visualization will take the +#' new meta data variable "cloneSize" and plot the number of cells with #' each designation using a secondary variable, like cluster. Credit to #' the idea goes to Drs. Carmona and Andreatta and their work with #' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. @@ -15,14 +15,13 @@ #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #' #' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) +#' scRep_example <- combineExpression(combined, scRep_example) #' -#' #Using occupiedscRepertoire() -#' occupiedscRepertoire(sce, x.axis = "ident") -#' table <- occupiedscRepertoire(sce, x.axis = "ident", exportTable = TRUE) +#' #Using clonalOccupy +#' clonalOccupy(scRep_example, x.axis = "ident") +#' table <- clonalOccupy(scRep_example, x.axis = "ident", exportTable = TRUE) #' #' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param x.axis The variable in the meta data to graph along the x.axis @@ -39,23 +38,25 @@ #' @export #' @return Stacked bar plot of counts of cells by clonotype frequency group -occupiedscRepertoire <- function(sc, - x.axis = "ident", - label = TRUE, - facet.by = NULL, - proportion = FALSE, - na.include = FALSE, - exportTable = FALSE, - palette = "inferno") { +clonalOccupy <- function(sc, + x.axis = "ident", + label = TRUE, + facet.by = NULL, + proportion = FALSE, + na.include = FALSE, + exportTable = FALSE, + palette = "inferno") { .checkSingleObject(sc) meta <- .grabMeta(sc) meta <- melt(table(meta[!is.na(meta$Frequency), - c(x.axis, facet.by, "cloneType")], useNA = "ifany")) + c(x.axis, facet.by, "cloneSize")], useNA = "ifany")) + #Check for NAs if (!na.include) { meta <- na.omit(meta) } meta <- meta[meta$value != 0,] + #Convert to proportion if(proportion) { meta <- meta %>% group_by(meta[,1]) %>% @@ -66,15 +67,16 @@ occupiedscRepertoire <- function(sc, if (exportTable) { return(meta) } - col <- length(unique(meta$cloneType)) + #Plotting + col <- length(unique(meta[,"cloneSize"])) if(proportion) { - plot <- ggplot(meta, aes(x = meta[,x.axis], y = prop, fill = cloneType)) + - geom_bar(stat = "identity") + plot <- ggplot(meta, aes(x = meta[,x.axis], y = prop, fill = cloneSize)) + + geom_bar(stat = "identity", color = "black", lwd = 0.25) lab <- "Proportion of Cells" } else { - plot <- ggplot(meta, aes(x = meta[,x.axis], y = value, fill = cloneType)) + - geom_bar(stat = "identity") + plot <- ggplot(meta, aes(x = meta[,x.axis], y = value, fill = cloneSize)) + + geom_bar(stat = "identity", color = "black", lwd = 0.25) lab <- "Single Cells" } diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 2b55b5b0..b1f58cb8 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -45,49 +45,37 @@ clonalQuant <- function(df, cloneCall <- .theCall(cloneCall) df <- .data.wrangle(df, split.by, cloneCall, chain) + mat.names <- c("contigs","values", "total", group.by) #Set up mat to store and selecting graph parameters if (!is.null(group.by)) { x <- group.by labs <- group.by - mat <- data.frame(matrix(NA, length(df), 4)) - colnames(mat) <- c("contigs","values", "total", group.by) - for (i in seq_along(df)) { - mat[i,1] <- length(unique(df[[i]][,cloneCall])) - mat[i,2] <- names(df)[i] - mat[i,3] <- length(df[[i]][,cloneCall]) - location <- which(colnames(df[[i]]) == group.by) - mat[i,4] <- df[[i]][1,location] } - col <- length(unique(mat[,group.by])) - if (scale) { y <- "scaled" - mat$scaled <- mat$contigs/mat$total*100 - ylab <- "Percent of Unique Clonotype" - - } else { - y <- "contigs" - x <- group.by - ylab <- "Unique Clonotypes"} } else { x <- "values" labs <- "Samples" - mat <- data.frame(matrix(NA, length(df), 3)) - colnames(mat) <- c("contigs","values", "total") - - #Generating quantification - for (i in seq_along(df)) { - mat[i,1] <- length(unique(df[[i]][,cloneCall])) + col <- length(unique(names(df))) + } + mat <- data.frame(matrix(NA, length(df), length(mat.names))) + colnames(mat) <- mat.names + for (i in seq_along(df)) { + mat[i,1] <- length(na.omit(unique(df[[i]][,cloneCall]))) mat[i,2] <- names(df)[i] - mat[i,3] <- length(df[[i]][,cloneCall]) - } - col <- length(unique(mat$values)) - if (scale == TRUE) { + mat[i,3] <- length(na.omit(df[[i]][,cloneCall])) + if (!is.null(group.by)) { + location <- which(colnames(df[[i]]) == group.by) + mat[i,4] <- df[[i]][1,location] + } + } + if (scale) { y <- "scaled" mat$scaled <- mat$contigs/mat$total*100 ylab <- "Percent of Unique Clonotype" - } else { + } else { y <- "contigs" - ylab <- "Unique Clonotypes" - } - } + x <- group.by + ylab <- "Unique Clonotypes" + } + if (exportTable) { if (length(df) > 1) { return(mat) @@ -102,12 +90,20 @@ clonalQuant <- function(df, if(order & is.null(group.by)) { mat[,x] <- factor(mat[,x], levels = mat[,x]) } + if(!is.null(group.by)) { + col <- length(unique(mat[,group.by])) + } + #Plotting plot <- ggplot(aes(x=mat[,x], y=mat[,y], fill=as.factor(mat[,x])), data = mat) + - stat_summary(geom = "errorbar", fun.data = mean_se, - position = "dodge", width=.5) + labs(fill = labs) + + stat_summary(geom = "errorbar", + fun.data = mean_se, + position = "dodge", + width=.5) + + labs(fill = labs) + + ylab(ylab) + stat_summary(fun=mean, geom="bar", color="black", lwd=0.25)+ - theme_classic() + xlab("Samples") + ylab(ylab) + + theme_classic() + xlab("Samples") + scale_fill_manual(values = .colorizer(palette, col)) # if it is a single run, remove x axis labels if sample name missing diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index a63c2c21..e09c1abb 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -56,6 +56,8 @@ clonalSizeDistribution <- function(df, } data <- bind_rows(df) unique_df <- unique(data[,cloneCall]) + + #Forming data frame to store values Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) colnames(Con.df)[1] <- "clonotype" @@ -81,6 +83,7 @@ clonalSizeDistribution <- function(df, distances <- .get_distances(list, grid, modelType="Spliced") mat_melt <- dendro_data(hclust(as.dist(distances), method = method), type = "rectangle") + #Plotting plot <- ggplot() + geom_segment(data = segment(mat_melt), aes(x = x, y = y, xend = xend, yend = yend)) + @@ -144,11 +147,6 @@ clonalSizeDistribution <- function(df, stop("std.err must be TRUE or FALSE.") } - if(!(method %in% c("Nelder-Mead","BFGS", - "CG", "L-BFGS-B", "SANN", "Brent"))){ - stop("invalid method supplied.") - } - if(is.null(shift)){ shift <- min(x) } diff --git a/R/combineContigs.R b/R/combineContigs.R index 8ae58c82..1e6e4adf 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -34,7 +34,7 @@ utils::globalVariables(c( #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' -#' @param df List of filtered contig annotations or outputs from \code{\link{loadContig}} +#' @param df List of filtered contig annotations or outputs from \code{\link{loadContigs}} #' @param samples The labels of samples #' @param ID The additional sample labeling #' @param removeNA This will remove any chain without values. @@ -141,7 +141,7 @@ combineTCR <- function(df, #' This index automatically calculates the Levenshtein distance between #' sequences with the same V gene and will index sequences using a normalized #' Levenshtein distance with the same ID. After which, clonotype clusters -#' are called using the \code{\link[igraph]{component}} function. Clonotype +#' are called using the \code{\link[igraph]{components}} function. Clonotype #' that are clustered across multiple sequences will then be labeled #' with "LD" in the CTstrict header. #' @@ -153,7 +153,7 @@ combineTCR <- function(df, #' ID = "Time1", #' threshold = 0.85) #' -#' @param df List of filtered contig annotations or outputs from \code{\link{loadContig}}. +#' @param df List of filtered contig annotations or outputs from \code{\link{loadContigs}}. #' @param samples The labels of samples #' @param ID The additional sample labeling #' @param call.related.clones Use the nucleotide sequence and V gene to call related clones. diff --git a/R/combineExpression.R b/R/combineExpression.R index 68f86e6d..c858e718 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -38,7 +38,7 @@ #' while NULL will merge all the contigs into a single data frame. #' @param proportion Whether to use the total frequency (FALSE) or the #' proportion (TRUE) of the clonotype based on the group.by variable. -#' @param cloneTypes The bins for the grouping based on frequency +#' @param cloneSize The bins for the grouping based on frequency #' @param filterNA Method to subset seurat object of barcodes without #' clonotype information #' @param addLabel This will add a label to the frequency header, allowing @@ -51,23 +51,20 @@ #' @return seurat or SingleCellExperiment object with attached clonotype #' information #' -combineExpression <- function( - df, - sc, - cloneCall ="strict", - chain = "both", - group.by ="none", - proportion = TRUE, - filterNA = FALSE, - cloneTypes = c( - Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1 - ), - addLabel = FALSE -) { +combineExpression <- function(df, + sc, + cloneCall ="strict", + chain = "both", + group.by ="none", + proportion = TRUE, + filterNA = FALSE, + cloneSize = c( + Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1), + addLabel = FALSE) { call_time <- Sys.time() options( dplyr.summarise.inform = FALSE ) - cloneTypes <- c(None = 0, cloneTypes) + cloneSize <- c(None = 0, cloneSize) df <- .checkList(df) cloneCall <- .theCall(cloneCall) Con.df <- NULL @@ -130,15 +127,15 @@ combineExpression <- function( } - Con.df$cloneType <- NA - for (x in seq_along(cloneTypes)) { names(cloneTypes)[x] <- - paste0(names(cloneTypes[x]), ' (', cloneTypes[x-1], - ' < X <= ', cloneTypes[x], ')') } - for (i in 2:length(cloneTypes)) { Con.df$cloneType <- - ifelse(Con.df$Frequency > cloneTypes[i-1] & Con.df$Frequency - <= cloneTypes[i], names(cloneTypes[i]), Con.df$cloneType) } + Con.df$cloneSize <- NA + for (x in seq_along(cloneSize)) { names(cloneSize)[x] <- + paste0(names(cloneSize[x]), ' (', cloneSize[x-1], + ' < X <= ', cloneSize[x], ')') } + for (i in 2:length(cloneSize)) { Con.df$cloneSize <- + ifelse(Con.df$Frequency > cloneSize[i-1] & Con.df$Frequency + <= cloneSize[i], names(cloneSize[i]), Con.df$cloneSize) } PreMeta <- unique(Con.df[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency", "cloneType")]) + "CTaa", "CTstrict", "Frequency", "cloneSize")]) dup <- PreMeta$barcode[which(duplicated(PreMeta$barcode))] PreMeta <- PreMeta[PreMeta$barcode %!in% dup,] barcodes <- PreMeta$barcode @@ -171,7 +168,7 @@ combineExpression <- function( rownames(colData(sc)) <- rownames } if (filterNA) { sc <- .filteringNA(sc) } - sc$cloneType <- factor(sc$cloneType, levels = rev(names(cloneTypes))) + sc$cloneSize <- factor(sc$cloneSize, levels = rev(names(cloneSize))) if(is_seurat_object(sc)) { sc@commands[["combineExpression"]] <- make_screp_seurat_cmd( diff --git a/R/global.R b/R/global.R index 6ff246f2..482ca90a 100644 --- a/R/global.R +++ b/R/global.R @@ -12,8 +12,7 @@ utils::globalVariables ("CTaa") utils::globalVariables ("CTnt") utils::globalVariables ("clone") - utils::globalVariables ("CloneType") - utils::globalVariables ("cloneType") + utils::globalVariables ("cloneSize") utils::globalVariables ("cluster") utils::globalVariables ("cdr3") utils::globalVariables ("Freq") diff --git a/R/utils.R b/R/utils.R index 12b9c946..a7d09d39 100644 --- a/R/utils.R +++ b/R/utils.R @@ -199,18 +199,18 @@ is_seurat_or_se_object <- function(obj) { #' @importFrom SingleCellExperiment colData .filteringNA <- function(sc) { meta <- .grabMeta(sc) - evalNA <- data.frame(meta[,"cloneType"]) + evalNA <- data.frame(meta[,"cloneSize"]) colnames(evalNA) <- "indicator" evalNA <- evalNA %>% transmute(indicator = ifelse(is.na(indicator), 0, 1)) rownames(evalNA) <- rownames(meta) if (inherits(x=sc, what ="cell_data_set")){ colData(sc)[["evalNA"]]<-evalNA - return(sc[, !is.na(sc$cloneType)]) + return(sc[, !is.na(sc$cloneSize)]) }else{ col.name <- names(evalNA) %||% colnames(evalNA) sc[[col.name]] <- evalNA - sc <- subset(sc, cloneType != 0) + sc <- subset(sc, cloneSize != 0) return(sc) } } @@ -476,7 +476,7 @@ is_df_or_list_of_df <- function(x) { df <- NULL for (i in seq_along(unique)) { subset <- subset(meta, meta[,split.by] == unique[i]) - subset <- subset(subset, !is.na(cloneType)) + subset <- subset(subset, !is.na(cloneSize)) df[[i]] <- subset } names(df) <- unique diff --git a/man/checkContig.Rd b/man/checkContig.Rd deleted file mode 100644 index caaa8ccd..00000000 --- a/man/checkContig.Rd +++ /dev/null @@ -1,52 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/checkContig.R -\name{checkContig} -\alias{checkContig} -\title{Assess the number of NAs in clonotypes} -\usage{ -checkContig( - df, - chain = "TRA", - examine = "gene", - group.by = NULL, - split.by = NULL, - exportTable = FALSE, - palette = "inferno" -) -} -\arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -\code{\link{combineExpression}}.} - -\item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} - -\item{examine}{indicate either "chain" for sequence-based NA or -"gene" for NA percentages across the VDJC genes.} - -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} - -\item{exportTable}{Returns the data frame used for forming the graph} - -\item{palette}{Colors to use in visualization - input any hcl.pals()} -} -\value{ -ggplot of the percentage of NA values -} -\description{ -This function quantifies the percent of NA values in either the -sequence information of the VDJC gene usage. If a data frame -output for the raw numbers and percentage of NAs are preferred, -set exportTable = TRUE. -} -\examples{ -#Making combined contig data -combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) -checkContig(combined, chain = "TRA", examine = "chain", group.by = "sample") - -} diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index ceb94731..2639cc7a 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -58,7 +58,7 @@ scRep_example <- get(data("scRep_example")) scRep_example <- combineExpression(combined, scRep_example) scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) -#Using occupiedscRepertoire() +#Using clonalBias() clonalBias(scRep_example, cloneCall = "aa", split.by = "Patient", diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index a310da84..58a23894 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -6,7 +6,7 @@ \usage{ clonalHomeostasis( df, - cloneTypes = c(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = + cloneSize = c(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1), cloneCall = "strict", chain = "both", @@ -20,7 +20,7 @@ clonalHomeostasis( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{cloneTypes}{The cutpoints of the proportions.} +\item{cloneSize}{The cutpoints of the proportions.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -44,10 +44,10 @@ ggplot of the space occupied by the specific proportion of clonotypes } \description{ This function calculates the space occupied by clonotype proportions. -The grouping of these clonotypes is based on the parameter cloneTypes, -at default, cloneTypes will group the clonotypes into bins of Rare = 0 +The grouping of these clonotypes is based on the parameter cloneSize, +at default, cloneSize will group the clonotypes into bins of Rare = 0 to 0.0001, Small = 0.0001 to 0.001, etc. To adjust the proportions, -change the number or labeling of the cloneTypes paramter. If a matrix +change the number or labeling of the cloneSize paramter. If a matrix output for the data is preferred, set exportTable = TRUE. } \examples{ diff --git a/man/occupiedscRepertoire.Rd b/man/clonalOccupy.Rd similarity index 73% rename from man/occupiedscRepertoire.Rd rename to man/clonalOccupy.Rd index f77c8501..502bdfea 100644 --- a/man/occupiedscRepertoire.Rd +++ b/man/clonalOccupy.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/occupiedscRepertoire.R -\name{occupiedscRepertoire} -\alias{occupiedscRepertoire} -\title{Visualize the number of single cells with clonotype frequencies by cluster} +% Please edit documentation in R/clonalOccupy.R +\name{clonalOccupy} +\alias{clonalOccupy} +\title{Visualize the number of single cells with cloneSizes by cluster} \usage{ -occupiedscRepertoire( +clonalOccupy( sc, x.axis = "ident", label = TRUE, @@ -38,8 +38,8 @@ Stacked bar plot of counts of cells by clonotype frequency group } \description{ View the count of clonotypes frequency group in seurat or SCE object -meta data after combineExpression(). The visualization will take the -new meta data variable "cloneType" and plot the number of cells with +meta data after \code{\link{combineExpression}}. The visualization will take the +new meta data variable "cloneSize" and plot the number of cells with each designation using a secondary variable, like cluster. Credit to the idea goes to Drs. Carmona and Andreatta and their work with \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. @@ -52,13 +52,12 @@ combined <- combineTCR(contig_list, #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) #Using combineExpresion() sce <- combineExpression(combined, sce) -#Using occupiedscRepertoire() -occupiedscRepertoire(sce, x.axis = "ident") -table <- occupiedscRepertoire(sce, x.axis = "ident", exportTable = TRUE) +#Using clonalOccupy +clonalOccupy(sce, x.axis = "ident") +table <- clonalOccupy(sce, x.axis = "ident", exportTable = TRUE) } diff --git a/man/combineBCR.Rd b/man/combineBCR.Rd index 897cacc4..510d9372 100644 --- a/man/combineBCR.Rd +++ b/man/combineBCR.Rd @@ -16,7 +16,7 @@ combineBCR( ) } \arguments{ -\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContig}}.} +\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContigs}}.} \item{samples}{The labels of samples} @@ -49,7 +49,7 @@ of an index of nucleotide sequence and the corresponding V gene. This index automatically calculates the Levenshtein distance between sequences with the same V gene and will index sequences using a normalized Levenshtein distance with the same ID. After which, clonotype clusters -are called using the \code{\link[igraph]{component}} function. Clonotype +are called using the \code{\link[igraph]{components}} function. Clonotype that are clustered across multiple sequences will then be labeled with "LD" in the CTstrict header. } diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index 4c758b8c..725895a2 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -12,7 +12,7 @@ combineExpression( group.by = "none", proportion = TRUE, filterNA = FALSE, - cloneTypes = c(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = + cloneSize = c(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1), addLabel = FALSE ) @@ -40,7 +40,7 @@ proportion (TRUE) of the clonotype based on the group.by variable.} \item{filterNA}{Method to subset seurat object of barcodes without clonotype information} -\item{cloneTypes}{The bins for the grouping based on frequency} +\item{cloneSize}{The bins for the grouping based on frequency} \item{addLabel}{This will add a label to the frequency header, allowing the user to try multiple group.by variables or recalculate frequencies after diff --git a/man/combineTCR.Rd b/man/combineTCR.Rd index 8621fcd9..da0b240c 100644 --- a/man/combineTCR.Rd +++ b/man/combineTCR.Rd @@ -14,7 +14,7 @@ combineTCR( ) } \arguments{ -\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContig}}} +\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContigs}}} \item{samples}{The labels of samples} diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R index 4ef32f8a..8aa8756b 100644 --- a/tests/testthat/test-combineContigs.R +++ b/tests/testthat/test-combineContigs.R @@ -5,15 +5,14 @@ test_that("combineTCR works", { trial1 <- combineTCR( df = lapply(contig_list[1:3], head), - samples = c("PY", "PY", "PX"), - ID = c("P", "T", "P") + samples = c("P17B", "P17L", "P18B") ) expected1 <- readRDS("testdata/combineContigs/combineTCR_list_expected.rds") expect_identical(trial1, expected1) trial2 <- combineTCR( - head(contig_list[[1]]), samples = "PY", ID = "P" + head(contig_list[[1]]), samples = "P17B" )[[1]] expected2 <- expected1[[1]] diff --git a/tests/testthat/test-seuratFunctions.R b/tests/testthat/test-seuratFunctions.R index 223073b3..1947ecc4 100644 --- a/tests/testthat/test-seuratFunctions.R +++ b/tests/testthat/test-seuratFunctions.R @@ -2,12 +2,15 @@ test_that("combineExpression works with seurat objects", { data("mini_contig_list", "scRep_example") - combined_test <- combineExpression(mini_contig_list, scRep_example) + combined <- combineTCR(mini_contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + combined_test <- combineExpression(combined, scRep_example) - expect_length(combined_test@meta.data, 15) - expect_equal(combined_test@meta.data[, 1:8], scRep_example@meta.data[, 1:8]) + expect_length(combined_test@meta.data, 12) + expect_equal(combined_test@meta.data[, 1:6], scRep_example@meta.data[, 1:6]) expect_equal( - combined_test@meta.data[, 9:15], + combined_test@meta.data[, 7:12], getdata("seuratFunctions", "combineExpression_new_metadata") ) }) diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R index 7bf8279a..5b1526f7 100644 --- a/tests/testthat/test-seurat_command.R +++ b/tests/testthat/test-seurat_command.R @@ -1,24 +1,26 @@ test_that("make_screp_seurat_cmd works", { data("scRep_example", "mini_contig_list") - - test_obj <- combineExpression(mini_contig_list, scRep_example) + combined <- combineTCR(mini_contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + test_obj <- combineExpression(combined, scRep_example) expect_true(!is.null(test_obj@commands[["combineExpression"]])) test_obj <- test_obj@commands[["combineExpression"]] expect_identical(test_obj@name, "combineExpression") expect_identical(class(test_obj@time.stamp), c("POSIXct", "POSIXt")) - expect_identical(test_obj@assay.used, "integrated") + expect_identical(test_obj@assay.used, "RNA") expect_identical( test_obj@call.string, - "combineExpression(mini_contig_list, scRep_example)" + "combineExpression(combined, scRep_example)" ) expect_equal( test_obj@params, list( cloneCall = "CTstrict", chain = "both", group.by = "none", proportion = TRUE, filterNA = FALSE, - cloneTypes = c( + cloneSize = c( `None ( < X <= 0)` = 0, `Rare (0 < X <= 1e-04)` = 1e-04, `Small (1e-04 < X <= 0.001)` = 0.001, `Medium (0.001 < X <= 0.01)` = 0.01, diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index b3d158b7..ecb7313d 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -17,9 +17,9 @@ test_that("'%!in%' works", { test_that("checkList works", { data("contig_list") - expect_identical(checkList(contig_list), contig_list) - expect_identical(checkList(contig_list[[1]]), list(contig_list[[1]])) - expect_identical(checkList(contig_list[[1]])[[1]], contig_list[[1]]) + expect_identical(.checkList(contig_list), contig_list) + expect_identical(.checkList(contig_list[[1]]), list(contig_list[[1]])) + expect_identical(.checkList(contig_list[[1]])[[1]], contig_list[[1]]) # no idea what to put to make the stop message happen. perhaps with data.table(contig_list) ? but then it shouldnt fail :P }) @@ -28,7 +28,7 @@ test_that("checkContigs works", { df1 = data.frame(a = c("x", "", "z"), b = c("1", "2", "3")), df2 = data.frame(c = c("foo", "bar", ""), d = c("", "spam", "eggs")) ) - expected <- checkContigs(input) + expected <- .checkContigs(input) expect_true(is.list(expected)) expect_true(is.data.frame(expected[[1]])) expect_equal(expected[[1]]$a, c("x", NA, "z")) @@ -46,7 +46,7 @@ test_that("checkContigs works", { # Test cases test_that("Modifying barcodes without ID works correctly", { samples <- c("sample1", "sample2") - modified_data <- modifyBarcodes( + modified_data <- .modifyBarcodes( df = getdata("utils", "df_list"), samples = samples, ID = NULL ) @@ -67,7 +67,7 @@ test_that("Modifying barcodes without ID works correctly", { test_that("Modifying barcodes with ID works correctly", { samples <- c("sample3", "sample4") ID <- c("id1", "id2") - modified_data <- modified_data <- modifyBarcodes( + modified_data <- modified_data <- .modifyBarcodes( df = getdata("utils", "df_list"), samples = samples, ID = ID ) @@ -90,7 +90,7 @@ test_that("Modifying barcodes with ID works correctly", { test_that("filteringMulti works", { expect_identical( - filteringMulti(head(contig_list[[1]])), + .filteringMulti(head(contig_list[[1]])), getdata("utils", "filteringMulti_expected") ) }) @@ -111,7 +111,7 @@ test_that("filteringMulti works", { test_that("makeGenes works for cellType T", { expect_identical( - makeGenes("T", getdata("utils", "makeGenes_T_input")), + .makeGenes("T", getdata("utils", "makeGenes_T_input")), getdata("utils", "makeGenes_T_expected") ) }) diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R index 1ec5f91c..0eae97da 100644 --- a/tests/testthat/test-viz.R +++ b/tests/testthat/test-viz.R @@ -3,89 +3,86 @@ # really work properly for single samples # testdata: (assumes combineTCR works) -combined <- combineTCR( - contig_list, - samples = c("PY", "PY", "PX", "PX", "PZ","PZ"), - ID = c("P", "T", "P", "T", "P", "T") -) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) single_contig <- combineTCR(contig_list[[1]]) single_contig_with_sample <- combineTCR( - contig_list[[1]], samples = "PX", ID = "P" + contig_list[[1]], samples = "P17B", ) # TODO test more cases with single_contig -test_that("quantContig works", { +test_that("clonalQuant works", { expect_doppelganger( - "quantContig_scaled_plot", quantContig(combined, scale = TRUE) + "clonalQuant_scaled_plot", clonalQuant(combined, scale = TRUE) ) expect_equal( - quantContig(combined, scale = TRUE, exportTable = TRUE), + clonalQuant(combined, scale = TRUE, exportTable = TRUE), data.frame( - "contigs" = c(2712L, 1585L, 823L, 918L, 1143L, 768L), - "values" = c("PY_P", "PY_T", "PX_P", "PX_T", "PZ_P", "PZ_T"), - "total" = c(3208L, 3119L, 1068L, 1678L, 1434L, 2768L), + "contigs" = c(745L, 2117L, 1254L, 1202L, 5544L, 1619L, 6087L, 192L), + "values" = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"), + "total" = c(2805L, 2893L, 1328L, 1278L, 6942L, 2747L, 8991L, 201L), "scaled" = c( - 84.5386533665835, 50.8175697338891, 77.059925093633, - 54.7079856972586, 79.7071129707113, 27.7456647398844 + 26.55971, 73.17663, 94.42771, 94.05321, 79.86171, 58.93702, 67.70103, 95.52239 ) ) ) expect_doppelganger( - "quantContig_single_sample_plot", quantContig(single_contig) + "clonalQuant_single_sample_plot", clonalQuant(single_contig) ) expect_identical( - quantContig(single_contig, exportTable = TRUE), - data.frame("contigs" = 2712L, "total" = 3208L) + clonalQuant(single_contig, exportTable = TRUE), + data.frame("contigs" = 745L, "total" = 2805L) ) }) -test_that("abundanceContig works", { +test_that("clonalAbundance works", { expect_doppelganger( - "abundanceContig_scaled_plot", abundanceContig(combined, scale = FALSE) + "clonalAbundance_scaled_plot", clonalAbundance(combined, scale = FALSE) ) }) -test_that("lengthContig works", { +test_that("clonalLength works", { expect_doppelganger( - "lengthContig_both_chain_plot", lengthContig(combined, chain = "both") + "clonalLength_both_chain_plot", clonalLength(combined, chain = "both") ) }) -test_that("compareClonotypes works", { +test_that("clonalCompare works", { expect_doppelganger( - "compareClonotypes_alluvial_plot", - compareClonotypes( + "clonalCompare_alluvial_plot", + clonalCompare( combined, numbers = 10, - samples = c("PX_P", "PX_T"), + samples = c("P17B", "P17L"), cloneCall="aa", graph = "alluvial" ) ) expect_doppelganger( - "compareClonotypes_area_plot", - compareClonotypes( + "clonalCompare_area_plot", + clonalCompare( combined, numbers = 10, - samples = c("PX_P", "PX_T"), + samples = c("P17B", "P17L"), cloneCall="aa", graph = "area" ) ) }) -test_that("scatterClonotype works", { +test_that("clonalScatter works", { expect_doppelganger( - "scatterClonotype_vignette_plot", - scatterClonotype( + "clonalScatter_vignette_plot", + clonalScatter( combined, cloneCall ="gene", - x.axis = "PY_P", - y.axis = "PY_T", + x.axis = "P17B", + y.axis = "P17L", dot.size = "total", graph = "proportion", seed = 42 @@ -96,10 +93,10 @@ test_that("scatterClonotype works", { }) # something in `clonesizeDistribution` prints "NULL" to the terminal -test_that("clonesizeDistribution works", { +test_that("clonalSizeDistribution works", { expect_doppelganger( - "clonesizeDistribution_vignette_plot", - clonesizeDistribution(combined, cloneCall = "gene+nt", method="ward.D2") + "clonalSizeDistribution_vignette_plot", + clonalSizeDistribution(combined, cloneCall = "gene+nt", method="ward.D2") ) }) diff --git a/tests/testthat/testdata/combineContigs/combineTCR_list_expected.rds b/tests/testthat/testdata/combineContigs/combineTCR_list_expected.rds index 5d08d24f48bb15a5a5cd93a45d691cce670a16f1..869b4c0e46e2d19e730775721dbbdd637288cbba 100644 GIT binary patch literal 1344 zcmV-G1;6?qiwFP!000001LaxYYa2%pR{SGvQrk(ILPG*>0)>L{+1AOj<;V3(+9*0y z>25153R$kU6AFo4WI>_O$3P)ZrT=fznc2JOR@#;BE>ESlrTa0nySLwbGxzQK>~5t} zS*@(C!uL9yR@Q%#ZLd+OtUM$`WfhMb@VT!YD|g`dZNGYasu*LOOTmO>T)>}KwV62_ zz{t7FECjTc49w;rYv|@YH+{dBAshPjJvhAZ5AeVf%OkYZ8lJ!395%>a_p|ngXs`NS zm24;4WOn`)Jblz_j=DkAPRHYH6h>?S4=`^Tk5hIR4Zu9cmfn|FF?% zdC;7MD<02(+}tb~Xg8$`lHk3S`UQyL$dYo5SzCcex|KZI`c?e)@+Jx9Mso9uU*hrk z^~Gg!dHW`Q``erJYEAMmeS3a+elf18I5w}ZCe@6uKjZ7q_@>vlxqN+j^=T9?uO`#; zI8Iw}c{3URdOn$%$DQ%@?|bQ7Hnm&;qdu!e3MQ(y?OT{p2T~v#?S{`eORD2 zf~nddb~9$kiC`)xASe)0ZZ{`Vg~Z6b+6bKIrY~uR6Ji8pULz|f9-!&aqC?1_Z`oDG zea?a~97Qn0Xf&7#icKC2MZan4j}6ZH&m9`$Ky_C>Y4W; zK*V(cA*dHBl<$*aIOb$db)mI`?;U0nt7^QmNPrx9b+?D^qzdp`-kEclLOo`o5dBfF zHTYC%>Ha7RpNElgp9D!;4n(Nyw+&dG`Y<B<$`uQ z*-|4ZYP#rg>c*(Y=9BJQ2&$H2zdJ`>wKN*u9IsXW_{2r2s!E37EGM^B$x!{6CmB@f zcs`r04a!06ZKGHy2hE>ktA|?1`M<49DjYR8^~y%{orSA;W9;N~+3^2qNrGI2aA!&( zvf!Kc*2n*>qFDLp77(lHv`hG(c zIZE_s?RZWb)GVUGY*eq=bi+N~8A2V1B3LAJgHC1@JsgBH+ZJ2A1CHR_8_|}h~U0#f}K0wg{VYx|Ac~(C-7@ZJE7~?$JO9_zNv7H7`wONpq(;b3_Ge z99&_E(D|HM!DM9vPktyU&>jm3ceL}e%Qys?Q097lB50lBUd`^?XsVV%zdOfZwKN#} zHgc=n5&F3hTUD6|oaN-UDjBJ>{K=p`C#c`eln7-f_qI_ol!NNqW(`q`ng8C-r(&UV zvaeKB-&wetH_lE@m!-g8_9Q643unF*rOf|YL>QBI{RbkmCqVD_5B~xbNUf|s82|wN Cqn^6} literal 1192 zcmV;Z1XueXiwFP!000002HjZSZ{tJ|c9Zm=2Zi1pJaC``BqSsxN3o+`ip0y_Cf-Y% z3rRfqninizTM*J+iqc3Y@elCGf6Otn>y3Y{6WcsN(stLovup1+-^@4e<$cSt+E%C4 zw%Ui-wGMv*&pxxP)+0G#wW&SE-xFnM9bxD|S#Ilaw;U}K>1QslCT-)hl=e$3lhHq`mYfg9&Oot=k`%@4Xld-qQ zz6S#2c?2F#Flq$i!hs-Q9KV7(3L{ylf>Q);;r+k8$o zr?H7EO%xbZG-?v5wiXZ{N@3j%*SpUt0p@%YD_`Tk2E-82-ViRW$2 zad_sBgjmc+FkgDP?GOk~o>eDDLA%cWs11KPcN)27k({`MYz;iu zX;D^JVH9Pr zR~si~>*j3ToUOjT$KltTmA+qx!)lZCqe!pfaJO0icD2c!=xBZWwyRHd9Ov`{dT`nx zfBdW0(VafN9=v}YZKCckYup2`{@x*%kP6?{rt=g$^r*=OTZjK5*Oz~MAkA?Fr6GDzLUw=ed=mNxK3M1xu zR=$A1*$KKV2$s{)+{@>v?^5xIE@v`H$Ws#xGKI(krcY%MJph>+xQt|(EkJ>&HU>iu zwhMj^cj1$+q0$_{d{dCQDzJ@ryF09bi`wwpD$GcpHm_n05d19Rd6*-?wsuNrpWIqY z*A7~|H8^JvUgf^4kL3?VH&;YAIb}wRjErc2KPBS>9oPS5`Dwk&sZBdko3=h+U4gmG zymFzVAoa?XnnbV5A5k(X3m{eym`h`?#`~BWc$gyBX)moXa^}odynnh&Vle5#aIpY? zIlKtQUNRcUV#Z&NXHX_ID&+zlQx#kgR~Hf>T10SwXbK1zcB4DsXTGFs|C& z(lf|}XZ|hWv5H`)JH1`q#1@0%+uW(DmBrmDiwGubaO#^DWgo3u>Nf0wB~Z2PF8wC*3j-|nwX G6#xL7C`9N0 diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index b75ad385..77fcc394 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -238,28 +238,7 @@ clonalQuant(combined, scale = TRUE) ``` -## checkContig -Depending on the experiment or sequencing run, a number of NAs can appear in either the sequence or VDJC gene usage. A quick way to check the percentage of NA values is to use ```checkContigs()``` - -**chain** -+ "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" - -**examine** -+ "gene" - examining the VDJC NA values -+ "chain" = examining the sequence-specific NA values - -```{r} -checkContig(combined, - chain = "TRA", - examine = "gene", - group.by = "Type") - -checkContig(combined, - chain = "TRB", - examine = "chain", - group.by = "sample") -``` ## abundanceContig @@ -481,9 +460,9 @@ After completing the basic processing and summary functions in scRepertoire, we ## clonalHomeostasis -By examining the clonal space, we effectively look at the relative space occupied by clones at specific proportions. Another way to think about this would be to think of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different numbers of clonal proportions. Clonal space homeostasis asks what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cutpoints are set under the **cloneType** variable in the function and can be adjusted. +By examining the clonal space, we effectively look at the relative space occupied by clones at specific proportions. Another way to think about this would be to think of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different numbers of clonal proportions. Clonal space homeostasis asks what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cutpoints are set under the **cloneSize** variable in the function and can be adjusted. -**cloneTypes** +**cloneSize** * Rare = .0001 * Small = .001 * Medium = .01 @@ -637,7 +616,7 @@ Next, we can attach the clonotypic information to our Seurat object using the `c We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 amino acid sequence, 3) CDR3 nucleotide sequence, or 4) VDJC genes and CDR3 nucleotide sequence. The attaching function will also calculate the clonotype frequency based on the **group.by** variable. If blank, **group.by** will calculate frequencies of clonotypes by individual run, but because we have 8 samples of paired peripheral blood and lung T cells, we will use the group.by variable to call "sample" to calculate frequencies across the same patient's blood and lung T cells. -In order to categorize the frequency, we have the variable **proportion**, which if TRUE, allows for the relative proportion or when FALSE, will use absolute frequency to define clonotype groups **cloneTypes** acts as a bin to place labels. As a default, **cloneTypes** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below is an example of using total frequency as expansion assignments. +In order to categorize the frequency, we have the variable **proportion**, which if TRUE, allows for the relative proportion or when FALSE, will use absolute frequency to define clonotype groups **cloneSize** acts as a bin to place labels. As a default, **cloneSize** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below is an example of using total frequency as expansion assignments. ```{r} sce <- combineExpression(combined, @@ -651,7 +630,7 @@ scRep_example <- combineExpression(combined, cloneCall="gene", group.by = "sample", proportion = FALSE, - cloneTypes=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500)) + cloneSize=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500)) ``` ## Combining both TCR and BCR @@ -686,7 +665,7 @@ Seurat::DimPlot(scRep_example, group.by = "Type") + Seurat::NoLegend() + scale_color_manual(values=colorblind_vector[c(1,6)]) -Seurat::DimPlot(scRep_example, group.by = "cloneType") + +Seurat::DimPlot(scRep_example, group.by = "cloneSize") + scale_color_manual(values=colorblind_vector[c(2,4,6)]) ``` @@ -764,18 +743,18 @@ scRep_example <- highlightClones(scRep_example, Seurat::DimPlot(scRep_example, group.by = "highlight") ``` -## occupiedscRepertoire +## clonalOccupy -We can also look at the count of cells by cluster assigned into specific frequency ranges by using the `occupiedscRepertoire()` function and selecting the **x.axis** to display cluster or other variables in the meta data of the single cell object. +We can also look at the count of cells by cluster assigned into specific frequency ranges by using the `clonalOccupy()` function and selecting the **x.axis** to display cluster or other variables in the meta data of the single cell object. + **proportion** can be used to look at relative level groupings + **label** will still return the absolute number of clonotypes ```{r} -occupiedscRepertoire(scRep_example, +clonalOccupy(scRep_example, x.axis = "seurat_clusters") -occupiedscRepertoire(scRep_example, +clonalOccupy(scRep_example, x.axis = "ident", proportion = TRUE, label = FALSE) From 4ced5c4e08d972e2d77c1e6dd75a8aa30de3d293 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 2 Oct 2023 10:09:22 -0500 Subject: [PATCH 068/142] clarify group.by and split.by --- DESCRIPTION | 3 ++- NEWS | 1 + R/abundanceContig.R | 9 +++------ R/clonalBias.R | 7 +++---- R/clonalCompare.R | 6 +++--- R/clonalDiversity.R | 8 +++----- R/clonalHomeostasis.R | 11 ++++------- R/clonalLength.R | 10 +++------- R/clonalOverlap.R | 7 +++---- R/clonalProportion.R | 10 ++++------ R/clonalQuant.R | 9 +++++---- R/clonalRarefaction.R | 13 ++++++++----- R/clonalScatter.R | 14 ++++---------- R/clonalSizeDistribution.R | 11 ++++------- R/exportClones.R | 7 ++----- R/percentAA.R | 13 ++++++++----- R/percentGenes.R | 11 ++++++----- R/percentVJ.R | 24 +++++++++++++----------- R/utils.R | 2 +- R/vizGenes.R | 10 ++++------ man/abundanceContig.Rd | 6 +----- man/clonalBias.Rd | 6 +++--- man/clonalCompare.Rd | 4 ++-- man/clonalDiversity.Rd | 6 +----- man/clonalHomeostasis.Rd | 6 +----- man/clonalLength.Rd | 7 +------ man/clonalOccupy.Rd | 6 +++--- man/clonalOverlap.Rd | 5 ++--- man/clonalProportion.Rd | 6 +----- man/clonalQuant.Rd | 4 ---- man/clonalRarefaction.Rd | 6 +----- man/clonalScatter.Rd | 10 +++------- man/clonalSizeDistribution.Rd | 6 +----- man/exportClones.Rd | 6 +----- man/percentAA.Rd | 6 +----- man/percentGenes.Rd | 6 +----- man/percentVJ.Rd | 6 +----- man/vizGenes.Rd | 6 +----- vignettes/vignette.Rmd | 14 +++++--------- 39 files changed, 114 insertions(+), 194 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index e969b944..d1c8566a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -36,7 +36,8 @@ Imports: truncdist, VGAM, cubature, - iNEXT + iNEXT, + grDevices Suggests: knitr, rmarkdown, diff --git a/NEWS b/NEWS index 783d6712..c97a7a00 100644 --- a/NEWS +++ b/NEWS @@ -22,6 +22,7 @@ UNDERLYING CHANGES * Universalized underlying function language and intermediate variables * Added features to clonalCompare to allow for highlighting sequences, relabeling clonotypes. * License change to MIT +* group.by and split.by have been consolidated into single group.by parameter DEPRECATED AND DEFUNCT diff --git a/R/abundanceContig.R b/R/abundanceContig.R index ec953f23..7910f291 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -21,9 +21,7 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column header for which you would like to analyze the data. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping #' @param order Maintain the order of the list when plotting #' @param scale Converts the graphs into density plots in order to show #' relative distributions. @@ -39,15 +37,14 @@ abundanceContig <- function(df, chain = "both", scale=FALSE, group.by = NULL, - split.by = NULL, order = TRUE, exportTable = FALSE, palette = "inferno") { - df <- .list.input.return(df,split.by) + df <- .list.input.return(df,group.by) Con.df <- NULL xlab <- "Abundance" cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, group.by, cloneCall, chain) names <- names(df) if (!is.null(group.by)) { diff --git a/R/clonalBias.R b/R/clonalBias.R index 598232ad..82545e5d 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -35,9 +35,9 @@ #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). -#' @param group.by The column header used for comparisons of bias. -#' @param split.by The column header used for calculating the baseline frequencies. -#' For example, "Type" for tumor vs peripheral blood comparison +#' @param group.by The variable to use for calculating bias +#' @param split.by The variable to use for calculating the baseline frequencies. +#' For example, "Type" for lung vs peripheral blood comparison #' @param n.boots number of bootstraps to downsample #' @param min.expand clonotype frequency cut off for the purpose of comparison #' @param exportTable Returns the data frame used for forming the graph @@ -170,7 +170,6 @@ get_clono_bias <- function(df, expanded <- df[[s]][which(df[[s]][,cloneCall] %in% names(clones)), c(group.by, cloneCall)] if (do.shuffle) { #reshuffle annotation column - set.seed(seed) expanded[[group.by]] <- sample(expanded[[group.by]]) } diff --git a/R/clonalCompare.R b/R/clonalCompare.R index b000eab0..1cde2d4d 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -30,7 +30,7 @@ #' all other clones returned will be grey. #' @param relabel.clones Simplify the legend of the graph by returning #' clones that are numerically indexed. -#' @param split.by If using a single-cell object, the column header +#' @param group.by If using a single-cell object, the column header #' to group the new list. NULL will return clusters. #' @param graph The type of graph produced, either "alluvial" or "area". #' @param exportTable Returns the data frame used for forming the graph. @@ -48,7 +48,7 @@ clonalCompare <- function(df, top.clones = NULL, highlight.clones = NULL, relabel.clones = FALSE, - split.by = NULL, + group.by = NULL, graph = "alluvial", exportTable = FALSE, palette = "inferno"){ @@ -58,7 +58,7 @@ clonalCompare <- function(df, top.clones <- NULL } cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, group.by, cloneCall, chain) Con.df <- NULL #Loop through the list to get a proportional summary diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 01c570d9..69e5e201 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -46,8 +46,7 @@ #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by Variable in which to group the diversity calculation #' @param x.axis Additional variable in which to split the x.axis -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for goruping #' @param metrics The indices to use in diversity calculations - "shannon", "inv.simpson", #' "norm.entropy", "gini.simpson", "chao1", "ACE" #' @param exportTable Exports a table of the data into the global environment @@ -69,7 +68,6 @@ clonalDiversity <- function(df, chain = "both", group.by = NULL, x.axis = NULL, - split.by = NULL, metrics = c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE"), exportTable = FALSE, palette = "inferno", @@ -80,7 +78,7 @@ clonalDiversity <- function(df, exportTable <- TRUE } cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, group.by, cloneCall, chain) mat <- NULL sample <- c() if (!is.null(group.by) || !is.null(x.axis)) { @@ -228,7 +226,7 @@ clonalDiversity <- function(df, # Calculate gamma gamma <- 0 - for(i in 1:q) { + for(i in seq_len(q)) { f_i <- sum(rare_data == i) gamma <- gamma + (1 - i / q)^f_i } diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 2d2e19be..2d09fad7 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -22,9 +22,7 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization #' @param palette Colors to use in visualization - input any hcl.pals() @@ -40,15 +38,14 @@ clonalHomeostasis <- function(df, cloneCall = "strict", chain = "both", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno") { cloneSize <- c(None = 0, cloneSize) cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) - - if(!is.null(group.by)) { + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, cloneCall, chain) + if(!is.null(group.by) & !sco) { df <- .groupList(df, group.by) } diff --git a/R/clonalLength.R b/R/clonalLength.R index 1c460af4..9eb23d25 100644 --- a/R/clonalLength.R +++ b/R/clonalLength.R @@ -19,10 +19,7 @@ #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), #' CDR3 amino acid (aa). -#' @param group.by The group header for which you would like to analyze -#' the data. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param scale Converts the graphs into density plots in order to show #' relative distributions. #' @param order Maintain the order of the list when plotting @@ -39,14 +36,13 @@ clonalLength <- function(df, cloneCall = "aa", chain = "both", group.by = NULL, - split.by = NULL, order = TRUE, scale = FALSE, exportTable = FALSE, palette = "inferno") { cloneCall <- .theCall(cloneCall) - df <- .list.input.return(df, split.by) + df <- .list.input.return(df, group.by) #Sorting out graphing parameters xlab <- "Length" @@ -61,7 +57,7 @@ clonalLength <- function(df, #Identifying and assigning chains chain.pos <- which(colnames(df[[1]]) == "cdr3_aa1")-1 - c1 <- na.omit(unique(substr(df[[1]][1:10,chain.pos], 1,3))) + c1 <- na.omit(unique(substr(df[[1]][seq_len(10),chain.pos], 1,3))) c2 <- switch(c1, "TRA" = "TRB", "IGH" = "IGL", diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 6997e0b0..aa3854c2 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -41,8 +41,7 @@ #' @param method The method to calculate the "overlap", # "morisita", "jaccard" indices, "cosine" similarity or "raw" #' for the base numbers. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping #' @param exportTable Returns the data frame used for forming the graph #' @param palette Colors to use in visualization - input any hcl.pals() #' @importFrom stringr str_sort str_to_title @@ -54,7 +53,7 @@ clonalOverlap <- function(df, cloneCall = "strict", method = NULL, chain = "both", - split.by = NULL, + group.by = NULL, exportTable = FALSE, palette = "inferno"){ if(method == "morisita") { @@ -63,7 +62,7 @@ clonalOverlap <- function(df, return_type = "unique" } cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, group.by, cloneCall, chain) df <- df[order(names(df))] values <- str_sort(as.character(unique(names(df))), numeric = TRUE) df <- df[.quiet(dput(values))] diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 4b08ff49..10ac5059 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -22,9 +22,7 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization #' @param palette Colors to use in visualization - input any hcl.pals() @@ -41,13 +39,13 @@ clonalProportion <- function(df, cloneCall = "strict", chain = "both", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno") { Con.df <- NULL cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) - if(!is.null(group.by)) { + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, cloneCall, chain) + if(!is.null(group.by) & !sco) { df <- .groupList(df, group.by) } diff --git a/R/clonalQuant.R b/R/clonalQuant.R index b1f58cb8..08fd2332 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -19,8 +19,6 @@ #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. #' @param order Maintain the order of the list when plotting #' @param scale Converts the graphs into percentage of unique clonotypes. #' @param exportTable Returns the data frame used for forming the graph. @@ -33,7 +31,6 @@ clonalQuant <- function(df, chain = "both", scale=FALSE, group.by = NULL, - split.by = NULL, order = TRUE, exportTable = FALSE, palette = "inferno") { @@ -43,7 +40,11 @@ clonalQuant <- function(df, } cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTgene", chain) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } mat.names <- c("contigs","values", "total", group.by) #Set up mat to store and selecting graph parameters diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index ff36e999..d20bd69d 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -23,9 +23,7 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by Variable in which to group the diversity calculation -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping #' @param plot.type sample-size-based rarefaction/extrapolation curve (\code{type = 1}); #' sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}). #' @param hill.numbers The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson) @@ -41,14 +39,19 @@ clonalRarefaction <- function(df, cloneCall = "strict", chain = "both", group.by = NULL, - split.by = NULL, plot.type = 1, hill.numbers = 0, n.boots = 20, exportTable = FALSE, palette = "inferno") { cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTgene", chain) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } + mat.list <- lapply(df, function(x) { table(x[,cloneCall]) }) diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 2b38f1e1..1f1df53e 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -26,12 +26,10 @@ #' @param y.axis name of the list element to appear on the y.axis #' @param dot.size either total or the name of the list element to #' use for size of dots -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping #' @param graph graph either proportion or raw clonotype count #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any hcl.pals() -#' @param seed the integer seed to set for the random variation of point coords. #' #' @import ggplot2 #' @@ -44,16 +42,12 @@ clonalScatter <- function(df, y.axis = NULL, chain = "both", dot.size = "total", - split.by = NULL, + group.by = NULL, graph = "proportion", exportTable = FALSE, - palette = "inferno", - seed = NULL) { - if (!is.null(seed)) { - set.seed(seed) - } + palette = "inferno") { cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) + df <- .data.wrangle(df, group.by, cloneCall, chain) axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) #Making new data frame and adding variables to graph diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index e09c1abb..0846ef79 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -24,9 +24,7 @@ #' @param threshold Numerical vector containing the thresholds #' the grid search was performed over. #' @param method The clustering parameter for the dendrogram. -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 @@ -44,14 +42,13 @@ clonalSizeDistribution <- function(df, method = "ward.D2", threshold = 1, group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno") { x <- xend <- yend <- mpg_div_hp <- NULL cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, split.by, cloneCall, chain) - - if(!is.null(group.by)) { + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTgene", chain) + if(!is.null(group.by) & !sco) { df <- .groupList(df, group.by) } data <- bind_rows(df) diff --git a/R/exportClones.R b/R/exportClones.R index 82376026..91a49b18 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -14,9 +14,7 @@ #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param write.file TRUE, save the file, FALSE, return a data.frame #' @param dir directory location to save the csv #' @param file.name the csv file name @@ -28,12 +26,11 @@ #' @author Jonathan Noonan, Nick Borcherding exportClones <- function(df, group.by = NULL, - split.by = NULL, write.file = TRUE, dir = NULL, file.name = "clones.csv") { - df <- .data.wrangle(df, split.by, "CTgene", "both") + df <- .data.wrangle(df, group.by, "CTgene", "both") df <- bind_rows(df, .id = "group") diff --git a/R/percentAA.R b/R/percentAA.R index 9b8b4ed9..78382097 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -13,9 +13,7 @@ #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param aa.length The maximum length of the cdr3 amino acid sequence #' @param exportTable Returns the data frame used for forming the graph #' @param palette Colors to use in visualization - input any hcl.pals() @@ -26,11 +24,16 @@ percentAA <- function(df, chain = "TRB", group.by = NULL, - split.by = NULL, aa.length = 20, exportTable = FALSE, palette = "inferno") { - df <- .data.wrangle(df, split.by, "CTaa", chain) + + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTaa", chain) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } + res.list <- list() for (i in seq_along(df)) { strings <- df[[i]][,"CTaa"] diff --git a/R/percentGenes.R b/R/percentGenes.R index 4aa8c736..a4add4ac 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -14,9 +14,7 @@ #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" #' @param gene "V", "D" or "J" -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 @@ -29,11 +27,14 @@ percentGenes <- function(df, chain = "TRB", gene = "Vgene", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno") { - df <- .data.wrangle(df, split.by, "CTgene", chain) + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTgene", chain) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } #Parsing gene input if (gene %in% c("Vgene", "V", "v", "v.gene")) { gene.loci <- paste0(chain, "V") diff --git a/R/percentVJ.R b/R/percentVJ.R index 3aefeae8..2bebe9c1 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -13,9 +13,7 @@ #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 @@ -25,12 +23,16 @@ #' @return ggplot of percentage of V and J gene pairings as a heatmap #' percentVJ <- function(df, - chain = "TRB", - group.by = NULL, - split.by = NULL, - exportTable = FALSE, - palette = "inferno") { - df <- .data.wrangle(df, split.by, "CTgene", chain) + chain = "TRB", + group.by = NULL, + exportTable = FALSE, + palette = "inferno") { + + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTgene", chain) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } if(chain %in% c("TRA", "TRG", "IGL")) { positions <- c(1,2) @@ -48,8 +50,8 @@ percentVJ <- function(df, #Need total unique genes gene.dictionary <- unique(unlist(gene_counts)) coordinates <- strsplit(gene.dictionary, ";") - V_values <- unique(sapply(coordinates, function(coord) coord[1])) - J_values <- unique(sapply(coordinates, function(coord) coord[2])) + V_values <- unique(unlist(lapply(coordinates, function(coord) coord[1]))) + J_values <- unique(unlist(lapply(coordinates, function(coord) coord[2]))) #Summarizing the gene usage across the list summary <- lapply(gene_counts, function(x) { diff --git a/R/utils.R b/R/utils.R index a7d09d39..cf4f2ffc 100644 --- a/R/utils.R +++ b/R/utils.R @@ -59,7 +59,7 @@ is_seurat_or_se_object <- function(obj) { .checkList <- function(df) { df <- tryCatch( { - if (is(df)[1] != "list") { + if (!inherits(df, "list")) { df <- list(df) } df diff --git a/R/vizGenes.R b/R/vizGenes.R index 0a556655..b4616263 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -25,9 +25,7 @@ #' @param order Categorical variable to organize the x-axis, either "gene" or "variance" #' @param scale Converts the individual count of genes to proportion using the total #' respective repertoire size -#' @param group.by The column header used for grouping. -#' @param split.by If using a single-cell object, the column header -#' to group the new list. NULL will return clusters. +#' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 @@ -45,12 +43,12 @@ vizGenes <- function(df, order = "gene", scale = TRUE, group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno") { element.names <- NULL - df <- .list.input.return(df, split.by = split.by) - if(!is.null(group.by)) { + sco <- is_seurat_object(df) | is_se_object(df) + df <- .list.input.return(df, split.by = group.by) + if(!is.null(group.by) & !sco) { df <- .groupList(df, group.by) } for(i in seq_along(df)) { diff --git a/man/abundanceContig.Rd b/man/abundanceContig.Rd index c678963f..91d82c66 100644 --- a/man/abundanceContig.Rd +++ b/man/abundanceContig.Rd @@ -10,7 +10,6 @@ abundanceContig( chain = "both", scale = FALSE, group.by = NULL, - split.by = NULL, order = TRUE, exportTable = FALSE, palette = "inferno" @@ -30,10 +29,7 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{scale}{Converts the graphs into density plots in order to show relative distributions.} -\item{group.by}{The column header for which you would like to analyze the data.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping} \item{order}{Maintain the order of the list when plotting} diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index 2639cc7a..e3ed5120 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -22,10 +22,10 @@ clonalBias( CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} -\item{split.by}{The column header used for calculating the baseline frequencies. -For example, "Type" for tumor vs peripheral blood comparison} +\item{split.by}{The variable to use for calculating the baseline frequencies. +For example, "Type" for lung vs peripheral blood comparison} -\item{group.by}{The column header used for comparisons of bias.} +\item{group.by}{The variable to use for calculating bias} \item{n.boots}{number of bootstraps to downsample} diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index 4e2ba184..671795a7 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -13,7 +13,7 @@ clonalCompare( top.clones = NULL, highlight.clones = NULL, relabel.clones = FALSE, - split.by = NULL, + group.by = NULL, graph = "alluvial", exportTable = FALSE, palette = "inferno" @@ -42,7 +42,7 @@ all other clones returned will be grey.} \item{relabel.clones}{Simplify the legend of the graph by returning clones that are numerically indexed.} -\item{split.by}{If using a single-cell object, the column header +\item{group.by}{If using a single-cell object, the column header to group the new list. NULL will return clusters.} \item{graph}{The type of graph produced, either "alluvial" or "area".} diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index 0d3e3466..ae6e5206 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -10,7 +10,6 @@ clonalDiversity( chain = "both", group.by = NULL, x.axis = NULL, - split.by = NULL, metrics = c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE"), exportTable = FALSE, palette = "inferno", @@ -30,13 +29,10 @@ VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{Variable in which to group the diversity calculation} +\item{group.by}{The variable to use for goruping} \item{x.axis}{Additional variable in which to split the x.axis} -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} - \item{metrics}{The indices to use in diversity calculations - "shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE"} diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index 58a23894..e70b6e62 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -11,7 +11,6 @@ clonalHomeostasis( cloneCall = "strict", chain = "both", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -29,10 +28,7 @@ VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} diff --git a/man/clonalLength.Rd b/man/clonalLength.Rd index 36ab365f..dc45ce98 100644 --- a/man/clonalLength.Rd +++ b/man/clonalLength.Rd @@ -9,7 +9,6 @@ clonalLength( cloneCall = "aa", chain = "both", group.by = NULL, - split.by = NULL, order = TRUE, scale = FALSE, exportTable = FALSE, @@ -26,11 +25,7 @@ CDR3 amino acid (aa).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{The group header for which you would like to analyze -the data.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{order}{Maintain the order of the list when plotting} diff --git a/man/clonalOccupy.Rd b/man/clonalOccupy.Rd index 502bdfea..a7b65444 100644 --- a/man/clonalOccupy.Rd +++ b/man/clonalOccupy.Rd @@ -54,10 +54,10 @@ combined <- combineTCR(contig_list, scRep_example <- get(data("scRep_example")) #Using combineExpresion() -sce <- combineExpression(combined, sce) +scRep_example <- combineExpression(combined, scRep_example) #Using clonalOccupy -clonalOccupy(sce, x.axis = "ident") -table <- clonalOccupy(sce, x.axis = "ident", exportTable = TRUE) +clonalOccupy(scRep_example, x.axis = "ident") +table <- clonalOccupy(scRep_example, x.axis = "ident", exportTable = TRUE) } diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index ab856b5c..bdc5b871 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -9,7 +9,7 @@ clonalOverlap( cloneCall = "strict", method = NULL, chain = "both", - split.by = NULL, + group.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -28,8 +28,7 @@ for the base numbers.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping} \item{exportTable}{Returns the data frame used for forming the graph} diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index e2cd4130..f736592e 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -10,7 +10,6 @@ clonalProportion( cloneCall = "strict", chain = "both", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -28,10 +27,7 @@ VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} diff --git a/man/clonalQuant.Rd b/man/clonalQuant.Rd index 27ff330f..ed010ae0 100644 --- a/man/clonalQuant.Rd +++ b/man/clonalQuant.Rd @@ -10,7 +10,6 @@ clonalQuant( chain = "both", scale = FALSE, group.by = NULL, - split.by = NULL, order = TRUE, exportTable = FALSE, palette = "inferno" @@ -31,9 +30,6 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{group.by}{The column header used for grouping.} -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} - \item{order}{Maintain the order of the list when plotting} \item{exportTable}{Returns the data frame used for forming the graph.} diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd index 2b968e75..f251859d 100644 --- a/man/clonalRarefaction.Rd +++ b/man/clonalRarefaction.Rd @@ -9,7 +9,6 @@ clonalRarefaction( cloneCall = "strict", chain = "both", group.by = NULL, - split.by = NULL, plot.type = 1, hill.numbers = 0, n.boots = 20, @@ -28,10 +27,7 @@ VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{Variable in which to group the diversity calculation} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping} \item{plot.type}{sample-size-based rarefaction/extrapolation curve (\code{type = 1}); sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}).} diff --git a/man/clonalScatter.Rd b/man/clonalScatter.Rd index 1a7f4e0a..03b10f30 100644 --- a/man/clonalScatter.Rd +++ b/man/clonalScatter.Rd @@ -11,11 +11,10 @@ clonalScatter( y.axis = NULL, chain = "both", dot.size = "total", - split.by = NULL, + group.by = NULL, graph = "proportion", exportTable = FALSE, - palette = "inferno", - seed = NULL + palette = "inferno" ) } \arguments{ @@ -36,16 +35,13 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{dot.size}{either total or the name of the list element to use for size of dots} -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping} \item{graph}{graph either proportion or raw clonotype count} \item{exportTable}{Returns the data frame used for forming the graph.} \item{palette}{Colors to use in visualization - input any hcl.pals()} - -\item{seed}{the integer seed to set for the random variation of point coords.} } \value{ ggplot of the relative clonotype numbers diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index 1bb3320c..fe2efb90 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -12,7 +12,6 @@ clonalSizeDistribution( method = "ward.D2", threshold = 1, group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -33,10 +32,7 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{threshold}{Numerical vector containing the thresholds the grid search was performed over.} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping} \item{exportTable}{Returns the data frame used for forming the graph.} diff --git a/man/exportClones.Rd b/man/exportClones.Rd index 72ffd34e..15ba0c6f 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -7,7 +7,6 @@ exportClones( df, group.by = NULL, - split.by = NULL, write.file = TRUE, dir = NULL, file.name = "clones.csv" @@ -17,10 +16,7 @@ exportClones( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{write.file}{TRUE, save the file, FALSE, return a data.frame} diff --git a/man/percentAA.Rd b/man/percentAA.Rd index 75146fbf..11d2a61f 100644 --- a/man/percentAA.Rd +++ b/man/percentAA.Rd @@ -8,7 +8,6 @@ percentAA( df, chain = "TRB", group.by = NULL, - split.by = NULL, aa.length = 20, exportTable = FALSE, palette = "inferno" @@ -20,10 +19,7 @@ percentAA( \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{aa.length}{The maximum length of the cdr3 amino acid sequence} diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd index ad321210..215fefdd 100644 --- a/man/percentGenes.Rd +++ b/man/percentGenes.Rd @@ -9,7 +9,6 @@ percentGenes( chain = "TRB", gene = "Vgene", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -22,10 +21,7 @@ percentGenes( \item{gene}{"V", "D" or "J"} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{exportTable}{Returns the data frame used for forming the graph} diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd index 2ca9a38a..80e7d2f3 100644 --- a/man/percentVJ.Rd +++ b/man/percentVJ.Rd @@ -8,7 +8,6 @@ percentVJ( df, chain = "TRB", group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -19,10 +18,7 @@ percentVJ( \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{exportTable}{Returns the data frame used for forming the graph} diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index a7352a14..b7da4749 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -13,7 +13,6 @@ vizGenes( order = "gene", scale = TRUE, group.by = NULL, - split.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -37,10 +36,7 @@ gene segments such as V, D, J, or C.} \item{scale}{Converts the individual count of genes to proportion using the total respective repertoire size} -\item{group.by}{The column header used for grouping.} - -\item{split.by}{If using a single-cell object, the column header -to group the new list. NULL will return clusters.} +\item{group.by}{The variable to use for grouping.} \item{exportTable}{Returns the data frame used for forming the graph.} diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 77fcc394..dcc4b58f 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -535,16 +535,12 @@ With the former two generally used to estimate baseline diversity and Chao/ACE i ```{r} clonalDiversity(combined, cloneCall = "gene", - group.by = "sample", - n.boots = 100) - - -clonalDiversity(combined, cloneCall = "gene") + n.boots = 50) ``` ## clonaRarefaction -We can also use Hill numbers to estimate the rarefaction using the the abundance of clones across groupings. This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). +We can also use Hill numbers to estimate the rarefaction using the the abundance of clones across groupings. This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). Like the other wrapping functions in scRepetoire, please cite the original work. **plot.type** + 1 - sample-size-based rarefaction/extrapolation @@ -666,7 +662,7 @@ Seurat::DimPlot(scRep_example, group.by = "Type") + scale_color_manual(values=colorblind_vector[c(1,6)]) Seurat::DimPlot(scRep_example, group.by = "cloneSize") + - scale_color_manual(values=colorblind_vector[c(2,4,6)]) + scale_color_manual(values=rev(colorblind_vector[c(2,4,6)])) ``` ## clonalOverlay @@ -740,7 +736,7 @@ scRep_example <- highlightClones(scRep_example, sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF")) -Seurat::DimPlot(scRep_example, group.by = "highlight") +Seurat::DimPlot(scRep_example, group.by = "highlight") ``` ## clonalOccupy @@ -769,7 +765,7 @@ alluvialClonotypes(scRep_example, cloneCall = "aa", y.axes = c("Patient", "ident", "Type"), color = "CVVSDNTGGFKTIF_CASSVRRERANTGELFF") + - scale_fill_manual(values = c("grey", colorblind_vector[7])) + scale_fill_manual(values = c("grey", colorblind_vector[3])) alluvialClonotypes(scRep_example, From 4941e5a36338489ffd3bb6306e54ed507db54d69 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 3 Oct 2023 09:07:35 -0500 Subject: [PATCH 069/142] Cleaning combineExpression() --- R/clonalOccupy.R | 2 +- R/clonalOverlay.R | 2 +- R/combineExpression.R | 118 +++++++++++++++++++++--------------------- 3 files changed, 62 insertions(+), 60 deletions(-) diff --git a/R/clonalOccupy.R b/R/clonalOccupy.R index ad2c40cf..bf09398a 100644 --- a/R/clonalOccupy.R +++ b/R/clonalOccupy.R @@ -48,7 +48,7 @@ clonalOccupy <- function(sc, palette = "inferno") { .checkSingleObject(sc) meta <- .grabMeta(sc) - meta <- melt(table(meta[!is.na(meta$Frequency), + meta <- melt(table(meta[!is.na(meta[,"clonalFrequency"]), c(x.axis, facet.by, "cloneSize")], useNA = "ifany")) #Check for NAs if (!na.include) { diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index c1333b87..ad3b8f64 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -51,7 +51,7 @@ clonalOverlay <- function(sc, facet.by <- tmp[,facet.by] tmp <- data.frame(facet.by, tmp) } - tmp$include <- ifelse(tmp$Frequency >= freq.cutpoint, "Yes", NA) + tmp$include <- ifelse(tmp[,"clonalFrequency"] >= freq.cutpoint, "Yes", NA) tmp2 <- subset(tmp, include == "Yes") #Plotting diff --git a/R/combineExpression.R b/R/combineExpression.R index c858e718..eda90381 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -64,98 +64,96 @@ combineExpression <- function(df, call_time <- Sys.time() options( dplyr.summarise.inform = FALSE ) + if (!proportion & any(cloneSize < 1)) { + stop("Adjust the cloneSize parameter - there are groupings < 1") + } cloneSize <- c(None = 0, cloneSize) + if (chain != "both") { + df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) + } df <- .checkList(df) cloneCall <- .theCall(cloneCall) + + #Getting Summaries of clones from combineTCR() or combineBCR() Con.df <- NULL meta <- .grabMeta(sc) cell.names <- rownames(meta) if (group.by == "none" | !is.null(group.by)) { for (i in seq_along(df)) { - if (chain != "both") { - df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) - } + data <- data.frame(df[[i]], stringsAsFactors = FALSE) data2 <- unique(data[,c("barcode", cloneCall)]) data2 <- na.omit(data2[data2[,"barcode"] %in% cell.names,]) - if (proportion) { - data2 <- data2 %>% group_by(data2[,cloneCall]) %>% - summarise(Frequency = n()/nrow(data2)) - } else { - data2 <- data2 %>% group_by(data2[,cloneCall]) %>% - summarise(Frequency = n()) - } + data2 <- data2 %>% + group_by(data2[,cloneCall]) %>% + summarise(clonalProportion = n()/nrow(data2), + clonalFrequency = n()) colnames(data2)[1] <- cloneCall data <- merge(data, data2, by = cloneCall, all = TRUE) data <- data[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency")] + "CTaa", "CTstrict", "clonalProportion", + "clonalFrequency")] Con.df <- rbind.data.frame(Con.df, data) } - } else if (group.by != "none" | is.null(group.by)) { + } else if (group.by != "none" | !is.null(group.by)) { data <- data.frame(bind_rows(df), stringsAsFactors = FALSE) data2 <- na.omit(unique(data[,c("barcode", cloneCall, group.by)])) data2 <- data2[data2[,"barcode"] %in% cell.names, ] - data2 <- as.data.frame(data2 %>% group_by(data2[,cloneCall], - data2[,group.by]) %>% summarise(Frequency = n())) - if(!is.null(group.by)) { - colnames(data2)[c(1,2)] <- c(cloneCall, group.by) - x <- unique(data[,group.by]) - for (i in seq_along(x)) { - sub1 <- subset(data, data[,group.by] == x[i]) - sub2 <- subset(data2, data2[,group.by] == x[i]) - merge <- merge(sub1, sub2, by=cloneCall) - if (proportion) { - merge$Frequency <- merge$Frequency/length(merge$Frequency) - } - Con.df <- rbind.data.frame(Con.df, merge) - } - nsize <- Con.df %>% group_by(Con.df[,paste0(group.by, ".x")]) %>% summarise(n = n()) - } else { - if (proportion) { - data <- data %>% - group_by(data[,cloneCall]) %>% - mutate(Frequency = n()/nrow(data)) - } else { - data <- data %>% - group_by(data[,cloneCall]) %>% - mutate(Frequency = n()) - } - Con.df <- data[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency")] - nsize <- length(Con.df) - } + data2 <- as.data.frame(data2 %>% + group_by(data2[,cloneCall], data2[,group.by]) %>% + summarise(clonalProportion = n()/nrow(data2), + clonalFrequency = n()) + ) - } + colnames(data2)[c(1,2)] <- c(cloneCall, group.by) + data <- merge(data, data2, by = cloneCall, all = TRUE) + Con.df <- data[,c("barcode", "CTgene", "CTnt", + "CTaa", "CTstrict", "clonalProportion", + "clonalFrequency")] + } + #Creating the bins for cloneSize Con.df$cloneSize <- NA - for (x in seq_along(cloneSize)) { names(cloneSize)[x] <- - paste0(names(cloneSize[x]), ' (', cloneSize[x-1], - ' < X <= ', cloneSize[x], ')') } - for (i in 2:length(cloneSize)) { Con.df$cloneSize <- - ifelse(Con.df$Frequency > cloneSize[i-1] & Con.df$Frequency - <= cloneSize[i], names(cloneSize[i]), Con.df$cloneSize) } + for (x in seq_along(cloneSize)) { + names(cloneSize)[x] <- paste0(names(cloneSize[x]), ' (', cloneSize[x-1], + ' < X <= ', cloneSize[x], ')') + } + + if(proportion) { + c.column <- "clonalProportion" + } else { + c.column <- "clonalFrequency" + } + #Assigning cloneSize + for (i in 2:length(cloneSize)) { + Con.df$cloneSize <- ifelse(Con.df[,c.column] > cloneSize[i-1] & + Con.df[,c.column] <= cloneSize[i], + names(cloneSize[i]), + Con.df$cloneSize) + } + + #Formating the meta data to add PreMeta <- unique(Con.df[,c("barcode", "CTgene", "CTnt", - "CTaa", "CTstrict", "Frequency", "cloneSize")]) + "CTaa", "CTstrict", "clonalProportion", + "clonalFrequency", "cloneSize")]) dup <- PreMeta$barcode[which(duplicated(PreMeta$barcode))] PreMeta <- PreMeta[PreMeta$barcode %!in% dup,] barcodes <- PreMeta$barcode PreMeta <- PreMeta[,-1] rownames(PreMeta) <- barcodes if (group.by != "none" && addLabel) { - location <- which(colnames(PreMeta) == "Frequency") - colnames(PreMeta)[location] <- paste0("Frequency.", group.by) + location <- which(colnames(PreMeta) %in% c("clonalProportion", + "clonalFrequency")) + colnames(PreMeta)[location] <- paste0(c("clonalProportion", + "clonalFrequency"), group.by) } - warn_str <- "< 1% of barcodes match: Ensure the barcodes in - the Seurat object match the barcodes in the combined immune receptor - list from scRepertoire - most common issue is the addition of the - prefixes corresponding to `samples` and 'ID' in the combineTCR/BCR() - functions" + if (is_seurat_object(sc)) { if (length(which(rownames(PreMeta) %in% rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { - warning(warn_str) + warning(.warn_str) } col.name <- names(PreMeta) %||% colnames(PreMeta) sc[[col.name]] <- PreMeta @@ -179,7 +177,11 @@ combineExpression <- function(df, } - +.warn_str <- "< 1% of barcodes match: Ensure the barcodes in + the Seurat object match the barcodes in the combined immune receptor + list from scRepertoire - most common issue is the addition of the + prefixes corresponding to 'samples' and 'ID' in the combineTCR/BCR() + functions" From c4aba9d9313253ac5ed7ca753ca70d104ad72c70 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 3 Oct 2023 10:41:20 -0500 Subject: [PATCH 070/142] inferCDR --- DESCRIPTION | 3 +- NAMESPACE | 3 + R/combineExpression.R | 2 +- R/inferCDR.R | 148 ++++++++++++++++++++++++++++++++++++++++++ man/inferCDR.Rd | 48 ++++++++++++++ 5 files changed, 202 insertions(+), 2 deletions(-) create mode 100644 R/inferCDR.R create mode 100644 man/inferCDR.Rd diff --git a/DESCRIPTION b/DESCRIPTION index d1c8566a..b3401a7b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -37,7 +37,8 @@ Imports: VGAM, cubature, iNEXT, - grDevices + grDevices, + seqinr Suggests: knitr, rmarkdown, diff --git a/NAMESPACE b/NAMESPACE index 187aeb47..470c1b94 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -26,6 +26,7 @@ export(createHTOContigList) export(exportClones) export(getCirclize) export(highlightClones) +export(inferCDR) export(loadContigs) export(percentAA) export(percentGenes) @@ -85,6 +86,8 @@ importFrom(plyr,llply) importFrom(reshape2,dcast) importFrom(reshape2,melt) importFrom(rlang,"%||%") +importFrom(seqinr,read.fasta) +importFrom(seqinr,translate) importFrom(stats,as.dist) importFrom(stats,hclust) importFrom(stats,na.omit) diff --git a/R/combineExpression.R b/R/combineExpression.R index eda90381..988fb95a 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -161,7 +161,7 @@ combineExpression <- function(df, rownames <- rownames(colData(sc)) if (length(which(rownames(PreMeta) %in% rownames))/length(rownames) < 0.01) { - warning(warn_str) } + warning(.warn_str) } colData(sc) <- cbind(colData(sc), PreMeta[rownames,])[, union(colnames(colData(sc)), colnames(PreMeta))] rownames(colData(sc)) <- rownames } diff --git a/R/inferCDR.R b/R/inferCDR.R new file mode 100644 index 00000000..1cdee1ba --- /dev/null +++ b/R/inferCDR.R @@ -0,0 +1,148 @@ +#' Add portions on the CDR loop based on Vgene usage +#' +#' This function will use the Vgene to add the amino acid sequence +#' of the CDR loop for given clones. For example, recovering the +#' CDR1 and CDR2 sequences, will produce a string with +#' CDR1-CDR2-CDR3 (ex: "SGH.......RS-YFS....ETQ-CASSLTDRTYEQYF"). +#' Sequences are derived from IMGT-based nomenclature. If a +#' vgene is not present or does not match IMGT, unrecoverable +#' regions will be denoted with "!" (ex: "!-!-CASSLTDRTYEQYF"). +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' TRB_CDR123 <- inferCDR(combined[[1]], +#' chain = "TRB", +#' regions = c("CDR1", "CDR2"), +#' species = "human") +#' +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. +#' @param chain indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' @param regions CDR loop regions - "FR1", "CDR1", "FR2", "CDR2", and/or "FR3" +#' @param species The species of the experiment - "human" or "mouse" +#' @importFrom stringr str_split +#' @export +#' @return vector of sequences corresponding with the additional regions + +inferCDR <- function(df, + chain = "TRB", + regions = c("CDR1", "CDR2"), + species = "human") { + + .sequence.positions[["FR3.F"]] <- c(97:103) + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, "CTgene", chain) + for (x in seq_along(df)) { + df[[x]] <- .off.the.chain(df[[x]], chain, "CTaa") + df[[x]] <- .off.the.chain(df[[x]], chain, "CTgene") + } + + sequence.pos <- .sequence.positions[grep(paste0(regions, collapse = "|"), names(.sequence.positions))] + + dat <- bind_rows(df) + cdr3.sequences <- dat[,"CTaa"] + Vgene <- str_split(dat[,"CTgene"], "[.]", simplify = TRUE)[,1] + + protein.reference <- .load.fasta(chain, species) + Vgene <- .tenX.V(Vgene, protein.reference) + + for (i in seq_len(length(regions)-1)) { + sequence.pos[[i]] <- c(sequence.pos[[i]], "-") + } + + sequence.pos <- unname(unlist(sequence.pos)) + + sequence.store <- c() + for(i in seq_len(length(cdr3.sequences))) { + if (Vgene[i] %in% names(protein.reference)) { + aa_reference <- protein.reference[[Vgene[i]]] + aa_sequence <- suppressWarnings(aa_reference[as.numeric(sequence.pos)]) + aa_sequence[is.na(aa_sequence)] <- "-" + aa_sequence <- paste0(aa_sequence, collapse = "") + tmp.sequence <- paste0(aa_sequence, "-", cdr3.sequences[i]) + } else { + aa_sequence <- rep("!", length(regions)) + aa_sequence <- paste0(aa_sequence, collapse = "-") + tmp.sequence <- paste0(aa_sequence, "-", cdr3.sequences[i]) + } + sequence.store <- c(sequence.store, tmp.sequence) + } + sequence.store[sequence.store == "!-!-NA"] <- NA + return(sequence.store) +} + +.sequence.positions <- list(FR1.A = c(1:15), + FR1.B = c(16:26), + CDR1 = c(27:38), + FR2.C = c(39:46), + FR2.Cp = c(47:55), + CDR2 = c(56:65), + FR3.C = c(66:74), + FR3.D = c(75:84), + FR3.E = c(85:96), + FR3.F = c(97:104)) + +#' @importFrom stringr str_remove_all +.tenX.V <- function(Vgene, reference) { + Vgene <- str_remove_all(Vgene, "NA_") + #Persistent Adaptive Conversion + V.dict <- as.data.frame(table(Vgene)) + unmatched.V <- as.vector(V.dict$Vgene[V.dict$Vgene %!in% names(reference)]) + rematched.V <- unmatched.V + for(i in seq_along(unmatched.V)) { + tmp.V <- unmatched.V[i] + tmp.V <- str_sort(names(reference), numeric = TRUE)[grep(tmp.V, str_sort(names(reference), numeric = TRUE))][1] + if(is.na(tmp.V)) { + tmp.V <- unmatched.V[i] + tmp.V <- str_split(tmp.V, "-", simplify = TRUE)[,1] + tmp.V <- str_sort(names(reference), numeric = TRUE)[grep(tmp.V, str_sort(names(reference), numeric = TRUE))][1] + } + rematched.V[i] <- tmp.V + } + + #Replacing Sticky Genes + names(rematched.V) <- unmatched.V + matching_indices <- match(Vgene, names(rematched.V)) + Vgene[!is.na(matching_indices)] <- rematched.V[matching_indices[!is.na(matching_indices)]] + return(Vgene) +} + + +#' @importFrom seqinr read.fasta translate +#' @importFrom stringr str_split str_replace_all +.load.fasta <- function(chain, species) { + species <- tolower(species) + species.link <- switch(species, + "human" = "Homo_sapiens", + "mouse" = "Mus_musculus", + stop("Please select either human or mouse for species")) + + chain.link <- switch(chain, + "TRB" = "TR/TRBV", + "TRA" = "TR/TRAV", + "TRG" = "TR/TRGV", + "TRD" = "TR/TRDV", + "Heavy" = "IG/IGHV", + "Light" = c("IG/IGLV", "IG/IGKV"), + stop("Please select chain: TRB, TRA, TRG, TRD, Heavy, Light")) + + dir <- paste0("https://www.imgt.org/download/V-QUEST/IMGT_V-QUEST_reference_directory/", + species.link, "/", chain.link, ".fasta") + reference <- lapply(dir, read.fasta) + if(chain == "Light") { + reference <- c(reference[[1]], reference[[2]]) + } else { + reference <- reference[[1]] + } + names(reference) <- str_split(names(reference), "[|]", simplify = TRUE)[,2] + protein.reference <- lapply(reference, function(x) { + tmp <- seqinr::translate(x) + tmp <- str_replace_all(tmp, "X", ".") + tmp + }) + +} diff --git a/man/inferCDR.Rd b/man/inferCDR.Rd new file mode 100644 index 00000000..67846f95 --- /dev/null +++ b/man/inferCDR.Rd @@ -0,0 +1,48 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/inferCDR.R +\name{inferCDR} +\alias{inferCDR} +\title{Add portions on the CDR loop based on Vgene usage} +\usage{ +inferCDR( + df, + group.by = NULL, + chain = "TRB", + regions = c("CDR1", "CDR2"), + species = "human" +) +} +\arguments{ +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} + +\item{chain}{indicate if both or a specific chain should be used - +e.g. "both", "TRA", "TRG", "IGH", "IGL"} + +\item{regions}{CDR loop regions - "FR1", "CDR1", "FR2", "CDR2", and/or "FR3"} + +\item{species}{The species of the experiment - "human" or "mouse"} +} +\value{ +vector of sequences corresponding with the additional regions +} +\description{ +This function will use the Vgene to add the amino acid sequence +of the CDR loop for given clones. For example, recovering the +CDR1 and CDR2 sequences, will produce a string with +CDR1-CDR2-CDR3 (ex: "SGH.......RS-YFS....ETQ-CASSLTDRTYEQYF"). +Sequences are derived from IMGT-based nomenclature. If a +vgene is not present or does not match IMGT, unrecoverable +regions will be denoted with "!" (ex: "!-!-CASSLTDRTYEQYF"). +} +\examples{ +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +TRB_CDR123 <- inferCDR(combined[[1]], + chain = "TRB", + regions = c("CDR1", "CDR2"), + species = "human") + +} From cc48946e3e46cc51e04d2b633603518f8444811c Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 5 Oct 2023 09:00:20 -0500 Subject: [PATCH 071/142] percentKmer --- .Rbuildignore | 2 + DESCRIPTION | 3 +- NAMESPACE | 3 + NEWS | 1 + R/percentKmer.R | 132 +++++++++++++++++++++++++++++++++++++++++ man/inferCDR.Rd | 8 +-- man/percentKmer.Rd | 51 ++++++++++++++++ vignettes/vignette.Rmd | 23 +++++++ 8 files changed, 215 insertions(+), 8 deletions(-) create mode 100644 R/percentKmer.R create mode 100644 man/percentKmer.Rd diff --git a/.Rbuildignore b/.Rbuildignore index e56ddeb1..28c121f5 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -4,3 +4,5 @@ ^\.github$ ^local_tests\.R$ ^LICENSE\.md$ +^JOSS$ +^draft-pdf\.yml$ diff --git a/DESCRIPTION b/DESCRIPTION index b3401a7b..b97676ad 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -38,7 +38,8 @@ Imports: cubature, iNEXT, grDevices, - seqinr + seqinr, + MatrixGenerics Suggests: knitr, rmarkdown, diff --git a/NAMESPACE b/NAMESPACE index 470c1b94..1f11e8a1 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -30,11 +30,13 @@ export(inferCDR) export(loadContigs) export(percentAA) export(percentGenes) +export(percentKmer) export(percentVJ) export(subsetContig) export(vizGenes) import(dplyr) import(ggplot2) +importFrom(MatrixGenerics,colMads) importFrom(Rcpp,sourceCpp) importFrom(SeuratObject,Embeddings) importFrom(SeuratObject,Idents) @@ -98,6 +100,7 @@ importFrom(stats,sd) importFrom(stringdist,stringdist) importFrom(stringdist,stringdistmatrix) importFrom(stringr,str_c) +importFrom(stringr,str_remove_all) importFrom(stringr,str_replace_all) importFrom(stringr,str_replace_na) importFrom(stringr,str_sort) diff --git a/NEWS b/NEWS index c97a7a00..d19884a0 100644 --- a/NEWS +++ b/NEWS @@ -6,6 +6,7 @@ NEW FEATURES * Added percentAA() * Added percentGenes() * Added percentVJ() +* Added percentKmer() * Added exportClones() to get a csv of paired sequences * Changed compareClonotypes to clonalCompare * Changed clonotypeSizeDistribution to clonalSizeDistribution diff --git a/R/percentKmer.R b/R/percentKmer.R new file mode 100644 index 00000000..7192ac8d --- /dev/null +++ b/R/percentKmer.R @@ -0,0 +1,132 @@ +#' Examining the relative composition of kmer motifs +#' +#' This function the of kmer for nucleotide or amino acids +#' in the CDR3 sequence. +#' +#' @examples +#' #Making combined contig data +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' percentKmer(combined, chain = "TRB", motif.length = 3) + +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' \code{\link{combineExpression}}. +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" +#' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt) or +#' CDR3 amino acid (aa). +#' @param group.by The variable to use for grouping. +#' @param motif.length The length of the kmer to analyze +#' @param top.motifs Return the n most variable motifs as a function of +#' median absolute deviation. +#' @param exportTable Returns the data frame used for forming the graph +#' @param palette Colors to use in visualization - input any hcl.pals() +#' @import ggplot2 +#' @importFrom reshape2 melt +#' @importFrom MatrixGenerics colMads +#' @export +#' @return ggplot of stacked bar graphs of amino acid proportions + +percentKmer <- function(df, + chain = "TRB", + cloneCall = "aa", + group.by = NULL, + motif.length = 3, + top.motifs = 30, + exportTable = FALSE, + palette = "inferno") { + motifs.to.save <- NULL + cloneCall <- .theCall(cloneCall) + sco <- is_seurat_object(df) | is_se_object(df) + df <- .data.wrangle(df, group.by, cloneCall, chain) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } + #Determining the function to generate motifs + kmerFunc <- switch(cloneCall, + "CTaa" = .generate_unique_aa_motifs, + "CTnt" = .generate_unique_nt_motifs, + stop("Please select 'nt' or 'aa' for the cloneCall")) + #Getting motifs and matrix to place the counts + unique.motifs <- kmerFunc(motif.length) + mat <- matrix(NA, ncol = length(unique.motifs), nrow = length(df)) + colnames(mat) <- unique.motifs + rownames(mat) <- names(df) + + #Looping through the counts of motifs + for(i in seq_along(df)) { + df[[i]][,cloneCall][which(df[[i]][,cloneCall] == "NA")] <- NA + df[[i]] <- df[[i]][!is.na(df[[i]][,cloneCall]),] + motifs <- .tokenize_multiple_sequences(df[[i]][,cloneCall], motif.length) + motif.table <- table(unlist(motifs)) + if(any(grepl("\\;", names(motif.table)))) { + motif.table <- motif.table[!grepl("\\;", names(motif.table))] + } + mat.pos <- match(names(motif.table), colnames(mat)) + mat[i,mat.pos] <- as.vector(motif.table) + mat[i,] <- mat[i,]/sum(na.omit(mat[i,])) + } + #Removing any column that is all NAs + if(any(colSums(is.na(mat)) == length(df))) { + mat <- mat[,-which(colSums(is.na(mat)) == length(df))] + } + mat[is.na(mat)] <- 0 + + #Filtering for top.motifs + if(!is.null(top.motifs)) { + mads <- colMads(mat) + names(mads) <- colnames(mat) + motifs.to.save <- names(sort(mads, decreasing = TRUE))[seq_len(top.motifs)] + mat <- mat[,colnames(mat) %in% motifs.to.save] + } + #Getting mat into a ggplot-compliant form + mat_melt <- melt(mat) + if (!is.null(motifs.to.save)) { + mat_melt$Var2 <- factor(mat_melt$Var2, levels = rev(motifs.to.save)) + } + + #Plotting + plot <- ggplot(mat_melt, aes(x=Var2, y = Var1, fill=value)) + + geom_tile(lwd= 0.1, color = "black") + + scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + + theme_classic() + + coord_flip() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), + axis.title = element_blank()) + if (exportTable == TRUE) { + return(mat) + } + return(plot) +} + + +.generate_unique_aa_motifs <- function(motif_length) { + amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y") + all_motifs <- expand.grid(replicate(motif_length, amino_acids, simplify = FALSE)) + unique_motifs <- unique(apply(all_motifs, 1, paste, collapse = "")) + return(unique_motifs) +} + +.generate_unique_nt_motifs <- function(motif_length) { + nt <- c("A", "T", "C", "G") + all_motifs <- expand.grid(replicate(motif_length, nt, simplify = FALSE)) + unique_motifs <- unique(apply(all_motifs, 1, paste, collapse = "")) + return(unique_motifs) +} + +.tokenize_sequence <- function(sequence, motif_length) { + tokens <- c() + sequence_length <- nchar(sequence) + + for (i in 1:(sequence_length - motif_length + 1)) { + motif <- substr(sequence, i, i + motif_length - 1) + tokens <- c(tokens, motif) + } + + return(tokens) +} + +.tokenize_multiple_sequences <- function(sequences, motif_length) { + tokenized_sequences <- sapply(sequences, .tokenize_sequence, motif_length = motif_length) + return(tokenized_sequences) +} diff --git a/man/inferCDR.Rd b/man/inferCDR.Rd index 67846f95..4d7486a9 100644 --- a/man/inferCDR.Rd +++ b/man/inferCDR.Rd @@ -4,13 +4,7 @@ \alias{inferCDR} \title{Add portions on the CDR loop based on Vgene usage} \usage{ -inferCDR( - df, - group.by = NULL, - chain = "TRB", - regions = c("CDR1", "CDR2"), - species = "human" -) +inferCDR(df, chain = "TRB", regions = c("CDR1", "CDR2"), species = "human") } \arguments{ \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or diff --git a/man/percentKmer.Rd b/man/percentKmer.Rd new file mode 100644 index 00000000..d4bc9689 --- /dev/null +++ b/man/percentKmer.Rd @@ -0,0 +1,51 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/percentKmer.R +\name{percentKmer} +\alias{percentKmer} +\title{Examining the relative composition of kmer motifs} +\usage{ +percentKmer( + df, + chain = "TRB", + cloneCall = "aa", + group.by = NULL, + motif.length = 3, + top.motifs = 30, + exportTable = FALSE, + palette = "inferno" +) +} +\arguments{ +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\code{\link{combineExpression}}.} + +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} + +\item{cloneCall}{How to call the clonotype - CDR3 nucleotide (nt) or +CDR3 amino acid (aa).} + +\item{group.by}{The variable to use for grouping.} + +\item{motif.length}{The length of the kmer to analyze} + +\item{exportTable}{Returns the data frame used for forming the graph} + +\item{palette}{Colors to use in visualization - input any hcl.pals()} + +\item{top.motif}{Return the n most variable motifs as a function of +median absolute deviation.} +} +\value{ +ggplot of stacked bar graphs of amino acid proportions +} +\description{ +This function the of kmer for nucleotide or amino acids +in the CDR3 sequence. +} +\examples{ +#Making combined contig data +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +percentKmer(combined, chain = "TRB", motif.length = 3) +} diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index dcc4b58f..136d8e9f 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -452,6 +452,29 @@ ggplot(df, aes(x = PC1, y = PC2)) + scale_fill_manual(values = hcl.colors(nrow(df), "inferno")) + theme_classic() ``` +## percentKmer + +Another quantification of the composition of the CDR3 sequence is to define motifs by sliding across the amino acid or nucleotide sequences at set intervals resulting in substrings or kmers. + +**motif.length** +Numerical value for the length of the kmer. + +**top.motifs** +Display the *N* most variable genes determinied by absolute deviation. + +```{r} +percentKmer(combined, + cloneCall = "aa", + chain = "TRB", + motif.length = 3, + top.motifs = 25) + +percentKmer(combined, + cloneCall = "nt", + chain = "TRB", + motif.length = 3, + top.motifs = 25) +``` *** # More Advanced Clonal Analysis From 26ecf2ba20641cd11579bd9714a4e36ed2c52e8e Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 5 Oct 2023 09:20:13 -0500 Subject: [PATCH 072/142] remove MatrixGenerics import --- DESCRIPTION | 3 +-- R/percentKmer.R | 4 +--- 2 files changed, 2 insertions(+), 5 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index b97676ad..b3401a7b 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -38,8 +38,7 @@ Imports: cubature, iNEXT, grDevices, - seqinr, - MatrixGenerics + seqinr Suggests: knitr, rmarkdown, diff --git a/R/percentKmer.R b/R/percentKmer.R index 7192ac8d..207df9b9 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -23,7 +23,6 @@ #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' @importFrom reshape2 melt -#' @importFrom MatrixGenerics colMads #' @export #' @return ggplot of stacked bar graphs of amino acid proportions @@ -74,8 +73,7 @@ percentKmer <- function(df, #Filtering for top.motifs if(!is.null(top.motifs)) { - mads <- colMads(mat) - names(mads) <- colnames(mat) + mads <- apply(mat, 2, mad) motifs.to.save <- names(sort(mads, decreasing = TRUE))[seq_len(top.motifs)] mat <- mat[,colnames(mat) %in% motifs.to.save] } From 07e9e7f48a81916b5bd9481bbbc1adef6f2522f9 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 5 Oct 2023 09:43:40 -0500 Subject: [PATCH 073/142] update clonalDiversity on vignette --- R/alluvialClonotypes.R | 2 +- R/clonalDiversity.R | 2 +- R/clonalHomeostasis.R | 2 +- R/clonalProportion.R | 2 +- R/inferCDR.R | 11 ++++++++--- vignettes/vignette.Rmd | 30 +++++++++++++++++++----------- 6 files changed, 31 insertions(+), 18 deletions(-) diff --git a/R/alluvialClonotypes.R b/R/alluvialClonotypes.R index f49dc32a..482be5ab 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClonotypes.R @@ -29,7 +29,7 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' Exploring interaction of clonotypes by seurat or SCE dynamics #' #' View the proportional contribution of clonotypes by seurat or SCE object -#' meta data after combineExpression(). The visualization is based on the +#' meta data after \code{\link{combineExpression}}. The visualization is based on the #' ggalluvial package, which requires the aesthetics to be part of the axes #' that are visualized. Therefore, alpha, facet, and color should be part of #' the the axes you wish to view or will add an additional stratum/column to diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 69e5e201..b04bff7d 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -46,7 +46,7 @@ #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by Variable in which to group the diversity calculation #' @param x.axis Additional variable in which to split the x.axis -#' @param group.by The variable to use for goruping +#' @param group.by The variable to use for grouping #' @param metrics The indices to use in diversity calculations - "shannon", "inv.simpson", #' "norm.entropy", "gini.simpson", "chao1", "ACE" #' @param exportTable Exports a table of the data into the global environment diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 2d09fad7..a2ec35e9 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -76,7 +76,7 @@ clonalHomeostasis <- function(df, geom_bar(stat = "identity", position="fill", color = "black", lwd= 0.25) + scale_fill_manual(name = "Clonotype Group", - values = rev(.colorizer(palette,col))) + + values = .colorizer(palette,col)) + xlab("Samples") + ylab("Relative Abundance") + theme_classic() diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 10ac5059..203c45ef 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -75,7 +75,7 @@ clonalProportion <- function(df, geom_bar(stat = "identity", position="fill", color = "black", lwd= 0.25) + scale_fill_manual(name = "Clonal Indices", - values = .colorizer(palette,col)) + + values = rev(.colorizer(palette,col))) + xlab("Samples") + ylab("Occupied Repertoire Space") + theme_classic() diff --git a/R/inferCDR.R b/R/inferCDR.R index 1cdee1ba..938caf91 100644 --- a/R/inferCDR.R +++ b/R/inferCDR.R @@ -40,14 +40,17 @@ inferCDR <- function(df, df[[x]] <- .off.the.chain(df[[x]], chain, "CTaa") df[[x]] <- .off.the.chain(df[[x]], chain, "CTgene") } - + #Get the sequence position for the regions selected sequence.pos <- .sequence.positions[grep(paste0(regions, collapse = "|"), names(.sequence.positions))] dat <- bind_rows(df) cdr3.sequences <- dat[,"CTaa"] Vgene <- str_split(dat[,"CTgene"], "[.]", simplify = TRUE)[,1] + #Laoding reference protein.reference <- .load.fasta(chain, species) + + #Matching the tenx Vgene nomenclature with IMGT Vgene <- .tenX.V(Vgene, protein.reference) for (i in seq_len(length(regions)-1)) { @@ -56,6 +59,7 @@ inferCDR <- function(df, sequence.pos <- unname(unlist(sequence.pos)) + #Loop through the sequences sequence.store <- c() for(i in seq_len(length(cdr3.sequences))) { if (Vgene[i] %in% names(protein.reference)) { @@ -74,7 +78,8 @@ inferCDR <- function(df, sequence.store[sequence.store == "!-!-NA"] <- NA return(sequence.store) } - + +#Positions of specific regions .sequence.positions <- list(FR1.A = c(1:15), FR1.B = c(16:26), CDR1 = c(27:38), @@ -111,7 +116,7 @@ inferCDR <- function(df, return(Vgene) } - +#Pulling the reference sequences form IMGT #' @importFrom seqinr read.fasta translate #' @importFrom stringr str_split str_replace_all .load.fasta <- function(chain, species) { diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 136d8e9f..ea1a5cab 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -403,7 +403,6 @@ percentGenes(combined, We can also use the output `percentGenes()` for dimensional reduction to summarise the gene usage by sample. This can be done with a simple principal component analysis (below) or even more complex reductions. ```{r} - df.genes <- percentGenes(combined, chain = "TRB", gene = "Vgene", @@ -523,7 +522,6 @@ clonalProportion(combined, cloneCall = "nt") ``` - ## clonalOverlap If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. Three methods currently can be performed in `clonalOverlap()` 1) overlap coefficient, 2) Morisita index, 3) Jaccard index, 4) Cosine similarity, and 5) raw numbers. @@ -546,19 +544,29 @@ clonalSizeDistribution(combined, ## clonalDiversity -Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: "shannon" "inv.simpson", "norm.entropy", "gini.simpson", "chao1", and "ACE". +Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: "shannon" "inv.simpson", "norm.entropy", "gini.simpson", "chao1", and "ACE". Please see the manual for more information on each metric and the underlying calculations. ```clonalDiversity()``` will automatically downsample to the smallest repertoire size and perform boostapping to return the median diversity estimates. + +**n.boots** +The number of calculations to perform + +**return.boots** +- TRUE: Return all the calculations +- FALSE: *Default* - return only the mean values -#' @param n.boots number of bootstraps to downsample in order to get mean diversity -#' @param return.boots export boot strapped values calculated - -#' will automatically exportTable = TRUE -#' @param skip.boots remove downsampling and boot strapping from the calculation +**skip.boots** +Skip the bootstrapping calculations -With the former two generally used to estimate baseline diversity and Chao/ACE indices used to estimate the richness of the samples. New implementation of this function include downsampling with 100 boot straps (**n.boots**) using the minimum number of unique clonotypes, as a more robust diversity estimate. To get a table of the bootstrapped values, set **return.boots** = TRUE. ```{r} clonalDiversity(combined, cloneCall = "gene", - n.boots = 50) + n.boots = 20) + +#Return only a subset of metrics +clonalDiversity(combined, + metrics = c("shannon", "ACE"), + cloneCall = "gene", + n.boots = 20) ``` ## clonaRarefaction @@ -567,13 +575,13 @@ We can also use Hill numbers to estimate the rarefaction using the the abundance **plot.type** + 1 - sample-size-based rarefaction/extrapolation -+ 2 - sample completeness curve ++ 2 - sample completeness curve + 3 - coverage-based rarefaction/extrapolation curve **hill.numbers** + 0 - species richness + 1 - Shannon -+ 2 - Simpson ++ 2 - Simpson ```{r, message=FALSE} clonalRarefaction(combined, From d03f59aa0a9759fd06b3bc78ac9eb781f15488be Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 5 Oct 2023 13:03:44 -0500 Subject: [PATCH 074/142] clean alluvialClones --- NAMESPACE | 4 +- NEWS | 1 + R/{alluvialClonotypes.R => alluvialClones.R} | 89 ++++++++++--------- R/clonalDiversity.R | 2 +- R/clonalOccupy.R | 2 +- R/inferCDR.R | 2 +- R/percentKmer.R | 3 +- ...lluvialClonotypes.Rd => alluvialClones.Rd} | 20 +++-- man/clonalDiversity.Rd | 2 +- man/percentKmer.Rd | 8 +- vignettes/vignette.Rmd | 15 ++-- 11 files changed, 81 insertions(+), 67 deletions(-) rename R/{alluvialClonotypes.R => alluvialClones.R} (65%) rename man/{alluvialClonotypes.Rd => alluvialClones.Rd} (80%) diff --git a/NAMESPACE b/NAMESPACE index 1f11e8a1..e3d36b5c 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -3,7 +3,7 @@ export(StartracDiversity) export(abundanceContig) export(addVariable) -export(alluvialClonotypes) +export(alluvialClones) export(clonalBias) export(clonalCompare) export(clonalDiversity) @@ -36,7 +36,6 @@ export(subsetContig) export(vizGenes) import(dplyr) import(ggplot2) -importFrom(MatrixGenerics,colMads) importFrom(Rcpp,sourceCpp) importFrom(SeuratObject,Embeddings) importFrom(SeuratObject,Idents) @@ -92,6 +91,7 @@ importFrom(seqinr,read.fasta) importFrom(seqinr,translate) importFrom(stats,as.dist) importFrom(stats,hclust) +importFrom(stats,mad) importFrom(stats,na.omit) importFrom(stats,optim) importFrom(stats,pgamma) diff --git a/NEWS b/NEWS index d19884a0..1a590b08 100644 --- a/NEWS +++ b/NEWS @@ -15,6 +15,7 @@ NEW FEATURES * Changed highlightClonotypes to highlightClones * Changed lengthContigs to clonalLength * Changed occupiedscRepertoire to clonalOccupy +* Changed alluvialClonotypes to clonalClones UNDERLYING CHANGES diff --git a/R/alluvialClonotypes.R b/R/alluvialClones.R similarity index 65% rename from R/alluvialClonotypes.R rename to R/alluvialClones.R index 482be5ab..b2eda04b 100644 --- a/R/alluvialClonotypes.R +++ b/R/alluvialClones.R @@ -1,32 +1,23 @@ #Making lodes to function in alluvial plots #' @importFrom ggalluvial to_lodes_form -makingLodes <- function(meta2, color, alpha, facet, set.axes) { - if (!is.null(color) & !is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2,key="x",value="stratum",id="alluvium", - axes=set.axes,diffuse=c(as.name(color),as.name(alpha),as.name(facet))) - } else if (!is.null(color) & !is.null(alpha) & is.null(facet)) { - lodes <- to_lodes_form(meta2,key="x",value="stratum",id="alluvium", - axes = set.axes, diffuse = c(as.name(color), as.name(alpha))) - } else if (!is.null(color) & is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2,key="x",value="stratum",id ="alluvium", - axes =set.axes, diffuse = c(as.name(color), as.name(facet))) - } else if (is.null(color) & is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id="alluvium",axes=set.axes,diffuse=c(as.name(alpha),as.name(facet))) - } else if (is.null(color) & is.null(alpha) & !is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes, diffuse = c(as.name(facet))) - } else if (!is.null(color) & is.null(alpha) & is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes, diffuse = c(as.name(color))) - } else if (is.null(color) & !is.null(alpha) & is.null(facet)) { - lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes, diffuse = c(as.name(alpha))) - } else { lodes <- to_lodes_form(meta2, key = "x", value = "stratum", - id = "alluvium", axes = set.axes)} - return(lodes) } - -#' Exploring interaction of clonotypes by seurat or SCE dynamics +.makingLodes <- function(meta2, color, alpha, facet, set.axes) { + diffuse_elements <- c() + if (!is.null(color)) { + diffuse_elements <- c(diffuse_elements, color) + } + if (!is.null(alpha)) { + diffuse_elements <- c(diffuse_elements, alpha) + } + if (!is.null(facet)) { + diffuse_elements <- c(diffuse_elements, facet) + } + + lodes <- to_lodes_form(meta2, key = "x", value = "stratum", + id = "alluvium", axes = set.axes, + diffuse = if(length(diffuse_elements) > 0) diffuse_elements else NULL) + return(lodes) +} +#' Exploring interaction of clonotypes by single-cell object dynamics #' #' View the proportional contribution of clonotypes by seurat or SCE object #' meta data after \code{\link{combineExpression}}. The visualization is based on the @@ -48,8 +39,8 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' scRep_example <- combineExpression(combined, scRep_example) #' scRep_example$Patient <- substring(scRep_example$orig.ident, 1,3) #' -#' #Using alluvialClonotypes() -#' alluvialClonotypes(scRep_example, +#' #Using alluvialClones() +#' alluvialClones(scRep_example, #' cloneCall = "gene", #' y.axes = c("Patient", "ident"), #' color = "ident") @@ -65,6 +56,8 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' @param color The column header or clonotype(s) to be highlighted. #' @param facet The column label to separate. #' @param alpha The column header to have gradated opacity. +#' @param exportTable Exports a table of the data into the global +#' environment in addition to the visualization #' @param palette Colors to use in visualization - input any hcl.pals() #' #' @import ggplot2 @@ -73,14 +66,16 @@ makingLodes <- function(meta2, color, alpha, facet, set.axes) { #' #' @export #' @return Alluvial ggplot comparing clonotype distribution. -alluvialClonotypes <- function(sc, - cloneCall = "strict", - chain = "both", - y.axes = NULL, - color = NULL, - alpha = NULL, - facet = NULL, - palette = "inferno") { +alluvialClones <- function(sc, + cloneCall = "strict", + chain = "both", + y.axes = NULL, + color = NULL, + alpha = NULL, + facet = NULL, + exportTable = FALSE, + palette = "inferno") { + x <- alluvium <- stratum <- NULL .checkSingleObject(sc) cloneCall <- .theCall(cloneCall) @@ -92,19 +87,33 @@ alluvialClonotypes <- function(sc, meta <- .off.the.chain(meta, chain, cloneCall) } meta$barcodes <- rownames(meta) + meta <- meta[!is.na(meta[,cloneCall]),] check <- colnames(meta) == color if (length(unique(check)) == 1 & unique(check)[1] == FALSE & !is.null(color)) { meta <- meta %>% mutate("clonotype(s)" = ifelse(meta[,cloneCall] %in% color, "Selected", "Other")) - color <- "clonotype(s)" } + color <- "clonotype(s)" + } + #Prepping the data for calculating lodes y.axes <- unique(c(y.axes, color, alpha, facet)) set.axes <- seq_along(y.axes) - meta2 <- meta[,c(y.axes, color, alpha, facet, cloneCall, "barcodes")] + meta2 <- meta[,c(y.axes, cloneCall, "barcodes")] meta2 <- unique(na.omit(meta2[!duplicated(as.list(meta2))])) - lodes <- makingLodes(meta2, color, alpha, facet, set.axes) + lodes <- .makingLodes(meta2, color, alpha, facet, set.axes) + #Filtering the lodes + if(any(lodes[,cloneCall] != "")) { + lodes <- lodes[lodes[,cloneCall] != "",] + } + if(any(is.na(lodes[,cloneCall]))) { + lodes <- lodes[!is.na(lodes[,cloneCall]),] + } + if (exportTable) { + return(lodes) + } + #Plotting plot <- ggplot(data = lodes, aes(x = x, stratum = stratum, alluvium = alluvium, diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index b04bff7d..8907bec8 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -130,7 +130,7 @@ clonalDiversity <- function(df, x.axis <- "x.axis" mat[,x.axis] <- 1 } - if (exportTable == TRUE) { + if (exportTable) { return(mat) } rownames(mat) <- names(df) diff --git a/R/clonalOccupy.R b/R/clonalOccupy.R index bf09398a..4e3b73c3 100644 --- a/R/clonalOccupy.R +++ b/R/clonalOccupy.R @@ -82,7 +82,7 @@ clonalOccupy <- function(sc, } plot <- plot + theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5)) + - scale_fill_manual(values = c(.colorizer(palette,col))) + + scale_fill_manual(values = rev(c(.colorizer(palette,col)))) + ylab(lab) + theme_classic() + theme(axis.title.x = element_blank()) diff --git a/R/inferCDR.R b/R/inferCDR.R index 938caf91..911384b9 100644 --- a/R/inferCDR.R +++ b/R/inferCDR.R @@ -35,7 +35,7 @@ inferCDR <- function(df, .sequence.positions[["FR3.F"]] <- c(97:103) sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTgene", chain) + df <- .data.wrangle(df, NULL, "CTgene", chain) for (x in seq_along(df)) { df[[x]] <- .off.the.chain(df[[x]], chain, "CTaa") df[[x]] <- .off.the.chain(df[[x]], chain, "CTgene") diff --git a/R/percentKmer.R b/R/percentKmer.R index 207df9b9..35f1b889 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -23,8 +23,9 @@ #' @param palette Colors to use in visualization - input any hcl.pals() #' @import ggplot2 #' @importFrom reshape2 melt +#' @importFrom stats mad #' @export -#' @return ggplot of stacked bar graphs of amino acid proportions +#' @return ggplot of percentage of kmers as a heatmap percentKmer <- function(df, chain = "TRB", diff --git a/man/alluvialClonotypes.Rd b/man/alluvialClones.Rd similarity index 80% rename from man/alluvialClonotypes.Rd rename to man/alluvialClones.Rd index 38cdddd1..865bc460 100644 --- a/man/alluvialClonotypes.Rd +++ b/man/alluvialClones.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/alluvialClonotypes.R -\name{alluvialClonotypes} -\alias{alluvialClonotypes} -\title{Exploring interaction of clonotypes by seurat or SCE dynamics} +% Please edit documentation in R/alluvialClones.R +\name{alluvialClones} +\alias{alluvialClones} +\title{Exploring interaction of clonotypes by single-cell object dynamics} \usage{ -alluvialClonotypes( +alluvialClones( sc, cloneCall = "strict", chain = "both", @@ -12,6 +12,7 @@ alluvialClonotypes( color = NULL, alpha = NULL, facet = NULL, + exportTable = FALSE, palette = "inferno" ) } @@ -34,6 +35,9 @@ visualizations.} \item{facet}{The column label to separate.} +\item{exportTable}{Exports a table of the data into the global +environment in addition to the visualization} + \item{palette}{Colors to use in visualization - input any hcl.pals()} } \value{ @@ -41,7 +45,7 @@ Alluvial ggplot comparing clonotype distribution. } \description{ View the proportional contribution of clonotypes by seurat or SCE object -meta data after combineExpression(). The visualization is based on the +meta data after \code{\link{combineExpression}}. The visualization is based on the ggalluvial package, which requires the aesthetics to be part of the axes that are visualized. Therefore, alpha, facet, and color should be part of the the axes you wish to view or will add an additional stratum/column to @@ -60,8 +64,8 @@ scRep_example <- get(data("scRep_example")) scRep_example <- combineExpression(combined, scRep_example) scRep_example$Patient <- substring(scRep_example$orig.ident, 1,3) -#Using alluvialClonotypes() -alluvialClonotypes(scRep_example, +#Using alluvialClones() +alluvialClones(scRep_example, cloneCall = "gene", y.axes = c("Patient", "ident"), color = "ident") diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index ae6e5206..a05ae0a8 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -29,7 +29,7 @@ VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{The variable to use for goruping} +\item{group.by}{The variable to use for grouping} \item{x.axis}{Additional variable in which to split the x.axis} diff --git a/man/percentKmer.Rd b/man/percentKmer.Rd index d4bc9689..e568c1ef 100644 --- a/man/percentKmer.Rd +++ b/man/percentKmer.Rd @@ -28,15 +28,15 @@ CDR3 amino acid (aa).} \item{motif.length}{The length of the kmer to analyze} +\item{top.motifs}{Return the n most variable motifs as a function of +median absolute deviation.} + \item{exportTable}{Returns the data frame used for forming the graph} \item{palette}{Colors to use in visualization - input any hcl.pals()} - -\item{top.motif}{Return the n most variable motifs as a function of -median absolute deviation.} } \value{ -ggplot of stacked bar graphs of amino acid proportions +ggplot of percentage of kmers as a heatmap } \description{ This function the of kmer for nucleotide or amino acids diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index ea1a5cab..d3097d31 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -787,24 +787,23 @@ clonalOccupy(scRep_example, label = FALSE) ``` -## alluvialClonotypes +## alluvialClones -After the metadata has been modified, we can look at clonotypes across multiple categories using the `alluvialClonotypes()` function. To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html). We are able to use the plots to examine the interchange of categorical variables. Because this function will produce a graph with each clonotype arranged by called stratification, this will take some time depending on the size of your total cells. To expedite, we will subset the Seurat object before using `alluvialClonotypes()`. +After the metadata has been modified, we can look at clonotypes across multiple categories using the `alluvialClones()` function. To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html). We are able to use the plots to examine the interchange of categorical variables. Because this function will produce a graph with each clonotype arranged by called stratification, this will take some time depending on the size of your total cells. To expedite, we will subset the Seurat object before using `alluvialClones()`. ```{r} -alluvialClonotypes(scRep_example, - cloneCall = "aa", - y.axes = c("Patient", "ident", "Type"), - color = "CVVSDNTGGFKTIF_CASSVRRERANTGELFF") + +alluvialClones(scRep_example, + cloneCall = "aa", + y.axes = c("Patient", "ident", "Type"), + color = "CVVSDNTGGFKTIF_CASSVRRERANTGELFF") + scale_fill_manual(values = c("grey", colorblind_vector[3])) -alluvialClonotypes(scRep_example, +alluvialClones(scRep_example, cloneCall = "gene", y.axes = c("Patient", "ident", "Type"), color = "ident") ``` - ## getCirclize Like alluvial graphs, we can also visualize the interconnection of clusters using the chord diagrams from the circlize R package. The first step is getting the data frame output to feed into the `chordDiagram()` function in circlize, which can be done using `getCirclize()`. This will calculate the relative number of clonotypes shared based on the **group.by** variable using the product of `combineExpression()`. It is important to note `getCirclize()` will create a matrix the size of the **group.by** variable and then simplify into instructions to be read by the circlize R package. The output is the total number of unique and shared clones by the **group.by** variable - this will be greater than the number of unique clonotypes due to the shared clone information. From b49bddc75bf3723c43c02a0950576885b0fd0f76 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 5 Oct 2023 15:38:33 -0500 Subject: [PATCH 075/142] cleaning documentation --- DESCRIPTION | 17 ++++++------ NAMESPACE | 1 + R/abundanceContig.R | 2 +- R/alluvialClones.R | 8 +++--- R/clonalBias.R | 11 ++++---- R/clonalCompare.R | 8 +++--- R/clonalDiversity.R | 52 +++++++++++++++++++---------------- R/clonalHomeostasis.R | 2 +- R/clonalLength.R | 2 +- R/clonalNetwork.R | 16 +++++------ R/clonalOccupy.R | 14 +++++----- R/clonalOverlap.R | 27 ++++++++++-------- R/clonalProportion.R | 8 +++--- R/clonalQuant.R | 2 +- R/clonalRarefaction.R | 20 +++++++------- R/clonalScatter.R | 19 ++++++------- R/clonalSizeDistribution.R | 11 ++++---- R/highlightClones.R | 31 +++++++++++++-------- R/loadContigs.R | 18 ++++++------ R/percentAA.R | 8 +++--- R/percentGenes.R | 8 +++--- R/percentKmer.R | 8 +++--- R/percentVJ.R | 2 +- R/startracDiversity.R | 16 +++++------ R/vizGenes.R | 2 +- man/StartracDiversity.Rd | 16 +++++------ man/abundanceContig.Rd | 2 +- man/alluvialClones.Rd | 8 +++--- man/clonalBias.Rd | 11 ++++---- man/clonalCompare.Rd | 8 +++--- man/clonalDiversity.Rd | 50 ++++++++++++++++++--------------- man/clonalHomeostasis.Rd | 2 +- man/clonalLength.Rd | 2 +- man/clonalNetwork.Rd | 16 +++++------ man/clonalOccupy.Rd | 14 +++++----- man/clonalOverlap.Rd | 25 ++++++++++------- man/clonalProportion.Rd | 8 +++--- man/clonalQuant.Rd | 2 +- man/clonalRarefaction.Rd | 23 ++++++++-------- man/clonalScatter.Rd | 19 ++++++------- man/clonalSizeDistribution.Rd | 14 ++++------ man/highlightClones.Rd | 6 ++-- man/loadContigs.Rd | 19 +++++++------ man/percentAA.Rd | 8 +++--- man/percentGenes.Rd | 8 +++--- man/percentKmer.Rd | 8 +++--- man/percentVJ.Rd | 2 +- man/vizGenes.Rd | 2 +- 48 files changed, 310 insertions(+), 276 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index b3401a7b..8cb2504d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -19,26 +19,27 @@ Imports: reshape2, ggalluvial, stringr, - SingleCellExperiment, - SummarizedExperiment, plyr, methods, - utils, rlang, igraph, ggraph, - tidygraph, - SeuratObject, stats, Rcpp, evmix, ggdendro, - truncdist, - VGAM, cubature, iNEXT, grDevices, - seqinr + S4Vectors, + seqinr, + SeuratObject, + SingleCellExperiment, + SummarizedExperiment, + tidygraph, + truncdist, + utils, + VGAM Suggests: knitr, rmarkdown, diff --git a/NAMESPACE b/NAMESPACE index e3d36b5c..6e4f15af 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -37,6 +37,7 @@ export(vizGenes) import(dplyr) import(ggplot2) importFrom(Rcpp,sourceCpp) +importFrom(S4Vectors,DataFrame) importFrom(SeuratObject,Embeddings) importFrom(SeuratObject,Idents) importFrom(SingleCellExperiment,colData) diff --git a/R/abundanceContig.R b/R/abundanceContig.R index 7910f291..f6480367 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -27,7 +27,7 @@ #' relative distributions. #' @param exportTable Returns the data frame used for forming the graph #' to the visualization. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @importFrom ggplot2 ggplot #' @export #' @return ggplot of the total or relative abundance of clonotypes diff --git a/R/alluvialClones.R b/R/alluvialClones.R index b2eda04b..d7dfc4e3 100644 --- a/R/alluvialClones.R +++ b/R/alluvialClones.R @@ -50,15 +50,15 @@ #' CDR3 nucleotide (nt) or CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param y.axes The columns that will separate the proportional +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". +#' @param y.axes The columns that will separate the proportional . #' visualizations. #' @param color The column header or clonotype(s) to be highlighted. #' @param facet The column label to separate. #' @param alpha The column header to have gradated opacity. #' @param exportTable Exports a table of the data into the global -#' environment in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' environment in addition to the visualization. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' #' @import ggplot2 #' @importFrom ggalluvial StatStratum geom_flow geom_stratum to_lodes_form geom_alluvium diff --git a/R/clonalBias.R b/R/clonalBias.R index 82545e5d..95fe16f6 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -1,12 +1,13 @@ #' Examine clonotype bias #' -#' Clonotype bias method was developed and outlined from a single-cell -#' \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} -#' characterizing CD4 responses to acute and chronic infection. The metric seeks to +#' The metric seeks to #' quantify how individual clones are skewed towards a specific cellular -#' compartment or cluster. A clonotype bias of 1 indicates that a clonotype +#' compartment or cluster. A clonotype bias of *1* - indicates that a clonotype #' is composed of cells from a single compartment or cluster, while a clonotype -#' bias of 0 matches the background subtype distribution. +#' bias of *0* - matches the background subtype distribution. Please read and cite +#' the following +#' \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} +#' if using \code{\link{clonalBias()}} #' #' @examples #' #Making combined contig data diff --git a/R/clonalCompare.R b/R/clonalCompare.R index 1cde2d4d..4816b163 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -22,19 +22,19 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param samples The specific samples to isolate for visualization. #' @param clonotypes The specific clonal sequences of interest. -#' @param top.clones The top number of clonotype sequences per group +#' @param top.clones The top number of clonotype sequences per group. #' @param highlight.clones Clonal sequences to highlight, if present, #' all other clones returned will be grey. #' @param relabel.clones Simplify the legend of the graph by returning #' clones that are numerically indexed. -#' @param group.by If using a single-cell object, the column header +#' @param group.by If using a single-cell object, the column header . #' to group the new list. NULL will return clusters. #' @param graph The type of graph produced, either "alluvial" or "area". #' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' #' @export diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 8907bec8..33b3411e 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -1,8 +1,8 @@ #' Examine the clonal diversity of samples #' -#' This function calculates traditional measures of diversity - Shannon, -#' inverse Simpson, normalized entropy, Gini-Simpson, Chao1 index, and -#' abundance-based coverage estimators (ACE) measure of species evenness by sample or group. +#' This function calculates traditional measures of diversity - **Shannon**, +#' **inverse Simpson**, **normalized entropy**, **Gini-Simpson**, **Chao1 index**, and +#' **abundance-based coverage estimators (ACE)** measure of species evenness by sample or group. #' The function automatically down samples the diversity metrics using #' 100 boot straps The group parameter can be used to condense the individual #' samples. If a matrix output for the data is preferred, set exportTable = TRUE. @@ -10,24 +10,30 @@ #' @details #' The formulas for the indices and estimators are as follows: #' -#' \deqn{Shannon Index: H = - \sum p_i \cdot \log(p_i)} +#' Shannon Index: +#' \deqn{H = - \sum p_i \cdot \log(p_i)} #' -#' \deqn{Inverse Simpson Index: D^{-1} = 1 / \sum p_i^2} +#' Inverse Simpson Index: +#' \deqn{ D^{-1} = 1 / \sum p_i^2} #' -#' \deqn{Normalized Entropy: E^H = H / \log(S)} +#' Normalized Entropy: +#' \deqn{E^H = H / \log(S)} #' -#' \deqn{Gini-Simpson Index: 1 - D = 1 - \sum p_i^2} +#' Gini-Simpson Index: +#' \deqn{1 - D = 1 - \sum p_i^2} #' -#' \deqn{Chao1 Index: \hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} +#' Chao1 Index: +#' \deqn{\hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} #' -#' \deqn{Abundance-based Coverage Estimator (ACE): \hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} +#' Abundance-based Coverage Estimator (ACE): +#' \deqn{\hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} #' #' Where: #' \itemize{ -#' \item{ \( p_i \) is the proportion of species \( i \) in the dataset.} -#' \item{ \( S \) is the total number of species.} -#' \item{ \( n_1 \) and \( n_2 \) are the number of singletons and doubletons, respectively.} -#' \item{ \( S_{abundant} \), \( S_{rare} \), \( C_{rare} \), and \( F_1 \) are parameters derived from the data.} +#' \item{\(p_i\) is the proportion of species \(i\) in the dataset.} +#' \item{\(S\) is the total number of species.} +#' \item{\(n_1\) and \(n_2\) are the number of singletons and doubletons, respectively.} +#' \item{\(S_{abundant}\), \(S_{rare}\), \(C_{rare}\), and \(F_1\) are parameters derived from the data.} #' } #' #' @examples @@ -43,25 +49,25 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by Variable in which to group the diversity calculation -#' @param x.axis Additional variable in which to split the x.axis -#' @param group.by The variable to use for grouping +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". +#' @param group.by Variable in which to group the diversity calculation. +#' @param x.axis Additional variable in which to split the x.axis. +#' @param group.by The variable to use for grouping. #' @param metrics The indices to use in diversity calculations - "shannon", "inv.simpson", -#' "norm.entropy", "gini.simpson", "chao1", "ACE" +#' "norm.entropy", "gini.simpson", "chao1", "ACE". #' @param exportTable Exports a table of the data into the global environment -#' in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' in addition to the visualization. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @param n.boots number of bootstraps to downsample in order to get mean diversity #' @param return.boots export boot strapped values calculated - -#' will automatically exportTable = TRUE -#' @param skip.boots remove downsampling and boot strapping from the calculation +#' will automatically exportTable = TRUE. +#' @param skip.boots remove downsampling and boot strapping from the calculation. #' @importFrom stringr str_sort str_split #' @importFrom reshape2 melt #' @importFrom dplyr sample_n #' @import ggplot2 #' @export -#' @return ggplot of the diversity of clonotype sequences across list +#' @return ggplot of the diversity of clones by group #' @author Andrew Malone, Nick Borcherding clonalDiversity <- function(df, cloneCall = "strict", diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index a2ec35e9..5e64730e 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -25,7 +25,7 @@ #' @param group.by The variable to use for grouping. #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom stringr str_split #' @importFrom reshape2 melt diff --git a/R/clonalLength.R b/R/clonalLength.R index 9eb23d25..ce9d0e19 100644 --- a/R/clonalLength.R +++ b/R/clonalLength.R @@ -26,7 +26,7 @@ #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @importFrom stringr str_split #' @importFrom ggplot2 ggplot #' @export diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index 6f02b969..2d357581 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -25,22 +25,22 @@ #' } #' #' @param sc The single-cell object after \code{\link{combineExpression}}. -#' @param reduction The name of the dimensional reduction of the single-cell object +#' @param reduction The name of the dimensional reduction of the single-cell object. #' @param identity A variable in the meta data to use for the nodes. #' @param filter.clones Use to select the top n clones (filter.clones = 2000) or #' n of clones based on the minimum number of all the comparators (filter.clone = "min"). -#' @param filter.identity Display the network for a specific level of the indicated identity -#' @param filter.proportion Remove clonotypes from the network below a specific proportion -#' @param filter.graph Remove the reciprocal edges from the half of the graph, -#' allowing for cleaner visualization +#' @param filter.identity Display the network for a specific level of the indicated identity. +#' @param filter.proportion Remove clonotypes from the network below a specific proportion. +#' @param filter.graph Remove the reciprocal edges from the half of the graph, +#' allowing for cleaner visualization. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param exportTable Exports a table of the data into the global -#' environment in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' environment in addition to the visualization. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @param exportClones Exports a table of clones that are shared #' across multiple identity groups and ordered by the total number #' of clone copies. diff --git a/R/clonalOccupy.R b/R/clonalOccupy.R index 4e3b73c3..9556c747 100644 --- a/R/clonalOccupy.R +++ b/R/clonalOccupy.R @@ -2,7 +2,7 @@ #' #' View the count of clonotypes frequency group in seurat or SCE object #' meta data after \code{\link{combineExpression}}. The visualization will take the -#' new meta data variable "cloneSize" and plot the number of cells with +#' new meta data variable **"cloneSize"** and plot the number of cells with #' each designation using a secondary variable, like cluster. Credit to #' the idea goes to Drs. Carmona and Andreatta and their work with #' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. @@ -24,14 +24,14 @@ #' table <- clonalOccupy(scRep_example, x.axis = "ident", exportTable = TRUE) #' #' @param sc The single-cell object after \code{\link{combineExpression}}. -#' @param x.axis The variable in the meta data to graph along the x.axis -#' @param label Include the number of clonotype in each category by x.axis variable -#' @param facet.by The column header used for faceting the graph -#' @param proportion Convert the stacked bars into relative proportion +#' @param x.axis The variable in the meta data to graph along the x.axis. +#' @param label Include the number of clonotype in each category by x.axis variable. +#' @param facet.by The column header used for faceting the graph. +#' @param proportion Convert the stacked bars into relative proportion. #' @param na.include Visualize NA values or not. #' @param exportTable Exports a table of the data into the global -#' environment in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' environment in addition to the visualization. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @importFrom dplyr %>% group_by mutate #' @importFrom reshape2 melt #' @import ggplot2 diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index aa3854c2..eeef1e5f 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -6,19 +6,24 @@ #' @details #' The formulas for the indices are as follows: #' -#' \deqn{Overlap Coefficient: OC = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} +#' Overlap Coefficient: +#' \deqn{overlap = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} #' -#' \deqn{Raw Count Overlap: RCO = \sum \min(a_i, b_i)} +#' Raw Count Overlap: +#' \deqn{raw = \sum \min(a_i, b_i)} #' -#' \deqn{Morisita Index: MI = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} +#' Morisita Index: +#' \deqn{morisita = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} #' -#' \deqn{Jaccard Index: JI = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} +#' Jaccard Index: +#' \deqn{jaccard = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} #' -#' \deqn{Cosine Similarity: CS = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} +#' Cosine Similarity: +#' \deqn{cosine = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} #' #' Where: #' \itemize{ -#' \item{ \( a_i \) and \( b_i \) are the abundances of species \( i \) in groups A and B, respectively.} +#' \item{\(a_i\) and \(b_i\) are the abundances of species \(i\) in groups A and B, respectively.} #' } #' #' @examples @@ -39,16 +44,16 @@ #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param method The method to calculate the "overlap", -# "morisita", "jaccard" indices, "cosine" similarity or "raw" +# "morisita", "jaccard", "cosine" indices or "raw" #' for the base numbers. -#' @param group.by The variable to use for grouping -#' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param group.by The variable to use for grouping. +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @importFrom stringr str_sort str_to_title #' @importFrom reshape2 melt #' @importFrom stats quantile #' @export -#' @return ggplot of the clonotypic overlap between elements of a list +#' @return ggplot of the overlap of clones by group clonalOverlap <- function(df, cloneCall = "strict", method = NULL, diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 203c45ef..18b12c99 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -21,11 +21,11 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param group.by The variable to use for grouping. -#' @param exportTable Exports a table of the data into the global -#' environment in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param exportTable Exports a table of the data into the global. +#' environment in addition to the visualization. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' #' @import ggplot2 #' @importFrom stringr str_sort diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 08fd2332..9115d807 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -22,7 +22,7 @@ #' @param order Maintain the order of the list when plotting #' @param scale Converts the graphs into percentage of unique clonotypes. #' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @export #' @return ggplot of the total or relative unique clonotypes diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index d20bd69d..1ef171ac 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -1,14 +1,14 @@ #' Calculate rarefaction based on the abundance of clones #' -#' This functions uses the Hill numbers of order q: species richness (q = 0), -#' Shannon diversity (q = 1, the exponential of Shannon entropy) and Simpson -#' diversity (q = 2, the inverse of Simpson concentration) to compute diversity +#' This functions uses the Hill numbers of order q: species richness (**q = 0&&), +#' Shannon diversity (**q = 1**, the exponential of Shannon entropy) and Simpson +#' diversity (**q = 2**, the inverse of Simpson concentration) to compute diversity #' estimates for rarefaction and extrapolation. The function relies on -#' \code{\link[iNEXT]{iNEXT}} and read/please cite the +#' \code{\link[iNEXT]{iNEXT}}. Please read and cite the #' \href{https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12613}{manuscript} #' if using this function. #' -#' #' @examples +#' @examples #' #Making combined contig data #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", @@ -22,15 +22,15 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The variable to use for grouping +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". +#' @param group.by The variable to use for grouping. #' @param plot.type sample-size-based rarefaction/extrapolation curve (\code{type = 1}); #' sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}). #' @param hill.numbers The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson) -#' @param n.boots The number of bootstraps to downsample in order to get mean diversity +#' @param n.boots The number of bootstraps to downsample in order to get mean diversity. #' @param exportTable Exports a table of the data into the global -#' environment in addition to the visualization -#' @param palette Colors to use in visualization - input any hcl.pals() +#' environment in addition to the visualization. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' #' @importFrom iNEXT iNEXT ggiNEXT #' @import ggplot2 diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 1f1df53e..2f1d934d 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -1,9 +1,8 @@ #' Scatter plot comparing the expansion of two samples #' #' This function produces a scatter plot directly comparing the specific clonotypes -#' between two samples. The clonotypes will be categorized by counts into singlets or multiplets, -#' either exclusive or shared between the selected samples. Visualization inspired -#' by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. +#' between two samples. The clonotypes will be categorized by counts into singlets +#' or expanded, either exclusive or shared between the selected samples. #' #' @examples #' #Making combined contig data @@ -21,15 +20,15 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param x.axis name of the list element to appear on the x.axis -#' @param y.axis name of the list element to appear on the y.axis +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". +#' @param x.axis name of the list element to appear on the x.axis. +#' @param y.axis name of the list element to appear on the y.axis. #' @param dot.size either total or the name of the list element to -#' use for size of dots -#' @param group.by The variable to use for grouping -#' @param graph graph either proportion or raw clonotype count +#' use for size of dots. +#' @param group.by The variable to use for grouping. +#' @param graph graph either proportion or raw clonotype count. #' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' #' @import ggplot2 #' diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 0846ef79..717db3cb 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -3,9 +3,8 @@ #' #' This function produces a hierarchical clustering of clonotypes by sample #' using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold -#' model in -#' \href{https://bioconductor.org/packages/devel/bioc/html/powerTCR.html}{powerTCR R package}. -#' Please read and cite PMID: 30485278 if using the function for analyses. +#' model. If using this model please read and cite +#' \href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. #' #' @examples #' #Making combined contig data @@ -20,13 +19,13 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param threshold Numerical vector containing the thresholds #' the grid search was performed over. #' @param method The clustering parameter for the dendrogram. -#' @param group.by The variable to use for grouping +#' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom dplyr bind_rows #' @importFrom ggdendro dendro_data segment label diff --git a/R/highlightClones.R b/R/highlightClones.R index 625e15d9..674c4879 100644 --- a/R/highlightClones.R +++ b/R/highlightClones.R @@ -1,7 +1,7 @@ #' Highlighting specific clonotypes in Seurat #' #' Use a specific clonotype sequence to highlight on top of the dimensional -#' reduction in Seurat object. +#' reduction in single-cell object. #' #' @examples #' #Getting the combined contigs @@ -19,20 +19,21 @@ #' scRep_example <- highlightClones(scRep_example , cloneCall= "aa", #' sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) #' -#' @param sc The Seurat object to attach after \code{\link{combineExpression}} +#' @param sc The single-cell object to attach after \code{\link{combineExpression}} #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param sequence The specific sequence or sequence to highlight -#' +#' @importFrom S4Vectors DataFrame #' @export -#' @return Seurat object with new meta data column for indicated clones +#' @return Single-cell object object with new meta data column for indicated clones highlightClones <- function(sc, - cloneCall = c("gene", "nt", "aa", "strict"), - sequence = NULL){ - if (!inherits(x=sc, what ="Seurat")) { - stop("Object indicated is not of class 'Seurat', make sure you - are using the correct data.") } + cloneCall = c("gene", "nt", "aa", "strict"), + sequence = NULL){ + if (!is_seurat_or_se_object(sc)) { + stop("Please select a single-cell object") + } + cloneCall <- .theCall(cloneCall) meta <- .grabMeta(sc) meta$highlight <- NA @@ -40,8 +41,14 @@ highlightClones <- function(sc, meta$highlight <- ifelse(meta[,cloneCall] == sequence[i], sequence[i], meta$highlight) } + meta <- meta[,-(which(colnames(meta) == "ident"))] - col.name <- names(meta) %||% colnames(meta) - sc[[col.name]] <- meta - sc + + if(is_se_object(sc)) { + colData(sc) <- DataFrame(meta) + } else { + col.name <- names(meta) %||% colnames(meta) + sc[[col.name]] <- meta + } + return(sc) } \ No newline at end of file diff --git a/R/loadContigs.R b/R/loadContigs.R index 7dead48c..7db515ba 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -5,17 +5,19 @@ #' using data derived from filtered outputs of 10X Genomics, there is no #' need to use this function as the data is already compatible. The function #' assumes if listing multiple directories, there are distinct outputs with -#' unmodified file names in them. The files that this function parses includes: -#' WAT3R = "barcode_results.csv", 10X = "filtered_contig_annotation.csv", -#' AIRR = "airr_rearrangement.tsv", TRUST4 = "barcode_report.tsv", and -#' BD = "Contigs_AIRR.tsv". +#' unmodified file names in them. #' -#' @examples -#' \dontrun{ -#' dir <- c("./Sample1/outs/") -#' contig.list <- loadContigs(dir, format = "10X") +#' The files that this function parses includes: +#' \itemize{ +#' \item 10X = "filtered_contig_annotation.csv" +#' \item AIRR = "airr_rearrangement.tsv" +#' \item BD = "Contigs_AIRR.tsv" +#' \item TRUST4 = "barcode_report.tsv" +#' \item WAT3R = "barcode_results.csv" #' } #' +#' @examples +#' #' TRUST4 <- read.csv("https://www.borch.dev/uploads/contigs/TRUST4_contigs.csv") #' contig.list <- loadContigs(TRUST4, format = "TRUST4") #' diff --git a/R/percentAA.R b/R/percentAA.R index 78382097..e223e8ed 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -12,11 +12,11 @@ #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". #' @param group.by The variable to use for grouping. -#' @param aa.length The maximum length of the cdr3 amino acid sequence -#' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param aa.length The maximum length of the CDR3 amino acid sequence. +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom reshape2 melt #' @export diff --git a/R/percentGenes.R b/R/percentGenes.R index a4add4ac..a3cf60ed 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -12,11 +12,11 @@ #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" -#' @param gene "V", "D" or "J" +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". +#' @param gene "V", "D" or "J". #' @param group.by The variable to use for grouping. -#' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom stringr str_split str_sort #' @importFrom reshape2 melt diff --git a/R/percentKmer.R b/R/percentKmer.R index 35f1b889..2b88312a 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -12,15 +12,15 @@ #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" +#' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". #' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt) or #' CDR3 amino acid (aa). #' @param group.by The variable to use for grouping. -#' @param motif.length The length of the kmer to analyze +#' @param motif.length The length of the kmer to analyze. #' @param top.motifs Return the n most variable motifs as a function of #' median absolute deviation. -#' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom reshape2 melt #' @importFrom stats mad diff --git a/R/percentVJ.R b/R/percentVJ.R index 2bebe9c1..bcd5d501 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -15,7 +15,7 @@ #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" #' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom stringr str_split str_sort #' @importFrom reshape2 melt diff --git a/R/startracDiversity.R b/R/startracDiversity.R index e5adc19b..f59ce58c 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -1,11 +1,11 @@ #' Startrac-based diversity indices for single-cell RNA-seq #' #' @description This function utilizes the Startrac approach derived from -#' \href{https://pubmed.ncbi.nlm.nih.gov/30479382/}{PMID: 30479382} +#' \href{https://pubmed.ncbi.nlm.nih.gov/30479382/}{PMID: 30479382}. #' Required to run the function, the "type" variable needs to include the #' difference in where the cells were derived. The output of this function -#' will produce 3 indices: expa (clonal expansion), migra -#' (cross-tissue migration), and trans (state transition). In order +#' will produce 3 indices: *expa* (clonal expansion), *migra* +#' (cross-tissue migration), and *trans* (state transition). In order #' to understand the underlying analyses of the outputs please #' read and cite the linked manuscript. #' @@ -34,11 +34,11 @@ #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param type The variable in the meta data that provides tissue type -#' @param group.by The variable in the meta data to group by, often samples -#' @param exportTable Returns the data frame used for forming the graph -#' @param palette Colors to use in visualization - input any hcl.pals() +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". +#' @param type The variable in the meta data that provides tissue type. +#' @param group.by The variable in the meta data to group by, often samples. +#' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @importFrom reshape2 melt #' @import ggplot2 #' @export diff --git a/R/vizGenes.R b/R/vizGenes.R index b4616263..e449532e 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -27,7 +27,7 @@ #' respective repertoire size #' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph. -#' @param palette Colors to use in visualization - input any hcl.pals() +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom stringr str_split #' @importFrom stats sd diff --git a/man/StartracDiversity.Rd b/man/StartracDiversity.Rd index 68e965d9..b2234539 100644 --- a/man/StartracDiversity.Rd +++ b/man/StartracDiversity.Rd @@ -24,26 +24,26 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} -\item{type}{The variable in the meta data that provides tissue type} +\item{type}{The variable in the meta data that provides tissue type.} -\item{group.by}{The variable in the meta data to group by, often samples} +\item{group.by}{The variable in the meta data to group by, often samples.} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot object of Startrac diversity metrics } \description{ This function utilizes the Startrac approach derived from -\href{https://pubmed.ncbi.nlm.nih.gov/30479382/}{PMID: 30479382} +\href{https://pubmed.ncbi.nlm.nih.gov/30479382/}{PMID: 30479382}. Required to run the function, the "type" variable needs to include the difference in where the cells were derived. The output of this function -will produce 3 indices: expa (clonal expansion), migra -(cross-tissue migration), and trans (state transition). In order +will produce 3 indices: *expa* (clonal expansion), *migra* +(cross-tissue migration), and *trans* (state transition). In order to understand the underlying analyses of the outputs please read and cite the linked manuscript. } diff --git a/man/abundanceContig.Rd b/man/abundanceContig.Rd index 91d82c66..f03ee4d2 100644 --- a/man/abundanceContig.Rd +++ b/man/abundanceContig.Rd @@ -36,7 +36,7 @@ relative distributions.} \item{exportTable}{Returns the data frame used for forming the graph to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the total or relative abundance of clonotypes diff --git a/man/alluvialClones.Rd b/man/alluvialClones.Rd index 865bc460..c53746ea 100644 --- a/man/alluvialClones.Rd +++ b/man/alluvialClones.Rd @@ -24,9 +24,9 @@ CDR3 nucleotide (nt) or CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} -\item{y.axes}{The columns that will separate the proportional +\item{y.axes}{The columns that will separate the proportional . visualizations.} \item{color}{The column header or clonotype(s) to be highlighted.} @@ -36,9 +36,9 @@ visualizations.} \item{facet}{The column label to separate.} \item{exportTable}{Exports a table of the data into the global -environment in addition to the visualization} +environment in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{@param}{palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ Alluvial ggplot comparing clonotype distribution. diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index e3ed5120..034b8a62 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -37,13 +37,14 @@ For example, "Type" for lung vs peripheral blood comparison} Returns ggplot of the clonotype bias } \description{ -Clonotype bias method was developed and outlined from a single-cell -\href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} -characterizing CD4 responses to acute and chronic infection. The metric seeks to +The metric seeks to quantify how individual clones are skewed towards a specific cellular -compartment or cluster. A clonotype bias of 1 indicates that a clonotype +compartment or cluster. A clonotype bias of *1* - indicates that a clonotype is composed of cells from a single compartment or cluster, while a clonotype -bias of 0 matches the background subtype distribution. +bias of *0* - matches the background subtype distribution. Please read and cite +the following +\href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} +if using \code{\link{clonalBias()}} } \examples{ #Making combined contig data diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index 671795a7..ef399c16 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -28,13 +28,13 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{samples}{The specific samples to isolate for visualization.} \item{clonotypes}{The specific clonal sequences of interest.} -\item{top.clones}{The top number of clonotype sequences per group} +\item{top.clones}{The top number of clonotype sequences per group.} \item{highlight.clones}{Clonal sequences to highlight, if present, all other clones returned will be grey.} @@ -42,14 +42,14 @@ all other clones returned will be grey.} \item{relabel.clones}{Simplify the legend of the graph by returning clones that are numerically indexed.} -\item{group.by}{If using a single-cell object, the column header +\item{group.by}{If using a single-cell object, the column header . to group the new list. NULL will return clusters.} \item{graph}{The type of graph produced, either "alluvial" or "area".} \item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the proportion of total sequencing read of diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index a05ae0a8..f4b52ad7 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -27,34 +27,34 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} -\item{group.by}{The variable to use for grouping} +\item{group.by}{The variable to use for grouping.} -\item{x.axis}{Additional variable in which to split the x.axis} +\item{x.axis}{Additional variable in which to split the x.axis.} \item{metrics}{The indices to use in diversity calculations - "shannon", "inv.simpson", -"norm.entropy", "gini.simpson", "chao1", "ACE"} +"norm.entropy", "gini.simpson", "chao1", "ACE".} \item{exportTable}{Exports a table of the data into the global environment -in addition to the visualization} +in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} \item{n.boots}{number of bootstraps to downsample in order to get mean diversity} \item{return.boots}{export boot strapped values calculated - -will automatically exportTable = TRUE} +will automatically exportTable = TRUE.} -\item{skip.boots}{remove downsampling and boot strapping from the calculation} +\item{skip.boots}{remove downsampling and boot strapping from the calculation.} } \value{ -ggplot of the diversity of clonotype sequences across list +ggplot of the diversity of clones by group } \description{ -This function calculates traditional measures of diversity - Shannon, -inverse Simpson, normalized entropy, Gini-Simpson, Chao1 index, and -abundance-based coverage estimators (ACE) measure of species evenness by sample or group. +This function calculates traditional measures of diversity - **Shannon**, +**inverse Simpson**, **normalized entropy**, **Gini-Simpson**, **Chao1 index**, and +**abundance-based coverage estimators (ACE)** measure of species evenness by sample or group. The function automatically down samples the diversity metrics using 100 boot straps The group parameter can be used to condense the individual samples. If a matrix output for the data is preferred, set exportTable = TRUE. @@ -62,24 +62,30 @@ samples. If a matrix output for the data is preferred, set exportTable = TRUE. \details{ The formulas for the indices and estimators are as follows: -\deqn{Shannon Index: H = - \sum p_i \cdot \log(p_i)} +Shannon Index: +\deqn{H = - \sum p_i \cdot \log(p_i)} -\deqn{Inverse Simpson Index: D^{-1} = 1 / \sum p_i^2} +Inverse Simpson Index: +\deqn{ D^{-1} = 1 / \sum p_i^2} -\deqn{Normalized Entropy: E^H = H / \log(S)} +Normalized Entropy: +\deqn{E^H = H / \log(S)} -\deqn{Gini-Simpson Index: 1 - D = 1 - \sum p_i^2} +Gini-Simpson Index: +\deqn{1 - D = 1 - \sum p_i^2} -\deqn{Chao1 Index: \hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} +Chao1 Index: +\deqn{\hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} -\deqn{Abundance-based Coverage Estimator (ACE): \hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} +Abundance-based Coverage Estimator (ACE): +\deqn{\hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} Where: \itemize{ - \item{ \( p_i \) is the proportion of species \( i \) in the dataset.} - \item{ \( S \) is the total number of species.} - \item{ \( n_1 \) and \( n_2 \) are the number of singletons and doubletons, respectively.} - \item{ \( S_{abundant} \), \( S_{rare} \), \( C_{rare} \), and \( F_1 \) are parameters derived from the data.} + \item{\(p_i\) is the proportion of species \(i\) in the dataset.} + \item{\(S\) is the total number of species.} + \item{\(n_1\) and \(n_2\) are the number of singletons and doubletons, respectively.} + \item{\(S_{abundant}\), \(S_{rare}\), \(C_{rare}\), and \(F_1\) are parameters derived from the data.} } } \examples{ diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index e70b6e62..6d2daeb8 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -33,7 +33,7 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the space occupied by the specific proportion of clonotypes diff --git a/man/clonalLength.Rd b/man/clonalLength.Rd index dc45ce98..4b62b70f 100644 --- a/man/clonalLength.Rd +++ b/man/clonalLength.Rd @@ -34,7 +34,7 @@ relative distributions.} \item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the discrete or relative length distributions of diff --git a/man/clonalNetwork.Rd b/man/clonalNetwork.Rd index cd54978c..1afba485 100644 --- a/man/clonalNetwork.Rd +++ b/man/clonalNetwork.Rd @@ -22,31 +22,31 @@ clonalNetwork( \arguments{ \item{sc}{The single-cell object after \code{\link{combineExpression}}.} -\item{reduction}{The name of the dimensional reduction of the single-cell object} +\item{reduction}{The name of the dimensional reduction of the single-cell object.} \item{identity}{A variable in the meta data to use for the nodes.} \item{filter.clones}{Use to select the top n clones (filter.clones = 2000) or n of clones based on the minimum number of all the comparators (filter.clone = "min").} -\item{filter.identity}{Display the network for a specific level of the indicated identity} +\item{filter.identity}{Display the network for a specific level of the indicated identity.} -\item{filter.proportion}{Remove clonotypes from the network below a specific proportion} +\item{filter.proportion}{Remove clonotypes from the network below a specific proportion.} -\item{filter.graph}{Remove the reciprocal edges from the half of the graph, -allowing for cleaner visualization} +\item{filter.graph}{Remove the reciprocal edges from the half of the graph, +allowing for cleaner visualization.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{exportTable}{Exports a table of the data into the global -environment in addition to the visualization} +environment in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} \item{exportClones}{Exports a table of clones that are shared across multiple identity groups and ordered by the total number diff --git a/man/clonalOccupy.Rd b/man/clonalOccupy.Rd index a7b65444..6781b647 100644 --- a/man/clonalOccupy.Rd +++ b/man/clonalOccupy.Rd @@ -18,20 +18,20 @@ clonalOccupy( \arguments{ \item{sc}{The single-cell object after \code{\link{combineExpression}}.} -\item{x.axis}{The variable in the meta data to graph along the x.axis} +\item{x.axis}{The variable in the meta data to graph along the x.axis.} -\item{label}{Include the number of clonotype in each category by x.axis variable} +\item{label}{Include the number of clonotype in each category by x.axis variable.} -\item{facet.by}{The column header used for faceting the graph} +\item{facet.by}{The column header used for faceting the graph.} -\item{proportion}{Convert the stacked bars into relative proportion} +\item{proportion}{Convert the stacked bars into relative proportion.} \item{na.include}{Visualize NA values or not.} \item{exportTable}{Exports a table of the data into the global -environment in addition to the visualization} +environment in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ Stacked bar plot of counts of cells by clonotype frequency group @@ -39,7 +39,7 @@ Stacked bar plot of counts of cells by clonotype frequency group \description{ View the count of clonotypes frequency group in seurat or SCE object meta data after \code{\link{combineExpression}}. The visualization will take the -new meta data variable "cloneSize" and plot the number of cells with +new meta data variable **"cloneSize"** and plot the number of cells with each designation using a secondary variable, like cluster. Credit to the idea goes to Drs. Carmona and Andreatta and their work with \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index bdc5b871..f160b300 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -28,14 +28,14 @@ for the base numbers.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{The variable to use for grouping} +\item{group.by}{The variable to use for grouping.} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ -ggplot of the clonotypic overlap between elements of a list +ggplot of the overlap of clones by group } \description{ This functions allows for the calculation and visualizations of @@ -44,19 +44,24 @@ various overlap metrics for clonotypes. \details{ The formulas for the indices are as follows: -\deqn{Overlap Coefficient: OC = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} +Overlap Coefficient: +\deqn{overlap = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} -\deqn{Raw Count Overlap: RCO = \sum \min(a_i, b_i)} +Raw Count Overlap: +\deqn{raw = \sum \min(a_i, b_i)} -\deqn{Morisita Index: MI = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} +Morisita Index: +\deqn{morisita = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} -\deqn{Jaccard Index: JI = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} +Jaccard Index: +\deqn{jaccard = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} -\deqn{Cosine Similarity: CS = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} +Cosine Similarity: +\deqn{cosine = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} Where: \itemize{ - \item{ \( a_i \) and \( b_i \) are the abundances of species \( i \) in groups A and B, respectively.} + \item{\(a_i\) and \(b_i\) are the abundances of species \(i\) in groups A and B, respectively.} } } \examples{ diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index f736592e..cff43bee 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -25,14 +25,14 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{group.by}{The variable to use for grouping.} -\item{exportTable}{Exports a table of the data into the global -environment in addition to the visualization} +\item{exportTable}{Exports a table of the data into the global. +environment in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{@param}{palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the space occupied by the specific rank of clonotypes diff --git a/man/clonalQuant.Rd b/man/clonalQuant.Rd index ed010ae0..fb3e23de 100644 --- a/man/clonalQuant.Rd +++ b/man/clonalQuant.Rd @@ -34,7 +34,7 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL"} \item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the total or relative unique clonotypes diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd index f251859d..852d96a8 100644 --- a/man/clonalRarefaction.Rd +++ b/man/clonalRarefaction.Rd @@ -25,36 +25,37 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} -\item{group.by}{The variable to use for grouping} +\item{group.by}{The variable to use for grouping.} \item{plot.type}{sample-size-based rarefaction/extrapolation curve (\code{type = 1}); sample completeness curve (\code{type = 2}); coverage-based rarefaction/extrapolation curve (\code{type = 3}).} \item{hill.numbers}{The Hill numbers to be plotted out (0 - species richness, 1 - Shannon, 2 - Simpson)} -\item{n.boots}{The number of bootstraps to downsample in order to get mean diversity} +\item{n.boots}{The number of bootstraps to downsample in order to get mean diversity.} \item{exportTable}{Exports a table of the data into the global -environment in addition to the visualization} +environment in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \description{ -This functions uses the Hill numbers of order q: species richness (q = 0), -Shannon diversity (q = 1, the exponential of Shannon entropy) and Simpson -diversity (q = 2, the inverse of Simpson concentration) to compute diversity +This functions uses the Hill numbers of order q: species richness (**q = 0&&), +Shannon diversity (**q = 1**, the exponential of Shannon entropy) and Simpson +diversity (**q = 2**, the inverse of Simpson concentration) to compute diversity estimates for rarefaction and extrapolation. The function relies on -\code{\link[iNEXT]{iNEXT}} and read/please cite the +\code{\link[iNEXT]{iNEXT}}. Please read and cite the \href{https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12613}{manuscript} if using this function. } -\details{ -#' @examples +\examples{ #Making combined contig data combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) clonalRarefaction(combined[c(1,2)], cloneCall = "gene", n.boots = 3) + + } diff --git a/man/clonalScatter.Rd b/man/clonalScatter.Rd index 03b10f30..8b808f48 100644 --- a/man/clonalScatter.Rd +++ b/man/clonalScatter.Rd @@ -25,32 +25,31 @@ clonalScatter( CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} -\item{x.axis}{name of the list element to appear on the x.axis} +\item{x.axis}{name of the list element to appear on the x.axis.} -\item{y.axis}{name of the list element to appear on the y.axis} +\item{y.axis}{name of the list element to appear on the y.axis.} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{dot.size}{either total or the name of the list element to -use for size of dots} +use for size of dots.} -\item{group.by}{The variable to use for grouping} +\item{group.by}{The variable to use for grouping.} -\item{graph}{graph either proportion or raw clonotype count} +\item{graph}{graph either proportion or raw clonotype count.} \item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the relative clonotype numbers } \description{ This function produces a scatter plot directly comparing the specific clonotypes -between two samples. The clonotypes will be categorized by counts into singlets or multiplets, -either exclusive or shared between the selected samples. Visualization inspired -by the work of \href{https://pubmed.ncbi.nlm.nih.gov/32103181/}{Wu, T, et al 2020}. +between two samples. The clonotypes will be categorized by counts into singlets +or expanded, either exclusive or shared between the selected samples. } \examples{ #Making combined contig data diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index fe2efb90..ad13396a 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -25,18 +25,17 @@ CDR3 nucleotide (nt), CDR3 amino acid (aa), or VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} +e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{method}{The clustering parameter for the dendrogram.} \item{threshold}{Numerical vector containing the thresholds the grid search was performed over.} -\item{group.by}{The variable to use for grouping} +\item{group.by}{The variable to use for grouping.} -\item{exportTable}{Returns the data frame used for forming the graph.} - -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{exportTable}{Returns the data frame used for forming the graph. +' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot dendrogram of the clone size distribution @@ -44,9 +43,8 @@ ggplot dendrogram of the clone size distribution \description{ This function produces a hierarchical clustering of clonotypes by sample using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold -model in -\href{https://bioconductor.org/packages/devel/bioc/html/powerTCR.html}{powerTCR R package}. -Please read and cite PMID: 30485278 if using the function for analyses. +model. If using this model please read and cite +\href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. } \examples{ #Making combined contig data diff --git a/man/highlightClones.Rd b/man/highlightClones.Rd index 4fee86c5..1c209975 100644 --- a/man/highlightClones.Rd +++ b/man/highlightClones.Rd @@ -11,7 +11,7 @@ highlightClones( ) } \arguments{ -\item{sc}{The Seurat object to attach after \code{\link{combineExpression}}} +\item{sc}{The single-cell object to attach after \code{\link{combineExpression}}} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -20,11 +20,11 @@ VDJC gene + CDR3 nucleotide (strict).} \item{sequence}{The specific sequence or sequence to highlight} } \value{ -Seurat object with new meta data column for indicated clones +Single-cell object object with new meta data column for indicated clones } \description{ Use a specific clonotype sequence to highlight on top of the dimensional -reduction in Seurat object. +reduction in single-cell object. } \examples{ #Getting the combined contigs diff --git a/man/loadContigs.Rd b/man/loadContigs.Rd index b52fc20c..af87085f 100644 --- a/man/loadContigs.Rd +++ b/man/loadContigs.Rd @@ -21,16 +21,19 @@ function with \code{\link{combineTCR}} or \code{\link{combineBCR}}. If using data derived from filtered outputs of 10X Genomics, there is no need to use this function as the data is already compatible. The function assumes if listing multiple directories, there are distinct outputs with -unmodified file names in them. The files that this function parses includes: -WAT3R = "barcode_results.csv", 10X = "filtered_contig_annotation.csv", -AIRR = "airr_rearrangement.tsv", TRUST4 = "barcode_report.tsv", and -BD = "Contigs_AIRR.tsv". +unmodified file names in them. +} +\details{ +The files that this function parses includes: +\itemize{ + \item 10X = "filtered_contig_annotation.csv" + \item AIRR = "airr_rearrangement.tsv" + \item BD = "Contigs_AIRR.tsv" + \item TRUST4 = "barcode_report.tsv" + \item WAT3R = "barcode_results.csv" } -\examples{ -\dontrun{ -dir <- c("./Sample1/outs/") -contig.list <- loadContigs(dir, format = "10X") } +\examples{ TRUST4 <- read.csv("https://www.borch.dev/uploads/contigs/TRUST4_contigs.csv") contig.list <- loadContigs(TRUST4, format = "TRUST4") diff --git a/man/percentAA.Rd b/man/percentAA.Rd index 11d2a61f..a7cdf2d4 100644 --- a/man/percentAA.Rd +++ b/man/percentAA.Rd @@ -17,15 +17,15 @@ percentAA( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL".} \item{group.by}{The variable to use for grouping.} -\item{aa.length}{The maximum length of the cdr3 amino acid sequence} +\item{aa.length}{The maximum length of the CDR3 amino acid sequence.} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of stacked bar graphs of amino acid proportions diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd index 215fefdd..75b069b9 100644 --- a/man/percentGenes.Rd +++ b/man/percentGenes.Rd @@ -17,15 +17,15 @@ percentGenes( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL".} -\item{gene}{"V", "D" or "J"} +\item{gene}{"V", "D" or "J".} \item{group.by}{The variable to use for grouping.} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of percentage of indicated genes as a heatmap diff --git a/man/percentKmer.Rd b/man/percentKmer.Rd index e568c1ef..e48215ed 100644 --- a/man/percentKmer.Rd +++ b/man/percentKmer.Rd @@ -19,21 +19,21 @@ percentKmer( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} +\item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL".} \item{cloneCall}{How to call the clonotype - CDR3 nucleotide (nt) or CDR3 amino acid (aa).} \item{group.by}{The variable to use for grouping.} -\item{motif.length}{The length of the kmer to analyze} +\item{motif.length}{The length of the kmer to analyze.} \item{top.motifs}{Return the n most variable motifs as a function of median absolute deviation.} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of percentage of kmers as a heatmap diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd index 80e7d2f3..56a9310d 100644 --- a/man/percentVJ.Rd +++ b/man/percentVJ.Rd @@ -22,7 +22,7 @@ percentVJ( \item{exportTable}{Returns the data frame used for forming the graph} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of percentage of V and J gene pairings as a heatmap diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index b7da4749..aecc450a 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -40,7 +40,7 @@ respective repertoire size} \item{exportTable}{Returns the data frame used for forming the graph.} -\item{palette}{Colors to use in visualization - input any hcl.pals()} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot bar diagram or heatmap of gene usage From 94a2680c378637e400d6d30dc1a6900ea3536631 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 6 Oct 2023 09:55:24 -0500 Subject: [PATCH 076/142] Add support for new formats Support json and omniscope --- DESCRIPTION | 11 ++++---- NAMESPACE | 1 + NEWS | 1 + R/clonalBias.R | 31 ++++++++++----------- R/loadContigs.R | 51 ++++++++++++++++++++++++++++------- man/alluvialClones.Rd | 2 +- man/clonalBias.Rd | 8 +++--- man/clonalProportion.Rd | 2 +- man/clonalSizeDistribution.Rd | 5 ++-- man/loadContigs.Rd | 4 ++- 10 files changed, 78 insertions(+), 38 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 8cb2504d..579cab29 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -14,27 +14,28 @@ Depends: ggplot2, R (>= 4.0) Imports: - stringdist, + cubature, dplyr, - reshape2, ggalluvial, - stringr, plyr, methods, - rlang, igraph, ggraph, stats, Rcpp, evmix, ggdendro, - cubature, iNEXT, grDevices, + reshape2, + rjson, + rlang, S4Vectors, seqinr, SeuratObject, SingleCellExperiment, + stringr, + stringdist, SummarizedExperiment, tidygraph, truncdist, diff --git a/NAMESPACE b/NAMESPACE index 6e4f15af..52d733ba 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -87,6 +87,7 @@ importFrom(plyr,join) importFrom(plyr,llply) importFrom(reshape2,dcast) importFrom(reshape2,melt) +importFrom(rjson,fromJSON) importFrom(rlang,"%||%") importFrom(seqinr,read.fasta) importFrom(seqinr,translate) diff --git a/NEWS b/NEWS index 1a590b08..18ef805f 100644 --- a/NEWS +++ b/NEWS @@ -25,6 +25,7 @@ UNDERLYING CHANGES * Added features to clonalCompare to allow for highlighting sequences, relabeling clonotypes. * License change to MIT * group.by and split.by have been consolidated into single group.by parameter +* Added support for .json and Omniscope formats DEPRECATED AND DEFUNCT diff --git a/R/clonalBias.R b/R/clonalBias.R index 95fe16f6..41017ae7 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -27,7 +27,7 @@ #' cloneCall = "aa", #' split.by = "Patient", #' group.by = "seurat_clusters", -#' n.boots = 20, +#' n.boots = 5, #' min.expand = 2) #' #' @@ -39,23 +39,23 @@ #' @param group.by The variable to use for calculating bias #' @param split.by The variable to use for calculating the baseline frequencies. #' For example, "Type" for lung vs peripheral blood comparison -#' @param n.boots number of bootstraps to downsample -#' @param min.expand clonotype frequency cut off for the purpose of comparison -#' @param exportTable Returns the data frame used for forming the graph +#' @param n.boots number of bootstraps to downsample. +#' @param min.expand clonotype frequency cut off for the purpose of comparison. +#' @param exportTable Returns the data frame used for forming the graph. #' @import ggplot2 #' @importFrom stringr str_sort #' @export #' @return Returns ggplot of the clonotype bias clonalBias <- function(df, - cloneCall="strict", - split.by=NULL, - group.by=NULL, - n.boots = 20, - min.expand=10, - exportTable = FALSE) { + cloneCall="strict", + split.by=NULL, + group.by=NULL, + n.boots = 20, + min.expand=10, + exportTable = FALSE) { cloneCall <- .theCall(cloneCall) #Calculating bias - bias <- get_clono_bias(df, + bias <- .get_clono_bias(df, split.by = split.by, group.by = group.by , cloneCall=cloneCall, @@ -63,7 +63,7 @@ clonalBias <- function(df, df_shuffle.list <- list() #Bootstrapping for (ii in seq_len(n.boots)) { - df_shuffle.list[[ii]] <- get_clono_bias(df, split.by = split.by, + df_shuffle.list[[ii]] <- .get_clono_bias(df, split.by = split.by, group.by = group.by, cloneCall=cloneCall, min.expand=min.expand, @@ -81,6 +81,7 @@ clonalBias <- function(df, corrected_p <- 1-(0.05/nrow(bias)) bias$Top_state <- factor(bias$Top_state, str_sort(unique(bias$Top_state), numeric = TRUE)) + #Calculating Bias Z-score bias$Z.score <- NA for(i in seq_len(nrow(bias))) { stat.pos <- bias[i,]$ncells @@ -101,7 +102,7 @@ clonalBias <- function(df, return(plot) } -get_clono_bg <- function(df, +.get_clono_bg <- function(df, split.by=split.by, group.by=group.by, cloneCall=cloneCall, @@ -125,7 +126,7 @@ get_clono_bg <- function(df, } #Code Derived from -get_clono_bias <- function(df, +.get_clono_bias <- function(df, split.by=NULL, group.by=NULL, cloneCall=cloneCall, @@ -142,7 +143,7 @@ get_clono_bias <- function(df, freq_diff=double(), bias=double()) - bg <- get_clono_bg(df, + bg <- .get_clono_bg(df, split.by=split.by, min.expand = min.expand, group.by = group.by, diff --git a/R/loadContigs.R b/R/loadContigs.R index 7db515ba..f6c0b686 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -11,7 +11,9 @@ #' \itemize{ #' \item 10X = "filtered_contig_annotation.csv" #' \item AIRR = "airr_rearrangement.tsv" +#' \item JSON = ".json" #' \item BD = "Contigs_AIRR.tsv" +#' \item Omniscope = ".csv" #' \item TRUST4 = "barcode_report.tsv" #' \item WAT3R = "barcode_results.csv" #' } @@ -29,8 +31,9 @@ #' #' @param dir The directory in which contigs are located or a list with contig elements #' @param format The format of the single-cell contig, currently supporting: -#' "10X", "AIRR", "TRUST4", "BD", and "WAT3R" +#' "10X", "AIRR", "BD", "JSON", "Omniscope", "TRUST4", and "WAT3R" #' @importFrom utils read.csv read.delim +#' @importFrom rjson fromJSON #' @export #' @return List of contigs for further processing in scRepertoire loadContigs <- function(dir, @@ -40,12 +43,19 @@ loadContigs <- function(dir, format.list <- list("WAT3R" = "barcode_results.csv", "10X" = "filtered_contig_annotation.csv", "AIRR" = "airr_rearrangement.tsv", + "JSON" = ".json", "TRUST4" = "barcode_report.tsv", - "BD" = "Contigs_AIRR.tsv") + "BD" = "Contigs_AIRR.tsv", + "Omniscope" =c("_OSB.csv", "_OST.csv")) file.pattern <- format.list[[format]] - contig.files <- list.files(dir, file.pattern, recursive = TRUE, full.names = TRUE) - if (format %in% c("10X", "WAT3R")) { + contig.files <- list.files(dir, paste0(file.pattern, collapse = "|"), recursive = TRUE, full.names = TRUE) + + if (format %in% c("10X", "WAT3R", "Omniscope")) { df <- lapply(contig.files, read.csv) + } else if("json") { + df <- lapply(contig.files, function(x) { + tmp <- as.data.frame(fromJSON(x)) + }) } else { df <- lapply(contig.files, read.delim) } @@ -55,11 +65,14 @@ loadContigs <- function(dir, } loadFunc <- switch(format, - "10X" = .parse10x, - "AIRR" = .parseAIRR, - "TRUST4" = .parseTRUST4, - "BD" = .parseBD, - "WAT3R" = .parseWAT3R, + "10X" = .parse10x, + "AIRR" = .parseAIRR, + "JSON" = .parseJSON, + "TRUST4" = .parseTRUST4, + "BD" = .parseBD, + "WAT3R" = .parseWAT3R, + "Omniscope" = .parseOmniscope, + stop("Invalid format provided")) df <- loadFunc(df) @@ -169,3 +182,23 @@ loadContigs <- function(dir, return(df) } + +.parseOmniscope <- function(df) { + for (i in seq_along(df)) { + df[[i]] <- df[[i]][,c("contig_id", "locus", "umi_count", "v_call", "d_call", "j_call", "c_call", "cdr3", "cdr3_aa")] + colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") + df[[i]] <- df[[i]][with(df[[i]], order(reads, chain)),] + } + return(df) +} + +.parseJSON <- function(df) { + for (i in seq_along(df)) { + df[[i]] <- do.call(rbind, df[[i]]) + df[[i]][df[[i]] == ""] <- NA + df[[i]] <- as.data.frame(df[[i]]) + df[[i]] <- df[[i]][,c("cell_id", "locus", "consensus_count", "v_call", "d_call", "j_call", "c_call", "junction", "junction_aa")] + colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") + } + return(df) +} diff --git a/man/alluvialClones.Rd b/man/alluvialClones.Rd index c53746ea..659ed06d 100644 --- a/man/alluvialClones.Rd +++ b/man/alluvialClones.Rd @@ -38,7 +38,7 @@ visualizations.} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization.} -\item{@param}{palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ Alluvial ggplot comparing clonotype distribution. diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index 034b8a62..e0f380c3 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -27,11 +27,11 @@ For example, "Type" for lung vs peripheral blood comparison} \item{group.by}{The variable to use for calculating bias} -\item{n.boots}{number of bootstraps to downsample} +\item{n.boots}{number of bootstraps to downsample.} -\item{min.expand}{clonotype frequency cut off for the purpose of comparison} +\item{min.expand}{clonotype frequency cut off for the purpose of comparison.} -\item{exportTable}{Returns the data frame used for forming the graph} +\item{exportTable}{Returns the data frame used for forming the graph.} } \value{ Returns ggplot of the clonotype bias @@ -64,7 +64,7 @@ clonalBias(scRep_example, cloneCall = "aa", split.by = "Patient", group.by = "seurat_clusters", - n.boots = 20, + n.boots = 5, min.expand = 2) diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index cff43bee..ff627394 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -32,7 +32,7 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{exportTable}{Exports a table of the data into the global. environment in addition to the visualization.} -\item{@param}{palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot of the space occupied by the specific rank of clonotypes diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index ad13396a..2a634b0d 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -34,8 +34,9 @@ the grid search was performed over.} \item{group.by}{The variable to use for grouping.} -\item{exportTable}{Returns the data frame used for forming the graph. -' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} +\item{exportTable}{Returns the data frame used for forming the graph.} + +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot dendrogram of the clone size distribution diff --git a/man/loadContigs.Rd b/man/loadContigs.Rd index af87085f..075eaac3 100644 --- a/man/loadContigs.Rd +++ b/man/loadContigs.Rd @@ -10,7 +10,7 @@ loadContigs(dir, format = "10X") \item{dir}{The directory in which contigs are located or a list with contig elements} \item{format}{The format of the single-cell contig, currently supporting: -"10X", "AIRR", "TRUST4", "BD", and "WAT3R"} +"10X", "AIRR", "BD", "JSON", "Omniscope", "TRUST4", and "WAT3R"} } \value{ List of contigs for further processing in scRepertoire @@ -28,7 +28,9 @@ The files that this function parses includes: \itemize{ \item 10X = "filtered_contig_annotation.csv" \item AIRR = "airr_rearrangement.tsv" + \item JSON = ".json" \item BD = "Contigs_AIRR.tsv" + \item Omniscope = ".csv" \item TRUST4 = "barcode_report.tsv" \item WAT3R = "barcode_results.csv" } From fb1a6ceaad80f9ce52ba75de1b75687929285d4d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 6 Oct 2023 15:24:11 -0500 Subject: [PATCH 077/142] clonalAbundance manual change --- NAMESPACE | 2 +- NEWS | 1 + R/abundanceContig.R | 4 ++-- man/{abundanceContig.Rd => clonalAbundance.Rd} | 8 ++++---- vignettes/vignette.Rmd | 8 ++++---- 5 files changed, 12 insertions(+), 11 deletions(-) rename man/{abundanceContig.Rd => clonalAbundance.Rd} (93%) diff --git a/NAMESPACE b/NAMESPACE index 52d733ba..6b6e8a2e 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -1,9 +1,9 @@ # Generated by roxygen2: do not edit by hand export(StartracDiversity) -export(abundanceContig) export(addVariable) export(alluvialClones) +export(clonalAbundance) export(clonalBias) export(clonalCompare) export(clonalDiversity) diff --git a/NEWS b/NEWS index 18ef805f..41585d6f 100644 --- a/NEWS +++ b/NEWS @@ -15,6 +15,7 @@ NEW FEATURES * Changed highlightClonotypes to highlightClones * Changed lengthContigs to clonalLength * Changed occupiedscRepertoire to clonalOccupy +* Changed abundanceContig to clonalAbundance * Changed alluvialClonotypes to clonalClones UNDERLYING CHANGES diff --git a/R/abundanceContig.R b/R/abundanceContig.R index f6480367..6b9bd976 100644 --- a/R/abundanceContig.R +++ b/R/abundanceContig.R @@ -12,7 +12,7 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' abundanceContig(combined, cloneCall = "gene", scale = FALSE) +#' clonalAbundance(combined, cloneCall = "gene", scale = FALSE) #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. @@ -32,7 +32,7 @@ #' @export #' @return ggplot of the total or relative abundance of clonotypes #' across quanta -abundanceContig <- function(df, +clonalAbundance <- function(df, cloneCall = "strict", chain = "both", scale=FALSE, diff --git a/man/abundanceContig.Rd b/man/clonalAbundance.Rd similarity index 93% rename from man/abundanceContig.Rd rename to man/clonalAbundance.Rd index f03ee4d2..79cf8b6a 100644 --- a/man/abundanceContig.Rd +++ b/man/clonalAbundance.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand % Please edit documentation in R/abundanceContig.R -\name{abundanceContig} -\alias{abundanceContig} +\name{clonalAbundance} +\alias{clonalAbundance} \title{Demonstrate the relative abundance of clonotypes by group or sample.} \usage{ -abundanceContig( +clonalAbundance( df, cloneCall = "strict", chain = "both", @@ -55,6 +55,6 @@ preferred, set exportTable = TRUE. combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -abundanceContig(combined, cloneCall = "gene", scale = FALSE) +clonalAbundance(combined, cloneCall = "gene", scale = FALSE) } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index d3097d31..80edf3c1 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -240,16 +240,16 @@ clonalQuant(combined, -## abundanceContig +## clonalAbundance -We can also examine the relative distribution of clonotypes by abundance. Here `abundanceContig()` will produce a line graph with a total number of clonotypes by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the contig object using the **group.by** variable in the function. +We can also examine the relative distribution of clonotypes by abundance. Here `clonalAbundance()` will produce a line graph with a total number of clonotypes by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the contig object using the **group.by** variable in the function. ```{r} -abundanceContig(combined, +clonalAbundance(combined, cloneCall = "gene", scale = FALSE) -abundanceContig(combined, +clonalAbundance(combined, group.by = "Type", scale = TRUE) ``` From 8ecd957441c0739f8a625ef352698fd10bfe29ea Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 6 Oct 2023 15:52:01 -0500 Subject: [PATCH 078/142] Adding equations for cloneSizeDist --- R/{abundanceContig.R => clonalAbundance.R} | 0 R/clonalSizeDistribution.R | 32 ++++++++++++++++++++-- R/createHTOContigList.R | 20 +++++++------- man/clonalSizeDistribution.Rd | 31 +++++++++++++++++++-- 4 files changed, 67 insertions(+), 16 deletions(-) rename R/{abundanceContig.R => clonalAbundance.R} (100%) diff --git a/R/abundanceContig.R b/R/clonalAbundance.R similarity index 100% rename from R/abundanceContig.R rename to R/clonalAbundance.R diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 717db3cb..62e3b736 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -1,11 +1,37 @@ -#' Hierarchical clustering of clonotypes on clonotype size and -#' Jensen-Shannon divergence +#' Hierarchical clustering of clones using Gamma-GPD spliced threshold model #' #' This function produces a hierarchical clustering of clonotypes by sample -#' using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold +#' using discrete gamma-GPD spliced threshold #' model. If using this model please read and cite #' \href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. #' +#' @details +#' The probability density function (pdf) for the Generalized Pareto Distribution (GPD) is given by: +#' \deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ +#' f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) +#' } +#' where: +#' \itemize{ +#' \item{\(\mu\) is a location parameter,} +#' \item{\(\sigma > 0\) is a scale parameter,} +#' \item{\(\xi\) is a shape parameter, and} +#' \item{\(x \ge \mu\) if \(\xi \ge 0\) and \(\mu \le x \le \mu - \sigma/\xi\) if \(\xi < 0\).} +#' } +#' +#' +#' +#' The probability density function (pdf) for the Gamma Distribution is given by: +#' \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ +#' f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) +#' } +#' where: +#' \itemize{ +#' \item{\(\alpha > 0\) is the shape parameter,} +#' \item{\(\beta > 0\) is the scale parameter,} +#' \item{\(x \ge 0\),} +#' \item{\(\Gamma(\alpha)\) is the gamma function of \(\alpha\).} +#' } +#' #' @examples #' #Making combined contig data #' combined <- combineTCR(contig_list, diff --git a/R/createHTOContigList.R b/R/createHTOContigList.R index db38cd7d..58c19fb6 100644 --- a/R/createHTOContigList.R +++ b/R/createHTOContigList.R @@ -1,20 +1,21 @@ #' Generate a contig list from a multiplexed experiment #' -#' Multiplexing single-cell sequencing runs is an efficient method for quantifying -#' multiple samples or conditions simultaneously. Unfortunately, the hashing information -#' is not stored in the TCR sequence data. In order preprocess and form a contig list -#' for downstream analysis in scRepertoire, createHTOContigList() take the filtered contig +#' This function reprocess and forms a list of contigs for downstream analysis +#' in scRepertoire, \code{\link{createHTOContigList} take the filtered contig #' annotation output and the single-cell RNA object to create the list. If using an #' integrated single-cell object, it is recommended to split the object by sequencing run -#' and remove extra prefixes and suffixes on the barcode before using createHTOContigList(). -#' Alternatively, the variable multi.run can be used to separate a list of contigs by a meta data -#' variable. This may have issues with the repeated barcodes. +#' and remove extra prefixes and suffixes on the barcode before using +#' \code{\link{createHTOContigList} . Alternatively, the variable **multi.run** can +#' be used to separate a list of contigs by a meta data variable. This may have +#' issues with the repeated barcodes. #' #' @examples #' \dontrun{ #' filtered.contig <- read.csv(".../Sample/outs/filtered_contig_annotations.csv") +#' #' contig.list <- createHTOContigList(contig = filtered.contig, -#' sc = Seurat.Obj, group.by = "HTO_maxID") +#' sc = Seurat.Obj, +#' group.by = "HTO_maxID") #' } #' #' @param contig The filtered contig annotation file from multiplexed experiment @@ -24,8 +25,7 @@ #' @param multi.run If using integrated single-cell object, the meta data variable #' that indicates the sequencing run. #' @export -#' @return Returns a list of contigs corresponding to the multiplexed Seurat -#' or Single-Cell Experiment object +#' @return Returns a list of contigs corresponding to the multiplexed single-cell object createHTOContigList <- function(contig, diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index 2a634b0d..586eec15 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -2,8 +2,7 @@ % Please edit documentation in R/clonalSizeDistribution.R \name{clonalSizeDistribution} \alias{clonalSizeDistribution} -\title{Hierarchical clustering of clonotypes on clonotype size and -Jensen-Shannon divergence} +\title{Hierarchical clustering of clones using Gamma-GPD spliced threshold model} \usage{ clonalSizeDistribution( df, @@ -43,10 +42,36 @@ ggplot dendrogram of the clone size distribution } \description{ This function produces a hierarchical clustering of clonotypes by sample -using the Jensen-Shannon distance and discrete gamma-GPD spliced threshold +using discrete gamma-GPD spliced threshold model. If using this model please read and cite \href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. } +\details{ +#' @details +The probability density function (pdf) for the Generalized Pareto Distribution (GPD) is given by: +\deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ +f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) +} +where: +\itemize{ + \item{\(\mu\) is a location parameter,} + \item{\(\sigma > 0\) is a scale parameter,} + \item{\(\xi\) is a shape parameter, and} + \item{\(x \ge \mu\) if \(\xi \ge 0\) and \(\mu \le x \le \mu - \sigma/\xi\) if \(\xi < 0\).} +} + +The probability density function (pdf) for the Gamma Distribution is given by: +\deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ +f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) +} +where: +\itemize{ + \item{\(\alpha > 0\) is the shape parameter,} + \item{\(\beta > 0\) is the scale parameter,} + \item{\(x \ge 0\),} + \item{\(\Gamma(\alpha)\) is the gamma function of \(\alpha\).} +} +} \examples{ #Making combined contig data combined <- combineTCR(contig_list, From 5796aee40f21204ca75ef55a01b79e93d87e28b3 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 6 Oct 2023 16:15:48 -0500 Subject: [PATCH 079/142] Annotating getCirclize --- R/clonalBias.R | 2 +- R/createHTOContigList.R | 4 +-- R/getCircilize.R | 52 +++++++++++++++++++++-------------- man/clonalAbundance.Rd | 2 +- man/clonalBias.Rd | 2 +- man/clonalSizeDistribution.Rd | 3 +- man/createHTOContigList.Rd | 20 +++++++------- man/getCirclize.Rd | 16 +++++++---- 8 files changed, 60 insertions(+), 41 deletions(-) diff --git a/R/clonalBias.R b/R/clonalBias.R index 41017ae7..90224000 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -7,7 +7,7 @@ #' bias of *0* - matches the background subtype distribution. Please read and cite #' the following #' \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} -#' if using \code{\link{clonalBias()}} +#' if using \code{\link{clonalBias}} #' #' @examples #' #Making combined contig data diff --git a/R/createHTOContigList.R b/R/createHTOContigList.R index 58c19fb6..2fe2af4c 100644 --- a/R/createHTOContigList.R +++ b/R/createHTOContigList.R @@ -1,11 +1,11 @@ #' Generate a contig list from a multiplexed experiment #' #' This function reprocess and forms a list of contigs for downstream analysis -#' in scRepertoire, \code{\link{createHTOContigList} take the filtered contig +#' in scRepertoire, \code{\link{createHTOContigList}} take the filtered contig #' annotation output and the single-cell RNA object to create the list. If using an #' integrated single-cell object, it is recommended to split the object by sequencing run #' and remove extra prefixes and suffixes on the barcode before using -#' \code{\link{createHTOContigList} . Alternatively, the variable **multi.run** can +#' \code{\link{createHTOContigList}}. Alternatively, the variable **multi.run** can #' be used to separate a list of contigs by a meta data variable. This may have #' issues with the repeated barcodes. #' diff --git a/R/getCircilize.R b/R/getCircilize.R index db1059bd..fa1bacc7 100644 --- a/R/getCircilize.R +++ b/R/getCircilize.R @@ -2,9 +2,14 @@ #' clonotypes as a chord diagram. #' #' This function will take the meta data from the product of -#' combineExpression()and generate a relational data frame to +#' \code{\link{combineExpression}} and generate a relational data frame to #' be used for a chord diagram. Each cord will represent the number of -#' clonotype unqiue and shared across the multiple group.by variable. +#' clonotype unique and shared across the multiple **group.by** variable. +#' If using the downstream circlize R package, please read and cite the +#' following \href{https://pubmed.ncbi.nlm.nih.gov/24930139/}{manuscript}. +#' If looking for more advance ways for circular visualizations, there +#' is a great \href{https://jokergoo.github.io/circlize_book/book/}{cookbook} +#' for the circlize package. #' #' @examples #' #Getting the combined contigs @@ -16,8 +21,9 @@ #' scRep_example <- get(data("scRep_example")) #' scRep_example <- combineExpression(combined, scRep_example) #' -#' #Getting data frame output for Circilize -#' circles <- getCirclize(scRep_example, group.by = "seurat_clusters") +#' #Getting data frame output for Circlize +#' circles <- getCirclize(scRep_example, +#' group.by = "seurat_clusters") #' #' #' @param sc The single-cell object after \code{\link{combineExpression}}. @@ -26,7 +32,7 @@ #' VDJC gene + CDR3 nucleotide (strict). #' @param group.by The group header for which you would like to analyze #' the data. -#' @param proportion Binary will calculate relationship unique +#' @param proportion Boolean will calculate relationship unique #' clonotypes (proportion = FALSE) or a ratio of the group.by #' variable (proportion = TRUE) #' @@ -34,23 +40,29 @@ #' @export #' @return data frame of shared clonotypes between groups #' @author Dillon Corvino, Nick Borcherding -getCirclize <- function(sc, cloneCall = "strict", - group.by = NULL, proportion = FALSE) { +getCirclize <- function(sc, + cloneCall = "strict", + group.by = NULL, + proportion = FALSE) { meta <- .grabMeta(sc) cloneCall <- .theCall(cloneCall) - test <- meta[, c(cloneCall, group.by)] - test <- test[!is.na(test[,cloneCall]),] - dTest <- suppressMessages(dcast(test, test[,cloneCall] ~ test[,group.by])) - dTest <- dTest[apply(dTest[,-1], 1, function(x) !all(x==0)),] - dTest2 <- dTest[-1] - dTest2[dTest2 >= 1] <- 1 - total <- nrow(dTest) + + #Quantifying clonotypes across group.by variable + dat <- meta[, c(cloneCall, group.by)] + dat <- dat[!is.na(dat[,cloneCall]),] + mat <- suppressMessages(dcast(dat, dat[,cloneCall] ~ dat[,group.by])) + mat <- mat[apply(mat[,-1], 1, function(x) !all(x==0)),] + mat2<- mat[-1] + mat2[mat2 >= 1] <- 1 + total <- nrow(mat) list <- list() - for (i in seq_len(nrow(dTest2))) { - list[[i]] <- which(dTest2[i,] > 0) + for (i in seq_len(nrow(mat2))) { + list[[i]] <- which(mat2[i,] > 0) } - matrix_out <- matrix(ncol = ncol(dTest2), nrow = ncol(dTest2), 0) + + #Making a pairwise matrix that sums the shared clonotypes + matrix_out <- matrix(ncol = ncol(mat2), nrow = ncol(mat2), 0) for (j in seq_along(list)) { matrix_out[list[[j]],list[[j]]] <- matrix_out[list[[j]],list[[j]]] + 1 if (length(list[[j]]) > 1) { @@ -58,12 +70,12 @@ getCirclize <- function(sc, cloneCall = "strict", diag(matrix_out[list[[j]],list[[j]]]) <- diag(matrix_out[list[[j]],list[[j]]]) - 1 } } - matrix_out[lower.tri(matrix_out)] <- NA - colnames(matrix_out) <- colnames(dTest2) - rownames(matrix_out) <- colnames(dTest2) + colnames(matrix_out) <- colnames(mat2) + rownames(matrix_out) <- colnames(mat2) + #Converting matrix to data frame to work with criclize output <- data.frame(from = rep(rownames(matrix_out), times = ncol(matrix_out)), to = rep(colnames(matrix_out), each = nrow(matrix_out)), diff --git a/man/clonalAbundance.Rd b/man/clonalAbundance.Rd index 79cf8b6a..905558b8 100644 --- a/man/clonalAbundance.Rd +++ b/man/clonalAbundance.Rd @@ -1,5 +1,5 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/abundanceContig.R +% Please edit documentation in R/clonalAbundance.R \name{clonalAbundance} \alias{clonalAbundance} \title{Demonstrate the relative abundance of clonotypes by group or sample.} diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index e0f380c3..6fd0b6f4 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -44,7 +44,7 @@ is composed of cells from a single compartment or cluster, while a clonotype bias of *0* - matches the background subtype distribution. Please read and cite the following \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} -if using \code{\link{clonalBias()}} +if using \code{\link{clonalBias}} } \examples{ #Making combined contig data diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index 586eec15..a354c7de 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -47,7 +47,6 @@ model. If using this model please read and cite \href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. } \details{ -#' @details The probability density function (pdf) for the Generalized Pareto Distribution (GPD) is given by: \deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) @@ -60,6 +59,8 @@ where: \item{\(x \ge \mu\) if \(\xi \ge 0\) and \(\mu \le x \le \mu - \sigma/\xi\) if \(\xi < 0\).} } + + The probability density function (pdf) for the Gamma Distribution is given by: \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) diff --git a/man/createHTOContigList.Rd b/man/createHTOContigList.Rd index 9648a29c..2f3d3a8c 100644 --- a/man/createHTOContigList.Rd +++ b/man/createHTOContigList.Rd @@ -18,25 +18,25 @@ If more than one header listed, the function combines them into a single variabl that indicates the sequencing run.} } \value{ -Returns a list of contigs corresponding to the multiplexed Seurat -or Single-Cell Experiment object +Returns a list of contigs corresponding to the multiplexed single-cell object } \description{ -Multiplexing single-cell sequencing runs is an efficient method for quantifying -multiple samples or conditions simultaneously. Unfortunately, the hashing information -is not stored in the TCR sequence data. In order preprocess and form a contig list -for downstream analysis in scRepertoire, createHTOContigList() take the filtered contig +This function reprocess and forms a list of contigs for downstream analysis +in scRepertoire, \code{\link{createHTOContigList}} take the filtered contig annotation output and the single-cell RNA object to create the list. If using an integrated single-cell object, it is recommended to split the object by sequencing run -and remove extra prefixes and suffixes on the barcode before using createHTOContigList(). -Alternatively, the variable multi.run can be used to separate a list of contigs by a meta data -variable. This may have issues with the repeated barcodes. +and remove extra prefixes and suffixes on the barcode before using +\code{\link{createHTOContigList}}. Alternatively, the variable **multi.run** can +be used to separate a list of contigs by a meta data variable. This may have +issues with the repeated barcodes. } \examples{ \dontrun{ filtered.contig <- read.csv(".../Sample/outs/filtered_contig_annotations.csv") + contig.list <- createHTOContigList(contig = filtered.contig, -sc = Seurat.Obj, group.by = "HTO_maxID") + sc = Seurat.Obj, + group.by = "HTO_maxID") } } diff --git a/man/getCirclize.Rd b/man/getCirclize.Rd index afe955cc..86d5baed 100644 --- a/man/getCirclize.Rd +++ b/man/getCirclize.Rd @@ -17,7 +17,7 @@ VDJC gene + CDR3 nucleotide (strict).} \item{group.by}{The group header for which you would like to analyze the data.} -\item{proportion}{Binary will calculate relationship unique +\item{proportion}{Boolean will calculate relationship unique clonotypes (proportion = FALSE) or a ratio of the group.by variable (proportion = TRUE)} } @@ -26,9 +26,14 @@ data frame of shared clonotypes between groups } \description{ This function will take the meta data from the product of -combineExpression()and generate a relational data frame to +\code{\link{combineExpression}} and generate a relational data frame to be used for a chord diagram. Each cord will represent the number of -clonotype unqiue and shared across the multiple group.by variable. +clonotype unique and shared across the multiple **group.by** variable. +If using the downstream circlize R package, please read and cite the +following \href{https://pubmed.ncbi.nlm.nih.gov/24930139/}{manuscript}. +If looking for more advance ways for circular visualizations, there +is a great \href{https://jokergoo.github.io/circlize_book/book/}{cookbook} +for the circlize package. } \examples{ #Getting the combined contigs @@ -40,8 +45,9 @@ combined <- combineTCR(contig_list, scRep_example <- get(data("scRep_example")) scRep_example <- combineExpression(combined, scRep_example) -#Getting data frame output for Circilize -circles <- getCirclize(scRep_example, group.by = "seurat_clusters") +#Getting data frame output for Circlize +circles <- getCirclize(scRep_example, + group.by = "seurat_clusters") } From 9f66b912a97e06fdf000b3f7a5ed559a418916e8 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 8 Oct 2023 11:55:31 -0500 Subject: [PATCH 080/142] Fix latex formating --- DESCRIPTION | 22 +++++++++++----------- R/clonalDiversity.R | 8 ++++---- R/clonalOverlap.R | 2 +- R/clonalSizeDistribution.R | 27 +++++++++++++-------------- man/clonalDiversity.Rd | 8 ++++---- man/clonalOverlap.Rd | 2 +- man/clonalSizeDistribution.Rd | 24 ++++++++++++------------ 7 files changed, 46 insertions(+), 47 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 579cab29..d83550a5 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -16,17 +16,16 @@ Depends: Imports: cubature, dplyr, + evmix, ggalluvial, - plyr, - methods, - igraph, + ggdendro, ggraph, - stats, - Rcpp, - evmix, - ggdendro, - iNEXT, grDevices, + igraph, + iNEXT, + methods, + plyr, + Rcpp, reshape2, rjson, rlang, @@ -34,6 +33,7 @@ Imports: seqinr, SeuratObject, SingleCellExperiment, + stats, stringr, stringdist, SummarizedExperiment, @@ -42,13 +42,13 @@ Imports: utils, VGAM Suggests: - knitr, - rmarkdown, + BiocManager, BiocStyle, circlize, + knitr, + rmarkdown, scales, scater, - BiocManager, Seurat, spelling, testthat (>= 3.0.0), diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 33b3411e..0cab22f6 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -30,10 +30,10 @@ #' #' Where: #' \itemize{ -#' \item{\(p_i\) is the proportion of species \(i\) in the dataset.} -#' \item{\(S\) is the total number of species.} -#' \item{\(n_1\) and \(n_2\) are the number of singletons and doubletons, respectively.} -#' \item{\(S_{abundant}\), \(S_{rare}\), \(C_{rare}\), and \(F_1\) are parameters derived from the data.} +#' \item{\eqn{p_i} is the proportion of species \eqn{i} in the dataset.} +#' \item{\eqn{S} is the total number of species.} +#' \item{\eqn{n_1} and \eqn{n_2} are the number of singletons and doubletons, respectively.} +#' \item{\eqn{S_{abundant}}, \eqn{S_{rare}}, \eqn{C_{rare}}, and \eqn{F_1} are parameters derived from the data.} #' } #' #' @examples diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index eeef1e5f..8875b9f9 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -23,7 +23,7 @@ #' #' Where: #' \itemize{ -#' \item{\(a_i\) and \(b_i\) are the abundances of species \(i\) in groups A and B, respectively.} +#' \item{\eqn{a_i} and \eqn{b_i} are the abundances of species \eqn{i} in groups A and B, respectively.} #' } #' #' @examples diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 62e3b736..05c99d33 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -10,26 +10,26 @@ #' \deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ #' f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) #' } -#' where: +#' +#' Where: #' \itemize{ -#' \item{\(\mu\) is a location parameter,} -#' \item{\(\sigma > 0\) is a scale parameter,} -#' \item{\(\xi\) is a shape parameter, and} -#' \item{\(x \ge \mu\) if \(\xi \ge 0\) and \(\mu \le x \le \mu - \sigma/\xi\) if \(\xi < 0\).} +#' \item{\eqn{\mu} is a location parameter} +#' \item{\eqn{\sigma > 0} is a scale parameter} +#' \item{\eqn{\xi} is a shape parameter} +#' \item{\eqn{x \ge \mu} if \eqn{\xi \ge 0} and \eqn{\mu \le x \le \mu - \sigma/\xi} if \eqn{\xi < 0}} #' } #' -#' -#' #' The probability density function (pdf) for the Gamma Distribution is given by: #' \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ #' f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) #' } -#' where: +#' +#' Where: #' \itemize{ -#' \item{\(\alpha > 0\) is the shape parameter,} -#' \item{\(\beta > 0\) is the scale parameter,} -#' \item{\(x \ge 0\),} -#' \item{\(\Gamma(\alpha)\) is the gamma function of \(\alpha\).} +#' \item{\eqn{\alpha > 0} is the shape parameter} +#' \item{\eqn{\beta > 0} is the scale parameter} +#' \item{\eqn{x \ge 0}} +#' \item{\eqn{\Gamma(\alpha)} is the gamma function of \eqn{\alpha}} #' } #' #' @examples @@ -55,8 +55,7 @@ #' @import ggplot2 #' @importFrom dplyr bind_rows #' @importFrom ggdendro dendro_data segment label -#' @importFrom stats hclust optim pgamma -#' @importFrom stats as.dist +#' @importFrom stats hclust optim pgamma as.dist #' @export #' @return ggplot dendrogram of the clone size distribution #' @author Hillary Koch diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index f4b52ad7..6087c82c 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -82,10 +82,10 @@ Abundance-based Coverage Estimator (ACE): Where: \itemize{ - \item{\(p_i\) is the proportion of species \(i\) in the dataset.} - \item{\(S\) is the total number of species.} - \item{\(n_1\) and \(n_2\) are the number of singletons and doubletons, respectively.} - \item{\(S_{abundant}\), \(S_{rare}\), \(C_{rare}\), and \(F_1\) are parameters derived from the data.} + \item{\eqn{p_i} is the proportion of species \eqn{i} in the dataset.} + \item{\eqn{S} is the total number of species.} + \item{\eqn{n_1} and \eqn{n_2} are the number of singletons and doubletons, respectively.} + \item{\eqn{S_{abundant}}, \eqn{S_{rare}}, \eqn{C_{rare}}, and \eqn{F_1} are parameters derived from the data.} } } \examples{ diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index f160b300..28272987 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -61,7 +61,7 @@ Cosine Similarity: Where: \itemize{ - \item{\(a_i\) and \(b_i\) are the abundances of species \(i\) in groups A and B, respectively.} + \item{\eqn{a_i} and \eqn{b_i} are the abundances of species \eqn{i} in groups A and B, respectively.} } } \examples{ diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index a354c7de..e0d7bec9 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -51,26 +51,26 @@ The probability density function (pdf) for the Generalized Pareto Distribution ( \deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) } -where: + +Where: \itemize{ - \item{\(\mu\) is a location parameter,} - \item{\(\sigma > 0\) is a scale parameter,} - \item{\(\xi\) is a shape parameter, and} - \item{\(x \ge \mu\) if \(\xi \ge 0\) and \(\mu \le x \le \mu - \sigma/\xi\) if \(\xi < 0\).} + \item{\eqn{\mu} is a location parameter} + \item{\eqn{\sigma > 0} is a scale parameter} + \item{\eqn{\xi} is a shape parameter} + \item{\eqn{x \ge \mu} if \eqn{\xi \ge 0} and \eqn{\mu \le x \le \mu - \sigma/\xi} if \eqn{\xi < 0}} } - - The probability density function (pdf) for the Gamma Distribution is given by: \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) } -where: + +Where: \itemize{ - \item{\(\alpha > 0\) is the shape parameter,} - \item{\(\beta > 0\) is the scale parameter,} - \item{\(x \ge 0\),} - \item{\(\Gamma(\alpha)\) is the gamma function of \(\alpha\).} + \item{\eqn{\alpha > 0} is the shape parameter} + \item{\eqn{\beta > 0} is the scale parameter} + \item{\eqn{x \ge 0}} + \item{\eqn{\Gamma(\alpha)} is the gamma function of \eqn{\alpha}} } } \examples{ From 6f4b35b3264beb1fe920945917f8d8ff6b14750d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 8 Oct 2023 14:28:38 -0500 Subject: [PATCH 081/142] clonalBias scrub --- R/clonalBias.R | 48 ++++++++++++++++++++++++++++++++++------------- man/clonalBias.Rd | 21 +++++++++++---------- 2 files changed, 46 insertions(+), 23 deletions(-) diff --git a/R/clonalBias.R b/R/clonalBias.R index 90224000..25d47f21 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -1,13 +1,12 @@ #' Examine clonotype bias #' -#' The metric seeks to -#' quantify how individual clones are skewed towards a specific cellular -#' compartment or cluster. A clonotype bias of *1* - indicates that a clonotype -#' is composed of cells from a single compartment or cluster, while a clonotype -#' bias of *0* - matches the background subtype distribution. Please read and cite -#' the following +#' The metric seeks to quantify how individual clones are skewed towards +#' a specific cellular compartment or cluster. A clonotype bias of *1* - +#' indicates that a clonotype is composed of cells from a single +#' compartment or cluster, while a clonotype bias of *0* - matches the +#' background subtype distribution. Please read and cite the following #' \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} -#' if using \code{\link{clonalBias}} +#' if using \code{\link{clonalBias}}. #' #' @examples #' #Making combined contig data @@ -31,8 +30,7 @@ #' min.expand = 2) #' #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -#' \code{\link{combineExpression}}. +#' @param df The product of \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -42,6 +40,7 @@ #' @param n.boots number of bootstraps to downsample. #' @param min.expand clonotype frequency cut off for the purpose of comparison. #' @param exportTable Returns the data frame used for forming the graph. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @importFrom stringr str_sort #' @export @@ -52,7 +51,9 @@ clonalBias <- function(df, group.by=NULL, n.boots = 20, min.expand=10, - exportTable = FALSE) { + exportTable = FALSE, + palette = "inferno") { + .checkSingleObject(sc) cloneCall <- .theCall(cloneCall) #Calculating bias bias <- .get_clono_bias(df, @@ -89,10 +90,30 @@ clonalBias <- function(df, z.score <- (bias[i,]$bias - row.pull$mean)/row.pull$std bias$Z.score[i] <- z.score } + + #Attaching the cloneSize of original combineExpression() + meta <- .grabMeta(df) + meta <- meta[meta[,cloneCall] %in% bias[,"Clone"],] + meta <- unique(meta[,c(cloneCall, split.by, "cloneSize")]) + + bias$cloneSize <- NA + for(i in seq_len(nrow(meta))) { + split <- bias[,1][i] + clone <- bias[,3][i] + bias$cloneSize[i] <- as.vector(meta[which(meta[,cloneCall] == clone & meta[,split.by] == split),"cloneSize"]) + } + + bias$cloneSize <- factor(bias$cloneSize , rev(levels(meta[,"cloneSize"]))) #Plotting plot <- ggplot(bias, aes(x=ncells,y=bias)) + - geom_point(aes(colour=Top_state)) + - .quiet(stat_quantile(data=df_shuffle, quantiles = c(corrected_p), method = "rqss", lambda=3)) + + geom_point(aes(fill=Top_state, size = cloneSize), shape = 21, stroke = 0.25) + + .quiet(stat_quantile(data=df_shuffle, + quantiles = c(corrected_p), + method = "rqss", + lambda=3, + color = "black", + lty = 2)) + + scale_fill_manual(values = .colorizer(palette, length(unique(bias[,"Top_state"])))) + theme_classic() + xlab("Clone Size") + ylab("Clonotype Bias") @@ -102,6 +123,7 @@ clonalBias <- function(df, return(plot) } +#Background summary of clones .get_clono_bg <- function(df, split.by=split.by, group.by=group.by, @@ -125,7 +147,7 @@ clonalBias <- function(df, return(bg) } -#Code Derived from +#Clone Bias .get_clono_bias <- function(df, split.by=NULL, group.by=NULL, diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index 6fd0b6f4..4a943193 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -11,12 +11,12 @@ clonalBias( group.by = NULL, n.boots = 20, min.expand = 10, - exportTable = FALSE + exportTable = FALSE, + palette = "inferno" ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -\code{\link{combineExpression}}.} +\item{df}{The product of \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -32,19 +32,20 @@ For example, "Type" for lung vs peripheral blood comparison} \item{min.expand}{clonotype frequency cut off for the purpose of comparison.} \item{exportTable}{Returns the data frame used for forming the graph.} + +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ Returns ggplot of the clonotype bias } \description{ -The metric seeks to -quantify how individual clones are skewed towards a specific cellular -compartment or cluster. A clonotype bias of *1* - indicates that a clonotype -is composed of cells from a single compartment or cluster, while a clonotype -bias of *0* - matches the background subtype distribution. Please read and cite -the following +The metric seeks to quantify how individual clones are skewed towards +a specific cellular compartment or cluster. A clonotype bias of *1* - +indicates that a clonotype is composed of cells from a single +compartment or cluster, while a clonotype bias of *0* - matches the +background subtype distribution. Please read and cite the following \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} -if using \code{\link{clonalBias}} +if using \code{\link{clonalBias}}. } \examples{ #Making combined contig data From 7e4f2361c9798eadc7dc110512991ea9a36c2c62 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 9 Oct 2023 10:10:33 -0500 Subject: [PATCH 082/142] scrub clonalNetwork --- R/clonalNetwork.R | 173 +++++++++++++++++++++-------------------- man/clonalNetwork.Rd | 17 ++-- vignettes/vignette.Rmd | 6 +- 3 files changed, 101 insertions(+), 95 deletions(-) diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index 2d357581..d1e7c821 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -20,13 +20,14 @@ #' scRep_example <- combineExpression(combined, scRep_example) #' #' #Using clonalNetwork() -#' clonalNetwork(scRep_example, reduction = "umap", -#' identity = "cluster") +#' clonalNetwork(scRep_example, +#' reduction = "umap", +#' group.by = "seurat_clusters") #' } #' #' @param sc The single-cell object after \code{\link{combineExpression}}. #' @param reduction The name of the dimensional reduction of the single-cell object. -#' @param identity A variable in the meta data to use for the nodes. +#' @param group.by The variable to use for the nodes. #' @param filter.clones Use to select the top n clones (filter.clones = 2000) or #' n of clones based on the minimum number of all the comparators (filter.clone = "min"). #' @param filter.identity Display the network for a specific level of the indicated identity. @@ -40,10 +41,11 @@ #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param exportTable Exports a table of the data into the global #' environment in addition to the visualization. -#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @param exportClones Exports a table of clones that are shared #' across multiple identity groups and ordered by the total number #' of clone copies. +#' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. + #' @import ggplot2 #' @importFrom stringr str_sort #' @importFrom igraph graph_from_data_frame V `V<-` @@ -56,7 +58,7 @@ #' clonalNetwork <- function(sc, reduction = "umap", - identity = "ident", + group.by = "ident", filter.clones = NULL, filter.identity = NULL, filter.proportion = NULL, @@ -64,55 +66,56 @@ clonalNetwork <- function(sc, cloneCall = "strict", chain = "both", exportTable = FALSE, - palette = "inferno", - exportClones = FALSE) { + exportClones = FALSE, + palette = "inferno") { to <- from <- weight <- y <- NULL cloneCall <- .theCall(cloneCall) meta <- .grabMeta(sc) - coord <- data.frame(.get.coord(sc, reduction), identity = meta[,identity]) + coord <- data.frame(.get.coord(sc, reduction), group.by = meta[,group.by]) min <- c() + meta <- .grabMeta(sc) if (!is.null(filter.clones)) { if(filter.clones == "min") { meta <- .grabMeta(sc) - id.meta <- split(meta, meta[,identity]) + id.meta <- split(meta, meta[,group.by]) for (x in seq_along(id.meta)) { min.tmp <- length(which(!is.na(unique(id.meta[[x]][,cloneCall])))) min <- c(min.tmp, min) } - min <- min(min) - table <- meta %>% - group_by(meta[,identity], meta[, cloneCall]) %>% - dplyr::count() %>% - na.omit() %>% - arrange(desc(n)) - runSum <-as.vector(cumsum(table[,3])) - table$cumSum <- as.vector(runSum$n) - cut <- which.min(unlist(abs(table[,4] - min))) - clones.to.filter <- table[seq_len(cut),1] - } else if (is.numeric(filter.clones)) { + #Filtering clones based on the minimum value + min_val <- min(min) table <- meta %>% - dplyr::count(meta[, cloneCall]) %>% + group_by(across(all_of(c(group.by, cloneCall)))) %>% + count() %>% na.omit() %>% - arrange(desc(n)) - runSum <-as.vector(cumsum(table[,2])) - table$cumSum <- as.vector(runSum) - cut <- which.min(unlist(abs(table[,3] - filter.clones))) - clones.to.filter <- table[seq_len(cut),1] - } - } - meta <- .grabMeta(sc) - if (!is.null(filter.clones)) { + arrange(desc(n)) %>% + mutate(cumSum = cumsum(n)) + cut <- which.min(abs(table$cumSum - min_val)) + clones.to.filter <- table$group.by[seq_len(cut)] + } else if (is.numeric(filter.clones)) { + #Filtering based on a numeric value + table <- meta %>% + dplyr::count(meta[, cloneCall]) %>% + na.omit() %>% + arrange(desc(n)) %>% + mutate(cumSum = cumsum(n)) + cut <- which.min(abs(table$cumSum - filter.clones)) + clones.to.filter <- table[seq_len(cut),1] + } meta <- meta[meta[,cloneCall] %in% clones.to.filter,] - } + } clones.duplicated <- na.omit(unique(meta[which(duplicated(meta[,cloneCall])),cloneCall])) + if(exportClones) { + #Summarizing all the clones by group.by table <- meta %>% - group_by(meta[,identity], meta[, cloneCall]) %>% + group_by(meta[,group.by], meta[, cloneCall]) %>% dplyr::count() %>% na.omit() %>% arrange(desc(n)) - - clones.across.identities <- names(table(table[,2])[which(table(table[,2]) > 1)]) + #Identifying the clones across the group by + clones.across.identities <- names(which(table(table[[2]]) > 1)) + #Getting the clones to output subset.table <- as.data.frame(table) subset.table <- subset.table[subset.table[,2] %in% clones.across.identities,] colnames(subset.table) <- c("id", "clone", "n") @@ -122,42 +125,41 @@ clonalNetwork <- function(sc, arrange(desc(sum)) return(dupl.clones) } - id <- as.vector(meta[,identity]) + + id <- as.vector(meta[,group.by]) id.names <- id names(id.names) <- row.names(meta) unique.id <- unique(id) + #Placing the Position of the nodes id.positions <- data.frame(coord) colnames(id.positions)[1:2] <- c("x", "y") - suppressMessages(centers <- id.positions%>% - group_by(identity) %>% - select(x, y) %>% - summarize_all(mean) %>% - data.frame()) - rownames(centers) <- centers[,1] - centers <- centers[,-1] - - clone.number <- meta[,c(cloneCall, identity)] %>% - group_by(meta[,identity]) %>% - na.omit() %>% - unique() %>% - summarise(n = n()) - names <- clone.number[,1] - clone.number <- unlist(clone.number[,2]) - names(clone.number) <- unlist(names[,1]) + centers <- id.positions %>% + group_by(group.by) %>% + summarise(across(c(x, y), mean, .names = "{col}")) %>% + column_to_rownames("group.by") + + #Unique clones per group.by + clone.number <- meta %>% + select(all_of(c(cloneCall, group.by))) %>% + group_by(meta[,group.by]) %>% + na.omit() %>% + unique() %>% + summarise(n = n()) %>% + {setNames(.$n, .$`meta[, group.by]`)} - total.number <- meta[,c(cloneCall, identity)] %>% - group_by(meta[,identity]) %>% - na.omit() %>% - summarise(n = n()) - names <- total.number[,1] - total.number <- unlist(total.number[,2]) - names(total.number) <- unlist(names[,1]) + #Total clones per group.by + total.number <- meta %>% + select(all_of(c(cloneCall, group.by))) %>% + group_by(meta[,group.by]) %>% + na.omit() %>% + summarise(n = n()) %>% + {setNames(.$n, .$`meta[, group.by]`)} edge.list <- NULL for (i in seq_along(clones.duplicated)) { pos <- which(meta[,cloneCall] == clones.duplicated[i]) - num <- table(meta[pos,identity]) + num <- table(meta[pos,group.by]) num <- num[num > 0] if(length(num) == 1) { next() @@ -175,15 +177,18 @@ clonalNetwork <- function(sc, edge.list <- data.frame(edge.list) colnames(edge.list)[3] <-"weight" - + #Filtering based on identity if (!is.null(filter.identity)) { col1 <- which(edge.list[,1] == filter.identity) col2 <- which(edge.list[,2] == filter.identity) edge.list <- edge.list[c(col1,col2),] - } else if (filter.graph) { + } + #Remove reciprocals + if (filter.graph) { unique.id <- str_sort(unique.id, numeric = TRUE) edge.list <- edge.list[edge.list[,1] %in% unique.id[seq_len(length(unique.id)/2)],] } + #Removing any clones below proportion threshold if(!is.null(filter.proportion)) { edge.list <-edge.list[edge.list[,3] > filter.proportion,] } @@ -195,31 +200,31 @@ clonalNetwork <- function(sc, V(graph)$size <- unname(clone.number) centers <- centers[rownames(centers) %in% names(V(graph)),] - plot1 <- ggraph(graph, layout = centers[match(names(V(graph)), rownames(centers)),]) + - geom_point(data = coord, aes(x = coord[,1], y = coord[,2], color = identity)) + - geom_edge_bend(aes(edge_color = as.numeric(weight)), alpha = 0.7, width = 1, - arrow = arrow(length = unit(4, 'mm')), - end_cap = circle(3, 'mm'), - angle_calc = "across", - check_overlap = TRUE, - strength = 0.75) + - geom_node_point(aes(size = size)) + - ylab(paste0(reduction, "_2")) + - xlab(paste0(reduction, "_1")) + - guides(color = "none") + - scale_edge_colour_gradientn(colors = rev(.colorizer(palette,13)), trans = "log10") + - labs(size = "Unique Clones", edge_color = "Relative Proportion of \nClones of Starting Node") + - theme( - panel.background = element_blank(), - panel.border = element_blank(), - panel.grid.major = element_blank(), - panel.grid.minor = element_blank(), - axis.line = element_line(colour = "black"), - legend.key=element_blank() - ) + plot <- ggraph(graph, layout = centers[match(names(V(graph)), rownames(centers)),]) + + geom_point(data = coord, aes(x = coord[,1], y = coord[,2], color = group.by)) + + geom_edge_bend(aes(edge_color = as.numeric(weight)), alpha = 0.7, width = 1, + arrow = arrow(length = unit(4, 'mm')), + end_cap = circle(3, 'mm'), + angle_calc = "across", + check_overlap = TRUE, + strength = 0.75) + + geom_node_point(aes(size = size)) + + ylab(paste0(reduction, "_2")) + + xlab(paste0(reduction, "_1")) + + guides(color = "none") + + scale_edge_colour_gradientn(colors = .colorizer(palette,13), trans = "log10") + + labs(size = "Unique Clones", edge_color = "Relative Proportion of \nClones of Starting Node") + + theme( + panel.background = element_blank(), + panel.border = element_blank(), + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + axis.line = element_line(colour = "black"), + legend.key=element_blank() + ) if (exportTable) { return(edge.list1) } - return(plot1) + return(plot) } diff --git a/man/clonalNetwork.Rd b/man/clonalNetwork.Rd index 1afba485..cef57e9f 100644 --- a/man/clonalNetwork.Rd +++ b/man/clonalNetwork.Rd @@ -7,7 +7,7 @@ clonalNetwork( sc, reduction = "umap", - identity = "ident", + group.by = "ident", filter.clones = NULL, filter.identity = NULL, filter.proportion = NULL, @@ -15,8 +15,8 @@ clonalNetwork( cloneCall = "strict", chain = "both", exportTable = FALSE, - palette = "inferno", - exportClones = FALSE + exportClones = FALSE, + palette = "inferno" ) } \arguments{ @@ -24,7 +24,7 @@ clonalNetwork( \item{reduction}{The name of the dimensional reduction of the single-cell object.} -\item{identity}{A variable in the meta data to use for the nodes.} +\item{group.by}{The variable to use for the nodes.} \item{filter.clones}{Use to select the top n clones (filter.clones = 2000) or n of clones based on the minimum number of all the comparators (filter.clone = "min").} @@ -46,11 +46,11 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{exportTable}{Exports a table of the data into the global environment in addition to the visualization.} -\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} - \item{exportClones}{Exports a table of clones that are shared across multiple identity groups and ordered by the total number of clone copies.} + +\item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ ggplot object @@ -75,8 +75,9 @@ scRep_example <- get(data("scRep_example")) scRep_example <- combineExpression(combined, scRep_example) #Using clonalNetwork() -clonalNetwork(scRep_example, reduction = "umap", - identity = "cluster") +clonalNetwork(scRep_example, + reduction = "umap", + group.by = "seurat_clusters") } } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 80edf3c1..f9639912 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -733,7 +733,7 @@ library(ggraph) #No Identity filter clonalNetwork(scRep_example, reduction = "umap", - identity = "seurat_clusters", + group.by = "seurat_clusters", filter.clones = NULL, filter.identity = NULL, cloneCall = "aa") @@ -741,8 +741,8 @@ clonalNetwork(scRep_example, #Examining Cluster 2 only clonalNetwork(scRep_example, reduction = "umap", - identity = "ident", - filter.identity = "C2", + group.by = "ident", + filter.identity = 3, cloneCall = "aa") ``` From ebb8c312d5096a0521cd949bd01df42bb1a69a9d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 10 Oct 2023 11:50:14 -0500 Subject: [PATCH 083/142] editing vizgenes --- R/utils.R | 27 ---------------------- R/vizGenes.R | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 63 insertions(+), 27 deletions(-) diff --git a/R/utils.R b/R/utils.R index cf4f2ffc..dc94d286 100644 --- a/R/utils.R +++ b/R/utils.R @@ -421,33 +421,6 @@ is_seurat_or_se_object <- function(obj) { return(min) } -#Parsing the CTgene -.select.gene <- function(df, chain, gene, label) { - if (chain %in% c("TRB", "TRD", "IGH")) { - gene <- unname(c(V = 1, D = 2, J = 3, C = 4)[gene]) - } else { - gene <- unname(c(V = 1, J = 2, C = 3)[gene]) - } - if (ncol(str_split(df[,"CTgene"], "_", simplify = TRUE)) == 1) { - C1 <- str_split(df[,"CTgene"], "_", simplify = TRUE)[,1] - C1 <- str_split(C1, "[.]", simplify = TRUE)[,gene] - df$C1 <- C1 - x <- "C1" - } else { - C1 <- str_split(df[,"CTgene"], "_", simplify = TRUE)[,1] - C1 <- str_split(C1, "[.]", simplify = TRUE)[,gene] - C2 <- str_split(df[,"CTgene"], "_", simplify = TRUE)[,2] - C2 <- str_split(C2, "[.]", simplify = TRUE)[,gene] - df$C1 <- C1 - df$C2 <- C2 - if (chain %in% c("TRA", "TRG", "IGH")) { - x <- "C1"} - else if (chain %in% c("TRB", "TRD", "IGL")) { - x <- "C2"} - } - return(df) -} - # check if object is a dataframe or list of dataframes is_df_or_list_of_df <- function(x) { if (is.data.frame(x)) { diff --git a/R/vizGenes.R b/R/vizGenes.R index e449532e..333ff4b3 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -125,4 +125,67 @@ vizGenes <- function(df, } if (exportTable == TRUE) { return(df) } return(plot) +} + + +.vizGenes <- function(df, + x.axis = "TRBV", + y.axis = "TRBJ", + plot = "heatmap", + order = "gene", + scale = TRUE, + group.by = NULL, + exportTable = FALSE, + palette = "inferno") { + + sco <- is_seurat_object(df) | is_se_object(df) + df <- .list.input.return(df, split.by = group.by) + if(!is.null(group.by) & !sco) { + df <- .groupList(df, group.by) + } + + df <- .bound.input.return(df) + #Parsing x.axis if gene used + if (x.axis %!in% colnames(df)) { + if (grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", x.axis)) { + df <- .select.gene(df, x.axis, x.axis) + colnames(df)[ncol(df)] <- x.axis ######## Check if need this + } + } + + #Parsing y.axis if gene used + if (y.axis %!in% colnames(df) | is.null(y.axis)) { + if (grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", y.axis)) { + df <- .select.gene(df, y.axis, y.axis) + colnames(df)[ncol(df)] <- y.axis ######## Check if need this + } + } + + #Filtering steps + df <- subset(df, !is.na(df[,c("x.axis", "y.axis")])) #remove NA values + df <- subset(df, df[,ncol(df)] != "NA") #remove values that are character "NA" + df <- subset(df, df[,ncol(df)] != "") + + + ################## + #Need to add the summary functions + #Need to add the visualization functions + +} + +#Parsing the CTgene +.select.gene <- function(df, position, label) { + chains <- c("TRAV" = 1, "TRBV" = 1, "TRGV" = 1, "TRDV" = 1, "IGHV" = 1, "IGLV" = 1, "IGKV" = 1, + "TRAJ" = 2, "TRGJ" = 2, "IGKJ" = 2, "IKLJ" = 2, + "TRBD" = 2, "TRDD" = 2, "IGHV" = 2, + "TRBJ" = 3, "TRDJ" = 2, "IGHJ" = 3) + chain.poisiton <- chains[position] + if(substring(position,3,3) %in% c("A", "G", "K", "L")) { + chain.gene <- str_split(df[,"CTgene"], "_", simplify = TRUE)[,1] + } else { + chain.gene <- str_split(df[,"CTgene"], "_", simplify = TRUE)[,2] + } + genes <- str_split(chain.gene, "[.]", simplify = TRUE)[,chain.poisiton] + df[,label] <- genes + return(df) } \ No newline at end of file From ace544faa9e1bd6e2b9e741ab0ce2ef097f0b52d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 11 Oct 2023 08:43:50 -0500 Subject: [PATCH 084/142] Summary for vizGenes --- R/vizGenes.R | 118 ++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 108 insertions(+), 10 deletions(-) diff --git a/R/vizGenes.R b/R/vizGenes.R index 333ff4b3..70d2d814 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -70,11 +70,11 @@ vizGenes <- function(df, #df <- table(df[,ncol(df)], df[,y.axis]) if (!is.null(y.axis) && y.axis != "element.names") { - df <- df %>% + mat <- df %>% group_by(df[,ncol(df)], df[,y.axis], element.names) %>% count() } else { - df <- df %>% + mat <- df %>% group_by(df[,ncol(df)], element.names) %>% count() } @@ -154,22 +154,119 @@ vizGenes <- function(df, } #Parsing y.axis if gene used - if (y.axis %!in% colnames(df) | is.null(y.axis)) { + if (y.axis %!in% colnames(df) | !is.null(y.axis)) { if (grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", y.axis)) { df <- .select.gene(df, y.axis, y.axis) colnames(df)[ncol(df)] <- y.axis ######## Check if need this + } else { + y.axis <- "element.names" } + } else { + y.axis <- "element.names" } #Filtering steps - df <- subset(df, !is.na(df[,c("x.axis", "y.axis")])) #remove NA values - df <- subset(df, df[,ncol(df)] != "NA") #remove values that are character "NA" - df <- subset(df, df[,ncol(df)] != "") + df[df == "NA"] <- NA + df[df == ""] <- NA + if(!is.null(y.axis) & any(is.na(df[,c(y.axis)]))) { + df <- subset(df, !is.na(df[,c(y.axis)])) + } + if(!is.null(x.axis) & any(is.na(df[,c(x.axis)]))) { + df <- subset(df, !is.na(df[,c(x.axis)])) + } + + if (!scale) { + col.lab <- "Total n" + values <- "count" + } else { + + col.lab <- "Scaled Values" + values <- "proportion" + } + #Calculating the summary values + if (!is.null(y.axis) && y.axis != "element.names") { + mat <- df %>% + group_by(element.names, df[,x.axis], df[,y.axis]) %>% + summarise(count = n()) %>% + ungroup() %>% + group_by(element.names) %>% + mutate(total = sum(count), + proportion = count / total) %>% + as.data.frame() %>% + na.omit() + colnames(mat)[2:3] <- c("x.axis", "y.axis") + mat$n <- mat[,values] + mat <- mat %>% + group_by(y.axis, x.axis) %>% + mutate(varcount = sum(n), + sd = sd(n, na.rm = TRUE), + mean = mean(n)) %>% + as.data.frame() + + } else { + mat <- df %>% + group_by(element.names, df[,x.axis]) %>% + summarise(count = n()) %>% + ungroup() %>% + group_by(element.names) %>% + mutate(total = sum(count), + proportion = count / total) %>% + as.data.frame() %>% + na.omit() + colnames(mat)[1:2] <- c("y.axis", "x.axis") + mat$n <- mat[,values] + mat <- mat %>% + group_by(y.axis, x.axis) %>% + mutate(varcount = sum(n), + sd = sd(n, na.rm = TRUE), + mean = mean(n)) %>% + as.data.frame() + } + + + + + + if (order == "variance") { + varOrder <- unique(mat[order(mat$varcount, decreasing = TRUE),"x.axis"]) + mat[,"x.axis"] <- factor(mat[,"x.axis"], levels = varOrder) + } + if (plot == "bar") { + mat2 <- unique(mat[,c("Var1", "Var2", "sd", "mean")]) + plot <- ggplot(mat2, aes(x=Var2, y = mean)) + + geom_bar(stat = "identity") + + geom_errorbar(aes(ymin=mean, ymax=mean+sd), width=.2, + position=position_dodge(.9)) + + theme_classic() + + theme(axis.title.x = element_blank(), #remove titles + axis.title.y = element_blank(), + axis.ticks.x = element_blank(), #removes ticks + axis.text.x = element_text(angle = 90, + vjust = 0.5, hjust=1, size=rel(0.5))) + + facet_grid(Var1~.) + } else if (plot == "heatmap") { + + plot <- ggplot(mat, aes(x=x.axis, y = y.axis)) + + geom_tile(aes(fill = n), color = "black") + + theme_classic() + + labs(fill = col.lab) + + theme(axis.title.x = element_blank(), #remove titles + axis.ticks.x = element_blank(), #removes ticks + axis.text.x = element_text(angle = 90, + vjust = 0.5, hjust=1, size=rel(0.5)), + axis.title.y = element_blank(), + axis.text.y = element_text(size=rel(0.5))) + + scale_fill_gradientn(colors = .colorizer(palette,5)) + } + if (exportTable == TRUE) { + return(mat) + } + return(plot) - ################## - #Need to add the summary functions - #Need to add the visualization functions + #Need to test: + #Different chains - both heatmap and barplot + #grouped elements - both heatmap and barplot } @@ -186,6 +283,7 @@ vizGenes <- function(df, chain.gene <- str_split(df[,"CTgene"], "_", simplify = TRUE)[,2] } genes <- str_split(chain.gene, "[.]", simplify = TRUE)[,chain.poisiton] + df[,label] <- NA df[,label] <- genes return(df) -} \ No newline at end of file +} From 8340d15407cdb18966ed52c206e825e6286b5556 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 12 Oct 2023 09:51:27 -0500 Subject: [PATCH 085/142] redo on vizGenes --- R/vizGenes.R | 142 +++++++++-------------------------------- man/vizGenes.Rd | 25 ++++---- vignettes/vignette.Rmd | 63 ++++++++---------- 3 files changed, 69 insertions(+), 161 deletions(-) diff --git a/R/vizGenes.R b/R/vizGenes.R index 70d2d814..212ced07 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -12,20 +12,21 @@ #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' -#' vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) +#' vizGenes(combined, +#' x.axis = "TRBV", +#' y.axis = NULL, +#' plot = "heatmap") #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param gene Which part of the immune receptor to visualize - V, D, J, C -#' @param chain indicate the specific chain should be used - -#' e.g. "TRA", "TRG", "IGH", "IGL" (no both option here) -#' @param plot The type of plot to return - heatmap or bar +#' @param plot The type of plot to return - heatmap or barplot +#' @param x.axis Gene segments to separate the x-axis, such as "TRAV", "TRBD", "IGKJ". #' @param y.axis Variable to separate the y-axis, can be both categorical or other gene -#' gene segments such as V, D, J, or C. +#' gene segments, such as "TRAV", "TRBD", "IGKJ". +#' @param group.by Variable in which to group the diversity calculation. #' @param order Categorical variable to organize the x-axis, either "gene" or "variance" #' @param scale Converts the individual count of genes to proportion using the total #' respective repertoire size -#' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 @@ -36,109 +37,26 @@ #' @return ggplot bar diagram or heatmap of gene usage vizGenes <- function(df, - gene = "V", - chain = "TRA", + x.axis = "TRBV", + y.axis = NULL, + group.by = NULL, plot = "heatmap", - y.axis = "sample", order = "gene", scale = TRUE, - group.by = NULL, exportTable = FALSE, palette = "inferno") { - element.names <- NULL + sco <- is_seurat_object(df) | is_se_object(df) - df <- .list.input.return(df, split.by = group.by) - if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) - } - for(i in seq_along(df)) { - df[[i]] <- .off.the.chain(df[[i]], chain, "CTgene") - } - df <- .bound.input.return(df) - if (y.axis %!in% colnames(df) | is.null(y.axis)) { - if (y.axis %!in% c("V", "D", "J", "C")) { - y.axis <- "element.names" - } else { - df <- .select.gene(df, chain, y.axis) - colnames(df)[ncol(df)] <- y.axis - } - } - df <- .select.gene(df, chain, gene) - df <- subset(df, !is.na(df[,ncol(df)])) #remove NA values - df <- subset(df, df[,ncol(df)] != "NA") #remove values that are character "NA" - df <- subset(df, df[,ncol(df)] != "") #remove rows with non genes - #df <- table(df[,ncol(df)], df[,y.axis]) - if (!is.null(y.axis) && y.axis != "element.names") { - mat <- df %>% - group_by(df[,ncol(df)], df[,y.axis], element.names) %>% - count() - } else { - mat <- df %>% - group_by(df[,ncol(df)], element.names) %>% - count() + #Extracting group.by in case of null + if(!grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", y.axis) && !is.null(y.axis)) { + group.by <- y.axis } - df <- df %>% - group_by(element.names) %>% - mutate(sum = sum(n)) - col.lab <- "Total n" - if (scale == TRUE) { - df[,"n"] <- df[,"n"]/df[,"sum"] - col.lab <- "Scaled Values" - } - colnames(df)[1:2] <- c("Var1", "Var2") - df <- df %>% - group_by(Var1, Var2) %>% - mutate(varcount = sum(n), - sd = sd(n, na.rm = TRUE), - mean = mean(n)) - if (order == "variance") { - varOrder <- order(df$varcount, decreasing = TRUE) - df$Var1 <- factor(df$Var1, levels = unique(df$Var1[varOrder])) - } - if (plot == "bar") { - df2 <- unique(df[,c("Var1", "Var2", "sd", "mean")]) - plot <- ggplot(df2, aes(x=Var1, y = mean)) + - geom_bar(stat = "identity") + - geom_errorbar(aes(ymin=mean, ymax=mean+sd), width=.2, - position=position_dodge(.9)) + - theme_classic() + - theme(axis.title.x = element_blank(), #remove titles - axis.title.y = element_blank(), - axis.ticks.x = element_blank(), #removes ticks - axis.text.x = element_text(angle = 90, - vjust = 0.5, hjust=1, size=rel(0.5))) + - facet_grid(Var2~.) - } else if (plot == "heatmap") { - - plot <- ggplot(df, aes(x=Var1, y = Var2)) + - geom_tile(aes(fill = n), color = "black") + - theme_classic() + - labs(fill = col.lab) + - theme(axis.title.x = element_blank(), #remove titles - axis.ticks.x = element_blank(), #removes ticks - axis.text.x = element_text(angle = 90, - vjust = 0.5, hjust=1, size=rel(0.5)), - axis.title.y = element_blank(), - axis.text.y = element_text(size=rel(0.5))) + - scale_fill_gradientn(colors = rev(.colorizer(palette,5))) + #If single-cell object, need group.by + if(is.null(group.by) & sco) { + group.by <- "ident" } - if (exportTable == TRUE) { return(df) } - return(plot) -} - - -.vizGenes <- function(df, - x.axis = "TRBV", - y.axis = "TRBJ", - plot = "heatmap", - order = "gene", - scale = TRUE, - group.by = NULL, - exportTable = FALSE, - palette = "inferno") { - sco <- is_seurat_object(df) | is_se_object(df) df <- .list.input.return(df, split.by = group.by) if(!is.null(group.by) & !sco) { df <- .groupList(df, group.by) @@ -154,7 +72,7 @@ vizGenes <- function(df, } #Parsing y.axis if gene used - if (y.axis %!in% colnames(df) | !is.null(y.axis)) { + if (any(y.axis %!in% colnames(df)) | !is.null(y.axis)) { if (grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", y.axis)) { df <- .select.gene(df, y.axis, y.axis) colnames(df)[ncol(df)] <- y.axis ######## Check if need this @@ -179,7 +97,6 @@ vizGenes <- function(df, col.lab <- "Total n" values <- "count" } else { - col.lab <- "Scaled Values" values <- "proportion" } @@ -224,18 +141,17 @@ vizGenes <- function(df, as.data.frame() } - - - if (order == "variance") { varOrder <- unique(mat[order(mat$varcount, decreasing = TRUE),"x.axis"]) - mat[,"x.axis"] <- factor(mat[,"x.axis"], levels = varOrder) + } else { + varOrder <- str_sort(unique(mat$x.axis), numeric = TRUE) } - if (plot == "bar") { - mat2 <- unique(mat[,c("Var1", "Var2", "sd", "mean")]) - plot <- ggplot(mat2, aes(x=Var2, y = mean)) + - geom_bar(stat = "identity") + + mat[,"x.axis"] <- factor(mat[,"x.axis"], levels = varOrder) + if (plot == "barplot") { + mat2 <- unique(mat[,c("x.axis", "y.axis", "sd", "mean")]) + plot <- ggplot(mat2, aes(x=x.axis, y = mean)) + + geom_bar(stat = "identity", color = "black", lwd = 0.25) + geom_errorbar(aes(ymin=mean, ymax=mean+sd), width=.2, position=position_dodge(.9)) + theme_classic() + @@ -244,13 +160,13 @@ vizGenes <- function(df, axis.ticks.x = element_blank(), #removes ticks axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=rel(0.5))) + - facet_grid(Var1~.) + facet_grid(y.axis~.) } else if (plot == "heatmap") { plot <- ggplot(mat, aes(x=x.axis, y = y.axis)) + - geom_tile(aes(fill = n), color = "black") + + geom_tile(aes(fill =log(n)), color = "black", lwd = 0.25) + theme_classic() + - labs(fill = col.lab) + + labs(fill = paste0("Log(",col.lab, ")")) + theme(axis.title.x = element_blank(), #remove titles axis.ticks.x = element_blank(), #removes ticks axis.text.x = element_text(angle = 90, diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index aecc450a..075625b8 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -6,13 +6,12 @@ \usage{ vizGenes( df, - gene = "V", - chain = "TRA", + x.axis = "TRBV", + y.axis = NULL, + group.by = NULL, plot = "heatmap", - y.axis = "sample", order = "gene", scale = TRUE, - group.by = NULL, exportTable = FALSE, palette = "inferno" ) @@ -21,23 +20,20 @@ vizGenes( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{gene}{Which part of the immune receptor to visualize - V, D, J, C} +\item{x.axis}{Gene segments to separate the x-axis, such as "TRAV", "TRBD", "IGKJ".} -\item{chain}{indicate the specific chain should be used - -e.g. "TRA", "TRG", "IGH", "IGL" (no both option here)} +\item{y.axis}{Variable to separate the y-axis, can be both categorical or other gene +gene segments, such as "TRAV", "TRBD", "IGKJ".} -\item{plot}{The type of plot to return - heatmap or bar} +\item{group.by}{Variable in which to group the diversity calculation.} -\item{y.axis}{Variable to separate the y-axis, can be both categorical or other gene -gene segments such as V, D, J, or C.} +\item{plot}{The type of plot to return - heatmap or barplot} \item{order}{Categorical variable to organize the x-axis, either "gene" or "variance"} \item{scale}{Converts the individual count of genes to proportion using the total respective repertoire size} -\item{group.by}{The variable to use for grouping.} - \item{exportTable}{Returns the data frame used for forming the graph.} \item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} @@ -58,6 +54,9 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -vizGenes(combined, gene = "V", chain = "TRB", plot = "bar", scale = TRUE) +vizGenes(combined, + x.axis = "TRBV", + y.axis = NULL, + plot = "heatmap") } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index f9639912..ac4e772a 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -318,29 +318,17 @@ clonalCompare(combined, ## vizGenes -A visualization of the relative usage of genes of the TCR or BCR, using `vizGenes()`. - -**gene** -+ "V" -+ "D" -+ "J" -+ "C" - -**chain** -+ "TRB" -+ "TRA" -+ "TRG" -+ "TRD" -+ "IGH" -+ "IGL" +A visualization of the relative usage of genes of the TCR or BCR, using `vizGenes()`. There is some functional crossover between `vizGenes()` and two functions below called `percentGenes()` and `percentVJ()`. But `vizGenes()` is more adaptable to allow for comparisons across chains, scaling, etc. -**plot** -+ "bar" for a bar chart -+ "heatmap" for a heatmap +**x.axis** ++ Gene segment to separate the x-axis, such as "TRAV", "TRBD", "IGKJ" **y.axis** -Variable to separate the counts along the y-axis. If not specified, -the default is to separate by the list element names. ++ Gene segment or categorical variable to visualize along the y-axis + +**plot** ++ "barplot" for a bar chart ++ "heatmap" for a heatmap **scale** + TRUE to scale the graph by number of genes per sample @@ -352,32 +340,37 @@ the default is to separate by the list element names. ```{r} vizGenes(combined, - gene = "V", - chain = "TRB", - plot = "bar", + x.axis = "TRBV", + y.axis = NULL, + plot = "barplot", scale = TRUE) ``` -We can also use `vizGenes()` to look at the usage of genes in a single chain. For example, say we are interested in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: +`vizGenes()` can also be used to look at the usage of genes in a single chain. For example, say we are interested in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: ```{r} #Peripheral Blood vizGenes(combined[c(1,3,5,7)], - gene = "V", - chain = "TRB", - y.axis = "J", - plot = "heatmap", - scale = TRUE, - order = "variance") + x.axis = "TRBV", + y.axis = "TRBJ", + plot = "heatmap", + scale = TRUE) #Lung vizGenes(combined[c(2,4,6,8)], - gene = "V", - chain = "TRB", - y.axis = "J", + x.axis = "TRBV", + y.axis = "TRBJ", + plot = "heatmap", + scale = TRUE) +``` +For the P17 patient samples, what if we are interested in chain pairings, we can look at TRBV and TRAV at the same time using them as inputs to **x.axis** and **y.axis**. + +```{r} +vizGenes(combined[c(1,2)], + x.axis = "TRBV", + y.axis = "TRAV", plot = "heatmap", - scale = TRUE, - order = "variance") + scale = FALSE) ``` ## percentAA From 3f7a8e98c78987b223973795109e801abad35433 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 12 Oct 2023 13:05:54 -0500 Subject: [PATCH 086/142] debug clonalNetwork --- R/clonalNetwork.R | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index d1e7c821..0203f534 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -137,8 +137,11 @@ clonalNetwork <- function(sc, centers <- id.positions %>% group_by(group.by) %>% summarise(across(c(x, y), mean, .names = "{col}")) %>% - column_to_rownames("group.by") - + as.data.frame() + + row.names(centers) <- centers$group.by + centers$group.by <- NULL + #Unique clones per group.by clone.number <- meta %>% select(all_of(c(cloneCall, group.by))) %>% @@ -202,7 +205,9 @@ clonalNetwork <- function(sc, plot <- ggraph(graph, layout = centers[match(names(V(graph)), rownames(centers)),]) + geom_point(data = coord, aes(x = coord[,1], y = coord[,2], color = group.by)) + - geom_edge_bend(aes(edge_color = as.numeric(weight)), alpha = 0.7, width = 1, + geom_edge_bend(aes(edge_color = as.numeric(weight)), + alpha = 0.7, + width = 1, arrow = arrow(length = unit(4, 'mm')), end_cap = circle(3, 'mm'), angle_calc = "across", From fe628803aa6b1450f8a290056f2cc5441b7b2db3 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 13 Oct 2023 08:34:49 -0500 Subject: [PATCH 087/142] Speed up edit distance calc --- NAMESPACE | 1 + R/clonalBias.R | 2 +- R/clonalNetwork.R | 1 + R/combineContigs.R | 53 ++++++++++++++++++++++++------------------ R/vizGenes.R | 2 +- vignettes/vignette.Rmd | 8 +++---- 6 files changed, 38 insertions(+), 29 deletions(-) diff --git a/NAMESPACE b/NAMESPACE index 6b6e8a2e..c41eeaeb 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -99,6 +99,7 @@ importFrom(stats,optim) importFrom(stats,pgamma) importFrom(stats,quantile) importFrom(stats,sd) +importFrom(stats,setNames) importFrom(stringdist,stringdist) importFrom(stringdist,stringdistmatrix) importFrom(stringr,str_c) diff --git a/R/clonalBias.R b/R/clonalBias.R index 25d47f21..7c94cd77 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -53,7 +53,7 @@ clonalBias <- function(df, min.expand=10, exportTable = FALSE, palette = "inferno") { - .checkSingleObject(sc) + .checkSingleObject(df) cloneCall <- .theCall(cloneCall) #Calculating bias bias <- .get_clono_bias(df, diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index 0203f534..c6cb9957 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -52,6 +52,7 @@ #' @importFrom dplyr %>% group_by select summarize_all count #' @importFrom tidygraph as_tbl_graph activate #' @importFrom ggraph ggraph geom_edge_bend geom_node_point scale_edge_colour_gradientn circle guide_edge_colourbar +#' @importFrom stats setNames #' @export #' @return ggplot object #' diff --git a/R/combineContigs.R b/R/combineContigs.R index 1e6e4adf..9312e08a 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -276,40 +276,49 @@ combineBCR <- function(df, tmp <- na.omit(unique(dictionary[,c(chain, "v.gene")])) #chunking the sequences for distance by v.gene unique.v <- na.omit(unique(tmp$v.gene)) - edge.list <- list() edge.list <- lapply(unique.v, function(y) { - #for(y in unique.v) { - secondary.list <- NULL + #for(y in unique.v) { + secondary.list <- list() filtered_df <- tmp %>% filter(v.gene == y) filtered_df <- filtered_df[!is.na(filtered_df[,chain]),] nucleotides <- unique(filtered_df[,chain]) if (length(nucleotides) > 1) { + chain_col_number <- 1 + nucleotide_lengths <- nchar(nucleotides) + # Pre-allocate list + list <- vector("list", length = length(nucleotides)) + for (i in 1:(length(nucleotides) - 1)) { - list <- NULL + temp_list <- vector("list", length = length(nucleotides) - i) + + idx_i <- tmp[, chain_col_number] == nucleotides[i] + len_i <- nucleotide_lengths[i] + for (j in (i + 1):length(nucleotides)) { distance <- stringdist::stringdist(nucleotides[i], nucleotides[j], method = "lv") - distance <- 1-distance/((nchar(nucleotides[i]) + nchar(nucleotides[j]))/2) + distance_norm <- 1 - distance / ((len_i + nucleotide_lengths[j]) / 2) - if (!is.na(distance) & distance > threshold) { - if(any(which(tmp[,chain] == nucleotides[j]) %!in% which(tmp[,chain] == nucleotides[i]))) { - stored.positions <- which(tmp[,chain] == nucleotides[j])[which(tmp[,chain] == nucleotides[j]) %!in% which(tmp[,chain] == nucleotides[i])] + if (!is.na(distance_norm) & distance_norm > threshold) { + idx_j <- tmp[, chain_col_number] == nucleotides[j] + stored_positions <- idx_j & !idx_i + + if(any(stored_positions)) { # Store this pair in the edge list that is not the same chain - ex.grid <- expand.grid(which(tmp[,chain] == nucleotides[i]), stored.positions) - colnames(ex.grid) <- c("from", "to") - list[[j]] <- ex.grid - - + temp_list[[j - i]] <- data.frame(from = which(idx_i), + to = which(stored_positions)) } - } - } - #Remove any NULL or 0 list elements - if(length(list) > 0) { - list <- list[-which(unlist(lapply(list, is.null)))] - list <- list[lapply(list,length)>0] - list <- bind_rows(list) %>% as.data.frame() - secondary.list[[i]] <- list + } } + list[[i]] <- do.call(rbind, temp_list) # Collapsing all data.frames in temp_list + } + + #Remove any NULL or 0 list elements + if(length(list) > 0) { + list <- list[-which(unlist(lapply(list, is.null)))] + list <- list[lapply(list,length)>0] + list <- bind_rows(list) %>% as.data.frame() + secondary.list[[i]] <- list } #Remove any NULL or 0 list elements if(length(secondary.list) > 0) { @@ -319,8 +328,6 @@ combineBCR <- function(df, } secondary.list }) - #Remove NULL elements and make data.frame - edge.list = edge.list[-which(sapply(edge.list, is.null))] edge.list <- bind_rows(edge.list) if (nrow(edge.list) > 0) { diff --git a/R/vizGenes.R b/R/vizGenes.R index 212ced07..2569c005 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -45,7 +45,7 @@ vizGenes <- function(df, scale = TRUE, exportTable = FALSE, palette = "inferno") { - + element.names <- NULL sco <- is_seurat_object(df) | is_se_object(df) #Extracting group.by in case of null diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index ac4e772a..26f81fbb 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -743,10 +743,10 @@ We can also use **exportClones** to quickly get clones that are shared across mu ```{r} shared.clones <- clonalNetwork(scRep_example, - reduction = "umap", - identity = "ident", - cloneCall = "aa", - exportClones = TRUE) + reduction = "umap", + group.by = "ident", + cloneCall = "aa", + exportClones = TRUE) head(shared.clones) ``` From d45bbb851472edce1ba980dce4cc4bd2af8ea7e1 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 13 Oct 2023 14:31:32 -0500 Subject: [PATCH 088/142] Update cloneCompare relabeling clones not goes in proportion order --- R/clonalCompare.R | 72 ++++++++++++++++++++++-------------------- vignettes/vignette.Rmd | 12 +++---- 2 files changed, 43 insertions(+), 41 deletions(-) diff --git a/R/clonalCompare.R b/R/clonalCompare.R index 4816b163..d01bee40 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -1,9 +1,9 @@ -#' Demonstrate the difference in clonal proportion between clonotypes +#' Demonstrate the difference in clonal proportion between clones #' #' This function produces an alluvial or area graph of the proportion of -#' the indicated clonotypes for all or selected samples. clonotypes can be -#' selected using the clonotypes parameter with the specific sequence of -#' interest or using the number parameter with the top n clonotypes by +#' the indicated clones for all or selected samples. clones can be +#' selected using the clones parameter with the specific sequence of +#' interest or using the number parameter with the top n clones by #' proportion to be visualized. #' #' @examples @@ -24,7 +24,7 @@ #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param samples The specific samples to isolate for visualization. -#' @param clonotypes The specific clonal sequences of interest. +#' @param clones The specific clonal sequences of interest. #' @param top.clones The top number of clonotype sequences per group. #' @param highlight.clones Clonal sequences to highlight, if present, #' all other clones returned will be grey. @@ -36,25 +36,26 @@ #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 +#' @importFrom stringr str_sort #' #' @export #' @return ggplot of the proportion of total sequencing read of -#' selecting clonotypes +#' selecting clones clonalCompare <- function(df, - cloneCall = "strict", - chain = "both", - samples = NULL, - clonotypes = NULL, - top.clones = NULL, - highlight.clones = NULL, - relabel.clones = FALSE, - group.by = NULL, - graph = "alluvial", - exportTable = FALSE, - palette = "inferno"){ + cloneCall = "strict", + chain = "both", + samples = NULL, + clones = NULL, + top.clones = NULL, + highlight.clones = NULL, + relabel.clones = FALSE, + group.by = NULL, + graph = "alluvial", + exportTable = FALSE, + palette = "inferno"){ #Tie goes to indicated clones over top clones - if(!is.null(top.clones) & !is.null(clonotypes)) { + if(!is.null(top.clones) & !is.null(clones)) { top.clones <- NULL } cloneCall <- .theCall(cloneCall) @@ -68,7 +69,7 @@ clonalCompare <- function(df, } tbl <- as.data.frame(table(df[[i]][,cloneCall])) tbl[,2] <- tbl[,2]/sum(tbl[,2]) - colnames(tbl) <- c("clonotypes", "Proportion") + colnames(tbl) <- c("clones", "Proportion") tbl$Sample <- names(df[i]) Con.df <- rbind.data.frame(Con.df, tbl) } @@ -77,20 +78,20 @@ clonalCompare <- function(df, if (!is.null(samples)) { Con.df <- Con.df[Con.df$Sample %in% samples,] } - if (!is.null(clonotypes)) { - Con.df <- Con.df[Con.df$clonotypes %in% clonotypes,] + if (!is.null(clones)) { + Con.df <- Con.df[Con.df$clones %in% clones,] } else if (!is.null(top.clones)) { top <- Con.df %>% group_by(Con.df[,3]) %>% slice_max(n = top.clones, order_by = Proportion, with_ties = FALSE) - Con.df <- Con.df[Con.df$clonotypes %in% top$clonotypes,] + Con.df <- Con.df[Con.df$clones %in% top$clones,] } if (nrow(Con.df) < length(unique(Con.df$Sample))) { - stop("Reasses the filtering strategies here, there is not - enough clonotypes to examine.") + stop("Reasses the filtering strategies here, there are not + enough clones to examine.") } - #Clonotype relabeling - clones.returned <- as.vector(unique(Con.df[,"clonotypes"])) + #Clones relabeling + clones.returned <- as.vector(unique(Con.df[order(Con.df[,"Proportion"], decreasing = TRUE),"clones"])) if (relabel.clones) { new.clones <- paste0("Clonotype: ", seq_len(length(clones.returned))) names(new.clones) <- clones.returned @@ -98,8 +99,9 @@ clonalCompare <- function(df, if(!is.null(highlight.clones)) { highlight.clones <- unname(new.clones[which(names(new.clones) %in% highlight.clones)]) } - Con.df[,"clonotypes"] <- new.clones[as.vector(Con.df[,"clonotypes"])] - clones.returned <- as.vector(unique(Con.df[,"clonotypes"])) + Con.df[,"clones"] <- new.clones[as.vector(Con.df[,"clones"])] + Con.df[,"clones"] <- factor(Con.df[,"clones"], levels = str_sort(unique(Con.df[,"clones"]), numeric = TRUE)) + clones.returned <- as.vector(unique(Con.df[,"clones"])) } if (exportTable == TRUE) { return(Con.df) @@ -107,12 +109,12 @@ clonalCompare <- function(df, #Plotting Functions plot <- ggplot(Con.df, aes(x = Sample, - fill = clonotypes, - group = clonotypes, - stratum = clonotypes, - alluvium = clonotypes, + fill = clones, + group = clones, + stratum = clones, + alluvium = clones, y = Proportion, - label = clonotypes)) + + label = clones)) + theme_classic() + theme(axis.title.x = element_blank(), legend.text=element_text(size=rel(0.5)), @@ -121,7 +123,7 @@ clonalCompare <- function(df, plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") } else if (graph == "area") { plot <- plot + - geom_area(aes(group = clonotypes), color = "black") + geom_area(aes(group = clones), color = "black") } #Highlighting specific clones @@ -132,7 +134,7 @@ clonalCompare <- function(df, names(clone.colors) <- clones.returned plot <- plot + scale_fill_manual(values = clone.colors) } else { - plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"clonotypes"])))) + plot <- plot + scale_fill_manual(values = .colorizer(palette, length(unique(Con.df[,"clones"])))) } return(plot) } diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 26f81fbb..d02ff6f8 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -308,12 +308,12 @@ We can also choose to highlight specific clonotypes, such as in the case of *"CV ```{r} clonalCompare(combined, - top.clones = 10, - highlight.clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"), - relabel.clones = TRUE, - samples = c("P17B", "P17L"), - cloneCall="aa", - graph = "alluvial") + top.clones = 10, + highlight.clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"), + relabel.clones = TRUE, + samples = c("P17B", "P17L"), + cloneCall="aa", + graph = "alluvial") ``` ## vizGenes From 2fcbb3bced44540f1eaead0d5cdd9e5ce3829a02 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 13 Oct 2023 14:52:17 -0500 Subject: [PATCH 089/142] Update combineExpression.R Added frequency cap detection, will automatically update the top cloneSize argument if too small. --- R/combineExpression.R | 41 +++++++++++++++++++++++------------------ 1 file changed, 23 insertions(+), 18 deletions(-) diff --git a/R/combineExpression.R b/R/combineExpression.R index 988fb95a..435633e9 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -1,15 +1,13 @@ -#' Adding clonotype information to a Seurat or SCE object +#' Adding clonotype information to a single-cell object #' #' This function adds the immune receptor information to the Seurat or #' SCE object to the meta data. By default this function also calculates -#' the frequencies of the clonotypes by sequencing run (group.by = "none"). -#' To change how the frequencies are calculated, select a column header for -#' the group.by variable. Importantly, before using combineExpression() -#' ensure the barcodes of the seurat or SCE object match the barcodes in the -#' output of the combinedContig() call. Check changeNames() to change the -#' prefix of the Seurat object. If combining more than one immune receptor -#' type, barcodes with both receptors will be removed during the combination -#' process. +#' the frequencies and proportion of the clonotypes by sequencing +#' run (**group.by** = "none"). To change how the frequencies/proportions +#' are calculated, select a column header for the **group.by** variable. +#' Importantly, before using \code{\link{combineExpression}} ensure the +#' barcodes of the single-cell object object match the barcodes in the output +#' of the \code{\link{combineTCR}} or \code{\link{combineBCR}}. #' #' @examples #' #Getting the combined contigs @@ -25,20 +23,22 @@ #' #Using combineExpresion() #' sce <- combineExpression(combined, sce) #' -#' @param df The product of CombineTCR() or CombineBCR() or a list of -#' both c(CombineTCR(), combineBCR()) +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or a list of +#' both c(\code{\link{combineTCR}}, c\code{\link{combineBCR}}) #' @param sc The seurat or SingleCellExperiment (SCE) object to attach #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt) CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param group.by The column label in the combined contig object in which +#' @param group.by The column label in the combined clones in which #' clonotype frequency will be calculated. "none" will keep the list as is, -#' while NULL will merge all the contigs into a single data frame. -#' @param proportion Whether to use the total frequency (FALSE) or the -#' proportion (TRUE) of the clonotype based on the group.by variable. -#' @param cloneSize The bins for the grouping based on frequency +#' while NULL will merge all the clones into a single data frame. +#' @param proportion Whether to proportion (**TRUE**) or total frequency (**FALSE**) +#' of the clonotype based on the group.by variable. +#' @param cloneSize The bins for the grouping based on proportion or frequency. +#' If proportion is **FALSE** and the *cloneSizes* are not set high enough based +#' on frequency, the upper limit of *cloneSizes* will be automatically amended. #' @param filterNA Method to subset seurat object of barcodes without #' clonotype information #' @param addLabel This will add a label to the frequency header, allowing @@ -58,8 +58,7 @@ combineExpression <- function(df, group.by ="none", proportion = TRUE, filterNA = FALSE, - cloneSize = c( - Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1), + cloneSize = c(Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1), addLabel = FALSE) { call_time <- Sys.time() @@ -111,6 +110,12 @@ combineExpression <- function(df, "CTaa", "CTstrict", "clonalProportion", "clonalFrequency")] } + #Detect if largest cloneSize category is too small for experiment and amend + #this prevents a ton of NA values in the data + if(!proportion && max(na.omit(Con.df[,"clonalFrequency"])) > cloneSize[length(cloneSize)]) { + cloneSize[length(cloneSize)] <- max(na.omit(Con.df[,"clonalFrequency"])) + } + #Creating the bins for cloneSize Con.df$cloneSize <- NA From 003cba8e05c8b0acfc58343b2f164cfe4ba3253a Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 14 Oct 2023 08:49:50 -0500 Subject: [PATCH 090/142] Update hex --- man/clonalCompare.Rd | 14 +++++++------- man/combineExpression.Rd | 32 ++++++++++++++++---------------- www/screpertoire_hex.png | Bin 167494 -> 185359 bytes 3 files changed, 23 insertions(+), 23 deletions(-) diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index ef399c16..0d25dae1 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -2,14 +2,14 @@ % Please edit documentation in R/clonalCompare.R \name{clonalCompare} \alias{clonalCompare} -\title{Demonstrate the difference in clonal proportion between clonotypes} +\title{Demonstrate the difference in clonal proportion between clones} \usage{ clonalCompare( df, cloneCall = "strict", chain = "both", samples = NULL, - clonotypes = NULL, + clones = NULL, top.clones = NULL, highlight.clones = NULL, relabel.clones = FALSE, @@ -32,7 +32,7 @@ e.g. "both", "TRA", "TRG", "IGH", "IGL".} \item{samples}{The specific samples to isolate for visualization.} -\item{clonotypes}{The specific clonal sequences of interest.} +\item{clones}{The specific clonal sequences of interest.} \item{top.clones}{The top number of clonotype sequences per group.} @@ -53,13 +53,13 @@ to group the new list. NULL will return clusters.} } \value{ ggplot of the proportion of total sequencing read of -selecting clonotypes +selecting clones } \description{ This function produces an alluvial or area graph of the proportion of -the indicated clonotypes for all or selected samples. clonotypes can be -selected using the clonotypes parameter with the specific sequence of -interest or using the number parameter with the top n clonotypes by +the indicated clones for all or selected samples. clones can be +selected using the clones parameter with the specific sequence of +interest or using the number parameter with the top n clones by proportion to be visualized. } \examples{ diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index 725895a2..a34c8d5b 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/combineExpression.R \name{combineExpression} \alias{combineExpression} -\title{Adding clonotype information to a Seurat or SCE object} +\title{Adding clonotype information to a single-cell object} \usage{ combineExpression( df, @@ -18,8 +18,8 @@ combineExpression( ) } \arguments{ -\item{df}{The product of CombineTCR() or CombineBCR() or a list of -both c(CombineTCR(), combineBCR())} +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or a list of +both c(\code{\link{combineTCR}}, c\code{\link{combineBCR}})} \item{sc}{The seurat or SingleCellExperiment (SCE) object to attach} @@ -30,17 +30,19 @@ VDJC gene + CDR3 nucleotide (strict).} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} -\item{group.by}{The column label in the combined contig object in which +\item{group.by}{The column label in the combined clones in which clonotype frequency will be calculated. "none" will keep the list as is, -while NULL will merge all the contigs into a single data frame.} +while NULL will merge all the clones into a single data frame.} -\item{proportion}{Whether to use the total frequency (FALSE) or the -proportion (TRUE) of the clonotype based on the group.by variable.} +\item{proportion}{Whether to proportion (**TRUE**) or total frequency (**FALSE**) +of the clonotype based on the group.by variable.} \item{filterNA}{Method to subset seurat object of barcodes without clonotype information} -\item{cloneSize}{The bins for the grouping based on frequency} +\item{cloneSize}{The bins for the grouping based on proportion or frequency. +If proportion is **FALSE** and the *cloneSizes* are not set high enough based +on frequency, the upper limit of *cloneSizes* will be automatically amended.} \item{addLabel}{This will add a label to the frequency header, allowing the user to try multiple group.by variables or recalculate frequencies after @@ -53,14 +55,12 @@ information \description{ This function adds the immune receptor information to the Seurat or SCE object to the meta data. By default this function also calculates -the frequencies of the clonotypes by sequencing run (group.by = "none"). -To change how the frequencies are calculated, select a column header for -the group.by variable. Importantly, before using combineExpression() -ensure the barcodes of the seurat or SCE object match the barcodes in the -output of the combinedContig() call. Check changeNames() to change the -prefix of the Seurat object. If combining more than one immune receptor -type, barcodes with both receptors will be removed during the combination -process. +the frequencies and proportion of the clonotypes by sequencing +run (**group.by** = "none"). 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    + + + + + + + +
    +

    scRepertoire VERSION 2.0.0 (2023-10-23)

    +
    +

    NEW FEATURES

    +
      +
    • Added percentAA()
      • +
    • Added percentGenes()
      • +
    • Added percentVJ()
      • +
    • Added percentKmer()
      • +
    • Added exportClones()
      • +
    • Changed compareClonotypes to clonalCompare()
      • +
    • Changed clonotypeSizeDistribution to +clonalSizeDistribution()
      • +
    • Changed scatterClonotypes to clonalScatter()
      • +
    • Changed quantContig to clonalQuant()
      • +
    • Changed highlightClonotypes to highlightClones()
      • +
    • Changed lengthContigs to clonalLength()
      • +
    • Changed occupiedscRepertoire to clonalOccupy()
      • +
    • Changed abundanceContig to clonalAbundance()
      • +
    • Changed alluvialClonotypes to clonalClones()
      • +
    • Added features to clonalCompare() to allow for +highlighting sequences, relabeling clonotypes.
      • +
    +
    +
    +

    UNDERLYING CHANGES

    +
      +
    • startrac diversity metric re-implemented to remove startrac-class +object intermediary
      • +
    • implemented powerTCR locally to reduce dependencies and continue +support
      • +
    • Universalized underlying function language and intermediate +variables
      • +
    • License change to MIT
      • +
    • group.by and split.by have been consolidated into single group.by +parameter
      • +
    • Added support for .json, Omniscope, and MiXCR formats for +loadContigs()
      • +
    +
    +
    +

    DEPRECATED AND DEFUNCT

    +
      +
    • Deprecate stripBarcodes()
      • +
    • Deprecate expression2List() (now only an internal function).
      • +
    • Deprecate checkContigs()
      • +
    +
    +
    +
    +

    scRepertoire VERSION 1.11.0

    +
      +
    • Rebasing for the purposes of bioconductor version
      • +
    +
    +
    +

    scRepertoire VERSION 1.7.5

    +
      +
    • Fixed combineBCR() to allow for non-related sequences
      • +
    +
    +
    +

    scRepertoire VERSION 1.7.4

    +
    +

    NEW FEATURES

    +
      +
    • checkContigs() function to quantify the percentages of NA values by +genes or sequences
      • +
    • exportClones to clonalNetwork() to isolate clones shared across +identities.
      • +
    +
    +
    +

    UNDERLYING CHANGES

    +
      +
    • Fix issue with clonalDiversity() and skipping boots
      • +
    • Fixing underlying assumptions with clonalBias()
      • +
    • Adding reads variable to parseAIRR
      • +
    • Fixing handling of samples parameter in combine contain +functions
      • +
    • removed need to relevel the cloneType factor after +combineExpression()
      • +
    • set up lapply() for combineBCR() and clusterTCR() - no more pairwise +distance matrix calculation
      • +
    • loadContigs() support for data.frames or lists of contigs
      • +
    • Added examples for loadContigs() to test function
      • +
    • Removed requirement for T cell type designation - will combine A/B +and G/D simultaneously
      • +
    • Updated combineBCR() to chunk nucleotide edit distance calculations +by V gene and give option to skip edit distance calculation with +call.related.clones = FALSE
      • +
    • Updated clusterTCR() to use lvcompare() function and base edit +distances of V gene usage.
      • +
    +
    +
    +
    +

    scRepertoire VERSION 1.7.3

    +
      +
    • Fix misspellings for parse contains functions
      • +
    • Optimize WAT3R and 10x loadContigs()
      • +
    • Remove combineTRUST4 - superseded by loadContigs()
      • +
    • Added support of TRUST4 for combineBCR()
      • +
    • Added support for BD in loadContigs()
      • +
    • loadContigs() TRUST4 parsing allows for all NA values in a +chain
      • +
    • combineExpression() group.by = NULL will now collapse the whole +list.
      • +
    +
    +
    +

    scRepertoire VERSION 1.7.2

    +
      +
    • ClonalDiversity() now has skip.boots to stop bootstrapping and +downsampling
      • +
    +
    +
    +

    scRepertoire VERSION 1.7.0

    +
      +
    • Rebumping the version change with new release of Bioconductor
      • +
    • Added mean call to the heatmap of vizGenes()
      • +
    • To combineTCR, filteringMulti now checks to remove list elements +with 0 cells.
      • +
    • Removed top_n() call as it is now deprecated, using slice_max() +without ties.
      • +
    • Add arrange() call during parseTCR() to organize the chains
      • +
    • Correct the gd flip in the combineContig and subsequent +functions
      • +
    • Removed viridis call in the clonalNetwork() function that was +leading to errors
      • +
    • Matched syntax for strict clonotype in combineBCR()
      • +
    • Added group.by variable to all applicable visualizations
      • +
    • Added return.boots to clonalDiversity(), allow for export of all +bootstrapped values
      • +
    +
    +
    +

    scRepertoire VERSION 1.5.4

    +
      +
    • modified grabMeta() internal function to no longer assume the active +Identity is clusters.
      • +
    • checkBlanks() now checks if a blank was detected before trying to +remove it
      • +
    • clonalNetwork() automatically resulted in default error message, bug +now removed.
      • +
    • clonotypeBias now adds z-score of bias when matrix is exported. +exportTable parameter is now fixed.
      • +
    +
    +
    +

    scRepertoire VERSION 1.5.3

    +
      +
    • Added loadContigs for non-10X formatted single-cell data
      • +
    • removed combineTRUST4, superseded by loadContigs
      • +
    • combineTCR() now allows for > 3 recovered TCRs per barcode
      • +
    • Readded the filtering steps to combineTCR(), will detect if data is +from 10X and automatically remove nonproductive or multi chains.
      • +
    • Updated parseTCR() to include evaluation for gamma/delta +chains.
      • +
    +
    +
    +

    scRepertoire VERSION 1.5.2

    +
      +
    • Arbitrarily numbering system to match new bio conductor dev +version
      • +
    • highlightClonotypes() now returns the specific clones instead of +clonotype 1, …
      • +
    • compareClonotypes numbers parameter now for group-wide numbers and +not overall top X numbers
      • +
    • Fixed issue with clonalDiversity that cause errors when group.by +parameter was used.
      • +
    • modified parseBCR() to reduce complexity and assume lambda >> +kappa
      • +
    • fixed clusterTCR() function broken with Seurat Objects
      • +
    • checkContigs no ensures data frames and that “” are converted into +NAs
      • +
    • modified makeGenes() internal function changing na.omit to +str_replace_na() and separating the BCR calls by chain to prevent +combination errors.
      • +
    +
    +
    +

    scRepertoire VERSION 1.3.5

    +
      +
    • Modified parseBCR() to check for contents of the chains. Resolve +issue with placing light chain into heavy chain slots when 2 contigs are +present.
      • +
    • Updated checkBlanks to include NA evaluation and placed the check in +all .viz functions
      • +
    • Added clonalNetwork() function
      • +
    • Modified diversity visualization to remove outliers and place graphs +on a single line
      • +
    • Modified clonalOverlay() to use new internal getCoord() function +like clonalNetwork()
      • +
    • Added threshold parameter to clonesizeDistribution()
      • +
    • Added support for single-cell objects to clusterTCR()
      • +
    +
    +
    +

    scRepertoire VERSION 1.3.4

    +
      +
    • Modification in clusterTCR() and combineBCR() to speed up the +comparison and use less memory
      • +
    • FilteringMulti, now isolates the top contig by chain, then for +barcodes with chains > 2, isolates the top expressing chains. This +substantially increases the speed of the filtering step.
      • +
    • Modified makeGenes() internal function to use strings str_c()
      • +
    • Added threshold parameter to combineTRUST4 for B cell +manipulation
      • +
    • Changed combineTCR function to prevent cell type mix up and +clarified in function documentation.
      • +
    • vizGenes can now be used to look at other component genes of the +receptor and “separate” parameter was replaced by “y.axis” +parameter.
      • +
    • Added clonotypeBias() function for inter-cluster comparison.
      • +
    • Fixed clusterTCR() and combineBCR() assumption that you will have +unrelated clones.
      • +
    +
    +
    +

    scRepertoire VERSION 1.3.3

    +
      +
    • CombineBCR() auto naming function updated to actually name the list +elements.
      • +
    • Added createHTOContigList() function to create contain list of +multiplexed experiments. Fixed issue with groupBy variable
      • +
    • Added Inv.Pielou matric to diversity call - this is essentially +1-shannon/ln(length). Due to the bootstrapping the length with be +constant.
      • +
    • Added include.na and split.by to occupiedscRepertoire and changed +labeling depending on frequency vs proportion
      • +
    • Added support for single-cell objects for most visualizations, list +organizing by single-cell object can be called using split.by +variable
      • +
    • All group and groupBy parameters are now group.by.
      • +
    +
    +
    +

    scRepertoire VERSION 1.3.2

    +
      +
    • This is the new numbering scheme apologies - we are all up-to-date +now and now cell ranger >= 5 will # work on bioconductor, so let’s +all just take that as a win.
      • +
    • added dot.size parameter to scatterClonotype
      • +
    • filteringMulti now subsets clonotypes with contains >=2, to +prevent 2 of the same chains
      • +
    • changed how coldata is added to SCE objects using merge instead of +union
      • +
    • Can now add BCR and TCR simultaneously by making large list
      • +
    • scatter plotting code is not so ugly and allows for user to select +dot.size as a variable on the x or y axis
      • +
    • Removed regressClonotype function - too many dependencies required, +adding an additional vignette to go through the process
      • +
    • Added chain option to visualizations and combineExpression to allow +users to facilitate single chains - removed chain option from +combineTCR/BCR/TRUST4 (the combined object will have both chains no +matter what)
      • +
    • Added NA filter to combineTCR/BCR/TRUST4 for cell barcodes with only +NA values
      • +
    • Added NA filter to expression2List() for cells with NA +clonotypes.
      • +
    • Updated VizGene to order the genes automatically by highest to +lowest variance
      • +
    • Updated VizGene to pull the correct genes based on selection
      • +
    • Updated parse method - old version had issue with place +V–>J–>D in the TRB/Heavy chains
      • +
    • Simplified the clonalDiversity() to allow for more options in +organizing plot and box plots.
      • +
    • CombineExpression() adds the groupBy variable to Frequency, allowing +for multiple calculations to be saved in the meta data.
      • +
    • Default color scheme now uses viridis plasma, because it I am on +transfusion medicine.
      • +
    +
    +
    +

    scRepertoire VERSION 1.2.2

    +
      +
    • added the combineTRUST4 function to parse contigs from TUST4 +pipeline
      • +
    • added the filter of contigs by chain in the combineTCR, combineBCR, +and combineTRUST4 functions
      • +
    • no longer require the ID in the combineTCR/BCR/TRUST4 functions
      • +
    • added jaccard index for overlap analysis
      • +
    • replaced vizVgene with vizGene - allowing users to look at any gene +in the combinedContig object
      • +
    • Fixed coloring scale on the overlap analysis
      • +
    • Added regressClonotype function using harmony to remove the +clonotype effect on feature space
      • +
    • allowed occupiedRepertoire to use proportion.
      • +
    • added scatterClonotype function to Viz.R
      • +
    +
    +
    +

    scRepertoire VERSION 1.2.1

    +
      +
    • number of changes to the parseTCR/BCR functions to limit +assumptions
      • +
    • Changed grabMeta to include assessment of colnames
      • +
    • fixed lengthDF handling of single chains with multi chains stored - +;
      • +
    • Added labels to alluvialClonotype and occupiedClonotype +plotting
      • +
    +
    +
    +

    scRepertoire VERSION 1.1.4

    +
      +
    • replaced hammingCompare with lvCompare to enable superior clonotype +calling in combineBCR function.
      • +
    • added proportion to combineExpression() function so users no longer +need to know absolute frequencies when combining the contiguous +information.
      • +
    • added clusterTCR() and clonalOverlay() functions.
      • +
    • added downsampling to the diversity calculations
      • +
    • replaced hammingCompare with lvCompare to enable superior clonotype +calling in combineBCR function.
      • +
    • added proportion to combineExpression() function so users no longer +need to know absolute frequencies when combining the contiguous +information.
      • +
    • added clusterTCR() and clonalOverlay() functions.
      • +
    • added downsampling to the diversity calculations
      • +
    • Clonal Overlap Coefficient issue fixed, was comparing unique +barcodes and not clonotypes
      • +
    • Added function checkBlanks to remove list elements without +clonotypes, this prevents errors for visualizations
      • +
    • Re-added Startrac metrics by stripping down the package and adding +it piecemeal
      • +
    • Heavily modified dependencies to reduce total number
      • +
    +
    +
    +

    scRepertoire VERSION 1.0.0

    +
      +
    • removed dependencies ggfittext and ggdendrogram
      • +
    • clonesizeDistribution now returns a plot() function
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.18

    +
      +
    • Updated author information in the vignette
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.17

    +
      +
    • Updated NEWS formatting
      • +
    • Edited DESCRIPTION to SingleCellExperiment R package
      • +
    • Updated information in the vignette
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.16

    +
      +
    • Added getCirclize()
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.15

    +
      +
    • Modified numerator for index function
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.14

    +
      +
    • Removed bracket from indexing function
      • +
    +

    #scRepertoire VERSION 0.99.13

    +
      +
    • Added exportTable to remaining viz functions
      • +
    • Modified morisita index to correct error
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.12

    +
      +
    • Reducing the size of the screp_example to fulfill < 5 mB +requirement. Randomly samples 100 cells and removed RNA counts from +Seurat object
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.11

    +
      +
    • Updated compareClonotype to allow for clonotype comparisons
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.10

    +
      +
    • Bioconductor did not detect the version update.
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.9

    +
      +
    • Bioconductor had no love - changed the Seurat package to imports +instead of required, see if that will address the compiling issue that +results in a killed: 9 error.
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.8

    +
      +
    • Passed checks on system, let’s see how much bioconductor hates +it
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.7

    +
      +
    • But really this time, changed the colData import
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.6

    +
      +
    • Changed colData import
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.5

    +
      +
    • Added screp_example data to package
      • +
    • Added visVgene function for visualizing the distribution of V genes +in TCR
      • +
    • Added support for monocle to combineExpression function
      • +
    • Updated documentation for combineTCR() and combineBCR()
      • +
    • Updated documentation to utilize SingleCellExperiment formats
      • +
    • Updated Vignette to utilize SingleCellExperiment formats
      • +
    • Added Author information to vignette
      • +
    • Add intro and conclusion to vignette
      • +
    • Removed html knitted vignette
      • +
    • Removed descriptive code snippets
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.4

    +
      +
    • Modified expression2List() to allow for variables across meta +data
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.1

    +
      +
    • Changed R (>= 3.6) to R (>= 4.0)
      • +
    +
    +
    +

    scRepertoire VERSION 0.99.0

    +
      +
    • Changed DESCRIPTION version to 0.99.0
      • +
    • Removed file seurat_example.rda, accidentally committed
      • +
    • Deleted git attributes
      • +
    • reduced Seurat object size for alluvialClonotype in vignette
      • +
    • Changed the alluvialClonotype assessment to account for only 1 +condition
      • +
    +
    +     +
    +

    scRepertoire VERSION 1.2.2

    +
      +
    • Removed Startrac-based functions in order to pass build on +Bioconductor.
      • +
    +

    DEPRECATED AND DEFUNCT

    +
      +
    • Deprecate StartracDiversity()
      • +
    +
    +
    +

    scRepertoire VERSION 1.2.0

    +

    SIGNIFICANT USER-VISIBLE CHANGES

    +
      +
    • Added support for SingleCellExperiement format.
      • +
    +

    DEPRECATED AND DEFUNCT

    +
      +
    • Deprecate combineSeurat in favor or combineExpression().
      • +
    • Deprecate seurat2List in favor of expression2List().
      • +
    +
    + + + + +
    + + + + + + + + + + + + + + + diff --git a/NEWS.md b/NEWS.md new file mode 100644 index 00000000..d223cbb8 --- /dev/null +++ b/NEWS.md @@ -0,0 +1,332 @@ + +# scRepertoire VERSION 2.0.0 (2023-10-23) + +## NEW FEATURES + +* Added ```percentAA()``` +* Added ```percentGenes()``` +* Added ```percentVJ()``` +* Added ```percentKmer()``` +* Added ```exportClones()``` +* Changed compareClonotypes to ```clonalCompare()``` +* Changed clonotypeSizeDistribution to ```clonalSizeDistribution()``` +* Changed scatterClonotypes to ```clonalScatter()``` +* Changed quantContig to ```clonalQuant()``` +* Changed highlightClonotypes to ```highlightClones()``` +* Changed lengthContigs to ```clonalLength()``` +* Changed occupiedscRepertoire to ```clonalOccupy()``` +* Changed abundanceContig to ```clonalAbundance()``` +* Changed alluvialClonotypes to ```clonalClones()``` +* Added features to ```clonalCompare()``` to allow for highlighting sequences, relabeling clonotypes. + +## UNDERLYING CHANGES + +* startrac diversity metric re-implemented to remove startrac-class object intermediary +* implemented powerTCR locally to reduce dependencies and continue support +* Universalized underlying function language and intermediate variables +* License change to MIT +* group.by and split.by have been consolidated into single group.by parameter +* Added support for .json, Omniscope, and MiXCR formats for ```loadContigs()``` +* Made GitHub.io webstie for support/vignettes/FAQ +* Restructured NEWS Tracking + +## DEPRECATED AND DEFUNCT + +* Deprecate stripBarcodes() +* Deprecate expression2List() (now only an internal function). +* Deprecate checkContigs() + + +# scRepertoire VERSION 1.11.0 + +* Rebasing for the purposes of bioconductor version + +# scRepertoire VERSION 1.7.5 + +* Fixed combineBCR() to allow for non-related sequences + +# scRepertoire VERSION 1.7.4 + +## NEW FEATURES + +* checkContigs() function to quantify the percentages of NA values by genes or sequences +* exportClones to clonalNetwork() to isolate clones shared across identities. + +## UNDERLYING CHANGES +* Fix issue with clonalDiversity() and skipping boots +* Fixing underlying assumptions with clonalBias() +* Adding reads variable to parseAIRR +* Fixing handling of samples parameter in combine contain functions +* removed need to relevel the cloneType factor after combineExpression() +* set up lapply() for combineBCR() and clusterTCR() - no more pairwise distance matrix calculation +* loadContigs() support for data.frames or lists of contigs +* Added examples for loadContigs() to test function +* Removed requirement for T cell type designation - will combine A/B and G/D simultaneously +* Updated combineBCR() to chunk nucleotide edit distance calculations by V gene and give option to skip edit distance calculation with call.related.clones = FALSE +* Updated clusterTCR() to use lvcompare() function and base edit distances of V gene usage. + + +# scRepertoire VERSION 1.7.3 + +* Fix misspellings for parse contains functions +* Optimize WAT3R and 10x loadContigs() +* Remove combineTRUST4 - superseded by loadContigs() +* Added support of TRUST4 for combineBCR() +* Added support for BD in loadContigs() +* loadContigs() TRUST4 parsing allows for all NA values in a chain +* combineExpression() group.by = NULL will now collapse the whole list. + + +# scRepertoire VERSION 1.7.2 + +* ClonalDiversity() now has skip.boots to stop bootstrapping and downsampling + + +# scRepertoire VERSION 1.7.0 + +* Rebumping the version change with new release of Bioconductor +* Added mean call to the heatmap of vizGenes() +* To combineTCR, filteringMulti now checks to remove list elements with 0 cells. +* Removed top_n() call as it is now deprecated, using slice_max() without ties. +* Add arrange() call during parseTCR() to organize the chains +* Correct the gd flip in the combineContig and subsequent functions +* Removed viridis call in the clonalNetwork() function that was leading to errors +* Matched syntax for strict clonotype in combineBCR() +* Added group.by variable to all applicable visualizations +* Added return.boots to clonalDiversity(), allow for export of all bootstrapped values + + +# scRepertoire VERSION 1.5.4 + +* modified grabMeta() internal function to no longer assume the active Identity is clusters. +* checkBlanks() now checks if a blank was detected before trying to remove it +* clonalNetwork() automatically resulted in default error message, bug now removed. +* clonotypeBias now adds z-score of bias when matrix is exported. exportTable parameter is now fixed. + +# scRepertoire VERSION 1.5.3 + +* Added loadContigs for non-10X formatted single-cell data +* removed combineTRUST4, superseded by loadContigs +* combineTCR() now allows for > 3 recovered TCRs per barcode +* Readded the filtering steps to combineTCR(), will detect if data is from 10X and automatically remove nonproductive or multi chains. +* Updated parseTCR() to include evaluation for gamma/delta chains. + +# scRepertoire VERSION 1.5.2 + +* Arbitrarily numbering system to match new bio conductor dev version +* highlightClonotypes() now returns the specific clones instead of clonotype 1, ... +* compareClonotypes numbers parameter now for group-wide numbers and not overall top X numbers +* Fixed issue with clonalDiversity that cause errors when group.by parameter was used. +* modified parseBCR() to reduce complexity and assume lambda >> kappa +* fixed clusterTCR() function broken with Seurat Objects +* checkContigs no ensures data frames and that "" are converted into NAs +* modified makeGenes() internal function changing na.omit to str_replace_na() and separating the BCR calls by chain to prevent combination errors. + +# scRepertoire VERSION 1.3.5 + +* Modified parseBCR() to check for contents of the chains. Resolve issue with placing light chain into heavy chain slots when 2 contigs are present. +* Updated checkBlanks to include NA evaluation and placed the check in all .viz functions +* Added clonalNetwork() function +* Modified diversity visualization to remove outliers and place graphs on a single line +* Modified clonalOverlay() to use new internal getCoord() function like clonalNetwork() +* Added threshold parameter to clonesizeDistribution() +* Added support for single-cell objects to clusterTCR() + + +# scRepertoire VERSION 1.3.4 + +* Modification in clusterTCR() and combineBCR() to speed up the comparison and use less memory +* FilteringMulti, now isolates the top contig by chain, then for barcodes with chains > 2, isolates the top expressing chains. This substantially increases the speed of the filtering step. +* Modified makeGenes() internal function to use strings str_c() +* Added threshold parameter to combineTRUST4 for B cell manipulation +* Changed combineTCR function to prevent cell type mix up and clarified in function documentation. +* vizGenes can now be used to look at other component genes of the receptor and "separate" parameter was replaced by "y.axis" parameter. +* Added clonotypeBias() function for inter-cluster comparison. +* Fixed clusterTCR() and combineBCR() assumption that you will have unrelated clones. + + +# scRepertoire VERSION 1.3.3 + +* CombineBCR() auto naming function updated to actually name the list elements. +* Added createHTOContigList() function to create contain list of multiplexed experiments. Fixed issue with groupBy variable +* Added Inv.Pielou matric to diversity call - this is essentially 1-shannon/ln(length). Due to the bootstrapping the length with be constant. +* Added include.na and split.by to occupiedscRepertoire and changed labeling depending on frequency vs proportion +* Added support for single-cell objects for most visualizations, list organizing by single-cell object can be called using split.by variable +* All group and groupBy parameters are now group.by. + + +# scRepertoire VERSION 1.3.2 + +* This is the new numbering scheme apologies - we are all up-to-date now and now cell ranger >= 5 will # work on bioconductor, so let's all just take that as a win. +* added dot.size parameter to scatterClonotype +* filteringMulti now subsets clonotypes with contains >=2, to prevent 2 of the same chains +* changed how coldata is added to SCE objects using merge instead of union +* Can now add BCR and TCR simultaneously by making large list +* scatter plotting code is not so ugly and allows for user to select dot.size as a variable on the x or y axis +* Removed regressClonotype function - too many dependencies required, adding an additional vignette to go through the process +* Added chain option to visualizations and combineExpression to allow users to facilitate single chains - removed chain option from combineTCR/BCR/TRUST4 (the combined object will have both chains no matter what) +* Added NA filter to combineTCR/BCR/TRUST4 for cell barcodes with only NA values +* Added NA filter to expression2List() for cells with NA clonotypes. +* Updated VizGene to order the genes automatically by highest to lowest variance +* Updated VizGene to pull the correct genes based on selection +* Updated parse method - old version had issue with place V-->J-->D in the TRB/Heavy chains +* Simplified the clonalDiversity() to allow for more options in organizing plot and box plots. +* CombineExpression() adds the groupBy variable to Frequency, allowing for multiple calculations to be saved in the meta data. +* Default color scheme now uses viridis plasma, because it I am on transfusion medicine. + +# scRepertoire VERSION 1.2.2 + +* added the combineTRUST4 function to parse contigs from TUST4 pipeline +* added the filter of contigs by chain in the combineTCR, combineBCR, and combineTRUST4 functions +* no longer require the ID in the combineTCR/BCR/TRUST4 functions +* added jaccard index for overlap analysis +* replaced vizVgene with vizGene - allowing users to look at any gene in the combinedContig object +* Fixed coloring scale on the overlap analysis +* Added regressClonotype function using harmony to remove the clonotype effect on feature space +* allowed occupiedRepertoire to use proportion. +* added scatterClonotype function to Viz.R + +# scRepertoire VERSION 1.2.1 + +* number of changes to the parseTCR/BCR functions to limit assumptions +* Changed grabMeta to include assessment of colnames +* fixed lengthDF handling of single chains with multi chains stored - ; +* Added labels to alluvialClonotype and occupiedClonotype plotting + +# scRepertoire VERSION 1.1.4 + +* replaced hammingCompare with lvCompare to enable superior clonotype calling in combineBCR function. +* added proportion to combineExpression() function so users no longer need to know absolute frequencies when combining the contiguous information. +* added clusterTCR() and clonalOverlay() functions. +* added downsampling to the diversity calculations +* replaced hammingCompare with lvCompare to enable superior clonotype calling in combineBCR function. +* added proportion to combineExpression() function so users no longer need to know absolute frequencies when combining the contiguous information. +* added clusterTCR() and clonalOverlay() functions. +* added downsampling to the diversity calculations +* Clonal Overlap Coefficient issue fixed, was comparing unique barcodes and not clonotypes +* Added function checkBlanks to remove list elements without clonotypes, this prevents errors for visualizations +* Re-added Startrac metrics by stripping down the package and adding it piecemeal +* Heavily modified dependencies to reduce total number + +# scRepertoire VERSION 1.0.0 + +* removed dependencies ggfittext and ggdendrogram +* clonesizeDistribution now returns a plot() function + + +# scRepertoire VERSION 0.99.18 + +* Updated author information in the vignette + + +# scRepertoire VERSION 0.99.17 + +* Updated NEWS formatting +* Edited DESCRIPTION to SingleCellExperiment R package +* Updated information in the vignette + + +# scRepertoire VERSION 0.99.16 + +* Added getCirclize() + + +# scRepertoire VERSION 0.99.15 + +* Modified numerator for index function + + +# scRepertoire VERSION 0.99.14 + +* Removed bracket from indexing function + +#scRepertoire VERSION 0.99.13 + +* Added exportTable to remaining viz functions +* Modified morisita index to correct error + +# scRepertoire VERSION 0.99.12 + +* Reducing the size of the screp_example to fulfill < 5 mB requirement. Randomly samples 100 cells and removed RNA counts from Seurat object + +# scRepertoire VERSION 0.99.11 + +* Updated compareClonotype to allow for clonotype comparisons + +# scRepertoire VERSION 0.99.10 + +* Bioconductor did not detect the version update. + +# scRepertoire VERSION 0.99.9 + +* Bioconductor had no love - changed the Seurat package to imports instead of required, see if that will address the compiling issue that results in a killed: 9 error. + +# scRepertoire VERSION 0.99.8 + +* Passed checks on system, let's see how much bioconductor hates it + +# scRepertoire VERSION 0.99.7 + +* But really this time, changed the colData import + +# scRepertoire VERSION 0.99.6 + +* Changed colData import + +# scRepertoire VERSION 0.99.5 + +* Added screp_example data to package +* Added visVgene function for visualizing the distribution of V genes in TCR +* Added support for monocle to combineExpression function +* Updated documentation for combineTCR() and combineBCR() +* Updated documentation to utilize SingleCellExperiment formats +* Updated Vignette to utilize SingleCellExperiment formats +* Added Author information to vignette +* Add intro and conclusion to vignette +* Removed html knitted vignette +* Removed descriptive code snippets + +# scRepertoire VERSION 0.99.4 + +* Modified expression2List() to allow for variables across meta data + +# scRepertoire VERSION 0.99.1 + +* Changed R (>= 3.6) to R (>= 4.0) + + +# scRepertoire VERSION 0.99.0 + +* Changed DESCRIPTION version to 0.99.0 +* Removed file seurat_example.rda, accidentally committed +* Deleted git attributes +* reduced Seurat object size for alluvialClonotype in vignette +* Changed the alluvialClonotype assessment to account for only 1 condition + + +# scRepertoire VERSION 1.2.3 + +* Changed the access of the sample data to github.io repo: +readRDS(url("https://ncborcherding.github.io/vignettes/scRepertoire_example.rds")) + + +# scRepertoire VERSION 1.2.2 + +* Removed Startrac-based functions in order to pass build on Bioconductor. + +DEPRECATED AND DEFUNCT + +* Deprecate StartracDiversity() + + +# scRepertoire VERSION 1.2.0 + +SIGNIFICANT USER-VISIBLE CHANGES + +* Added support for SingleCellExperiement format. + + +DEPRECATED AND DEFUNCT + +* Deprecate combineSeurat in favor or combineExpression(). +* Deprecate seurat2List in favor of expression2List(). diff --git a/_pkgdown.yml b/_pkgdown.yml index 867709fd..8d79f59e 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -44,9 +44,9 @@ navbar: - text: "Clonal Bias" href: articles/Clonal_Bias.html - text: ------- - - text: "Using Trex for TCRs" + - text: "Combining Deep Learning and TCRs with Trex" href: articles/Trex.html - - text: "Using Ibex for BCRs" + - text: "Combining Deep Learning and BCRs with Ibex" href: articles/Ibex.html - text: "News/Changelog" href: news/index.html diff --git a/inst/pkgdown.yml b/inst/pkgdown.yml index 116d43d4..abc352d5 100644 --- a/inst/pkgdown.yml +++ b/inst/pkgdown.yml @@ -19,7 +19,7 @@ articles: SC_Visualizations: SC_Visualizations.html Trex: Trex.html vignette: vignette.html -last_built: 2023-10-22T17:28Z +last_built: 2023-10-23T17:39Z urls: reference: https://ncborcherding.github.io/scRepertoire/reference article: https://ncborcherding.github.io/scRepertoire/articles diff --git a/vignettes/articles/Ibex.Rmd b/vignettes/articles/Ibex.Rmd index db62e955..e3116551 100644 --- a/vignettes/articles/Ibex.Rmd +++ b/vignettes/articles/Ibex.Rmd @@ -1,11 +1,11 @@ --- -title: "Using Ibex for BCRs" +title: "Combining Deep Learning and BCRs with Ibex" date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' output: rmarkdown::html_vignette theme: united df_print: kable vignette: > - %\VignetteIndexEntry{Using Ibex for BCRs} + %\VignetteIndexEntry{Combining Deep Learning and BCRs with Ibex} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -47,7 +47,7 @@ suppressMessages(library(dplyr)) ``` -The idea behind Ibex is to combine BCR cdr3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using BCR and RNA. Ibex was designed to make a customizable approach to this combined approach using R. +The idea behind Ibex is to combine BCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using BCR and RNA. Ibex was designed to make a customizable approach to this combined approach using R. More infromation is available at the [Ibex GitHub Repo](https://github.com/ncborcherding/Ibex). diff --git a/vignettes/articles/Trex.Rmd b/vignettes/articles/Trex.Rmd index 581cc55b..161efcff 100644 --- a/vignettes/articles/Trex.Rmd +++ b/vignettes/articles/Trex.Rmd @@ -1,11 +1,11 @@ --- -title: "Using Trex for TCRs" +title: "Combining Deep Learning and TCRs with Trex" date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' output: rmarkdown::html_vignette theme: united df_print: kable vignette: > - %\VignetteIndexEntry{Using Trex for TCRs} + %\VignetteIndexEntry{Combining Deep Learning and TCRs with Trex} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -38,7 +38,6 @@ knitr::opts_chunk$set( time_it = TRUE ) -suppressMessages(library(scRepertoire)) suppressMessages(library(Trex)) suppressMessages(library(Seurat)) suppressMessages(library(ggplot2)) @@ -46,34 +45,6 @@ suppressMessages(library(viridis)) suppressMessages(library(dplyr)) suppressMessages(library(reticulate)) use_condaenv(condaenv = "r-reticulate", required = TRUE) - -data("contig_list") -combined.TCR <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) - -googledrive::drive_download("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link", overwrite = TRUE) - -scRep_example <- readRDS("scRep_example_full.rds") -scRep_example <- scRep_example %>% - ScaleData(verbose = FALSE) %>% - FindVariableFeatures(verbose = FALSE) %>% - RunPCA(verbose = FALSE) - - -scRep_example <- combineExpression(combined.TCR, - scRep_example, - cloneCall="gene", - group.by = "sample") - -#Adding patient information -scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) - -#Adding type information -scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) - -#Defining colors -colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) ``` @@ -87,6 +58,13 @@ More infromation is available at the [Trex GitHub Repo](https://github.com/ncbor devtools::install_github("ncborcherding/Trex") ``` +## The Data Set + +To show the multiple options of Trex, the example data is derived from [GSE167118](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167118) - a cohort of CITE-seq data derived from severe COVID-19 patients. The [corresponding manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/) is excellent. The data example built into the package (trex_example) is derived from randomly sampling the single-cells from patient 17. + +```{r} +SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Trex_FullExample.rds")) +``` ## maTrex Function @@ -114,8 +92,7 @@ Trex has 2 major functions - the first being ```maTrex()```, which is the backbo If choosing the geometric transformation, what value of theta to use (default is pi) ```{r} -library(Trex) -Trex_vectors <- maTrex(scRep_example, +Trex_vectors <- maTrex(SeuratObj, chains = "TRA", encoder.model = "VAE", encoder.input = "AF") @@ -124,7 +101,7 @@ ggplot(data = as.data.frame(Trex_vectors), aes(Trex_1, Trex_2)) + geom_point() + theme_classic() -Trex_vectors2 <- maTrex(scRep_example, +Trex_vectors2 <- maTrex(SeuratObj, chains = "TRB", method = "geometric", theta = pi) @@ -139,31 +116,30 @@ ggplot(as.data.frame(Trex_vectors2), aes(x = Trex_1, y = Trex_2)) + Additionally, ```runTrex()``` can be used to append the Seurat or Single-cell Experiment object with the Trex vectors and allow for further analysis. Importantly, ```runTrex()``` will remove single cells that do not have recovered TCR data in the metadata of the object. ```{r} -scRep_example <- runTrex(scRep_example, - chains = "TRB", - encoder.model = "VAE", - encoder.input = "KF", - reduction.name = "Trex.KF") +SeuratObj <- runTrex(SeuratObj, + chains = "TRB", + encoder.model = "VAE", + encoder.input = "KF", + reduction.name = "Trex.KF") ``` - # Using Trex Vectors After ```runTrex()``` we have the encoded values stored under **"Trex..."**. Using the Trex reduction stored in Seurat, we can calculate the nearest neighbor and shared nearest neighbor indexes and generate a UMAP. ```{r} #Generating UMAP from Trex Neighbors -scRep_example <- RunUMAP(scRep_example, - reduction = "Trex.KF", - dims = 1:30, - reduction.name = 'Trex.umap', - reduction.key = 'trexUMAP_') +SeuratObj <- RunUMAP(SeuratObj, + reduction = "Trex.KF", + dims = 1:30, + reduction.name = 'Trex.umap', + reduction.key = 'trexUMAP_') #Trex UMAP -plot1 <- DimPlot(scRep_example, +plot1 <- DimPlot(SeuratObj, reduction = "Trex.umap") + NoLegend() -plot2 <- DimPlot(scRep_example, +plot2 <- DimPlot(SeuratObj, group.by = "CTaa", reduction = "Trex.umap") + scale_color_viridis(discrete = TRUE, option = "B") + @@ -172,29 +148,29 @@ plot2 <- DimPlot(scRep_example, plot1 + plot2 ``` -We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija's group [paper](https://pubmed.ncbi.nlm.nih.gov/34062119/). We will use the RNA and Trex vectors for the WNN calculations. +We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija's group [paper](https://pubmed.ncbi.nlm.nih.gov/34062119/). We will use the RNA, ADT protein levels, and Trex vectors for the WNN calculations. ```{r} -scRep_example <- FindMultiModalNeighbors( - scRep_example, - reduction.list = list("pca", "Trex.KF"), - dims.list = list(1:30, 1:30), +SeuratObj <- FindMultiModalNeighbors( + SeuratObj, + reduction.list = list("pca", "apca", "Trex.KF"), + dims.list = list(1:30, 1:20, 1:30), modality.weight.name = "RNA.weight") -scRep_example <- RunUMAP(scRep_example, +SeuratObj <- RunUMAP(SeuratObj, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_") -scRep_example <- FindClusters(scRep_example, +SeuratObj <- FindClusters(SeuratObj, graph.name = "wsnn", resolution = 0.6, algorithm = 3, verbose = FALSE) #WNN UMAP -plot3 <- DimPlot(scRep_example, +plot3 <- DimPlot(SeuratObj, reduction = "wnn.umap") -plot4 <- DimPlot(scRep_example, +plot4 <- DimPlot(SeuratObj, reduction = "wnn.umap", group.by = "CTaa") + scale_color_viridis(discrete = TRUE, option = "B") + @@ -205,24 +181,30 @@ plot3 + plot4 ## Comparing the outcome to just one modality -We can also look at the differences in the UMAP generated from RNA or Trex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above. +We can also look at the differences in the UMAP generated from RNA, ADT, or Trex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above. ```{r} -scRep_example <- RunUMAP(scRep_example, - reduction = 'pca', - dims = 1:30, - assay = 'RNA', - reduction.name = 'rna.umap', - reduction.key = 'rnaUMAP_') - - -plot5 <- DimPlot(scRep_example, reduction = "rna.umap") + NoLegend() -plot6 <- DimPlot(scRep_example, reduction = "Trex.umap") + NoLegend() - -plot5 + plot6 +SeuratObj <- RunUMAP(SeuratObj, + reduction = 'pca', + dims = 1:30, + assay = 'RNA', + reduction.name = 'rna.umap', + reduction.key = 'rnaUMAP_') + +SeuratObj <- RunUMAP(SeuratObj, + reduction = 'apca', + dims = 1:20, + assay = 'ADT', + reduction.name = 'adt.umap', + reduction.key = 'adtUMAP_') + +plot5 <- DimPlot(SeuratObj, reduction = "rna.umap") + NoLegend() +plot6 <- DimPlot(SeuratObj, reduction = "adt.umap") + NoLegend() +plot7 <- DimPlot(SeuratObj, reduction = "Trex.umap") + NoLegend() + +plot5 + plot6 + plot7 ``` - # CoNGA Reduction Recent [work](https://pubmed.ncbi.nlm.nih.gov/34426704/) has proposed using representative cells for the characterization of clonotype and gene expression relationships. In order to generate these representative cells, either a mean expression across a clone or using the PCA dimensional space to identify a single cell that has the minimum euclidean distance across a clone. @@ -230,7 +212,9 @@ Recent [work](https://pubmed.ncbi.nlm.nih.gov/34426704/) has proposed using repr In order to generate a single-cell object based on the CoNGA approach, Trex offers the function ```CoNGAfy()```. For **method**, select either "mean" or "dist" as described above. After performing ```CoNGAfy()```, the user can use any of the above reduction strategies. ```{r} -CoNGA.seurat <- CoNGAfy(scRep_example, +library(dplyr, include.only = c("%>%")) + +CoNGA.seurat <- CoNGAfy(SeuratObj, method = "dist") CoNGA.seurat <- runTrex(CoNGA.seurat, @@ -244,10 +228,10 @@ CoNGA.seurat <- CoNGA.seurat %>% FindClusters(algorithm = 3) CoNGA.seurat <- RunUMAP(CoNGA.seurat, - reduction = "Trex.KF", - dims = 1:20, - reduction.name = 'Trex.umap', - reduction.key = 'trexUMAP_') + reduction = "Trex.KF", + dims = 1:20, + reduction.name = 'Trex.umap', + reduction.key = 'trexUMAP_') DimPlot(CoNGA.seurat, reduction = "Trex.umap") + NoLegend() ``` @@ -258,7 +242,7 @@ Towards find epitope relationships, Trex has a built in data base of TCRA and TC The database is collated from multiple sources and represents a great deal of effort from these sources: -1. [VDJdb](https://vdjdb.cdr3.net/) - [citation](https://pubmed.ncbi.nlm.nih.gov/28977646/) +1. [VDJdb](https://vdjdb.CDR3.net/) - [citation](https://pubmed.ncbi.nlm.nih.gov/28977646/) 2. [McPAS-TCR](http://friedmanlab.weizmann.ac.il/McPAS-TCR/) - [citation](https://pubmed.ncbi.nlm.nih.gov/28481982/) 3. [iedb](https://www.iedb.org/) - [citation](https://pubmed.ncbi.nlm.nih.gov/30357391/) 4. [TBA](https://db.cngb.org/pird/home/) (now called PIRD) - [citation](https://pubmed.ncbi.nlm.nih.gov/31373607/) @@ -277,15 +261,16 @@ DimPlot(CoNGA.seurat, group.by = "TRB_Epitope.sequence") ``` -Or ```annotateDB()``` can be used on the full single-cell object and examine the sequence information along the RNA-based UMAP. An added feature to the function allows the annotations to be extended to cdr3 sequences that are within defined edit distances from the reference using **edit.distance**. +Or ```annotateDB()``` can be used on the full single-cell object and examine the sequence information along the RNA-based UMAP. An added feature to the function allows the annotations to be extended to CDR3 sequences that are within defined edit distances from the reference using **edit.distance**. ```{r} -scRep_example <- annotateDB(scRep_example, +SeuratObj <- annotateDB(SeuratObj, chains = "TRB", edit.distance = 2) -DimPlot(scRep_example, +DimPlot(SeuratObj, reduction = "rna.umap", group.by = "TRB_Epitope.species") ``` + From cb44c552307ef5dd982e59d65cdef80fbe62706e Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 23 Oct 2023 15:49:27 -0500 Subject: [PATCH 098/142] Update _pkgdown.yml --- _pkgdown.yml | 126 +++++++++++++++++++++++++-------------------------- 1 file changed, 62 insertions(+), 64 deletions(-) diff --git a/_pkgdown.yml b/_pkgdown.yml index 8d79f59e..e7e70e77 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,63 +1,65 @@ url: https://ncborcherding.github.io/scRepertoire/ Template: params: - bootswatch: flatly - + bootswatch: flatly home: sidebar: - structure: [links, license, custom, authors, dev] + structure: + - links + - license + - custom + - authors + - dev components: custom: title: Citation - text: "[Citing scRepertoire](https://f1000research.com/articles/9-47)" - + text: '[Citing scRepertoire](https://f1000research.com/articles/9-47)' navbar: left: - - text: "Getting Started" - href: index.html - - text: "Install" - href: articles/Installation.html - - text: "Vignettes" - menu: - - text: "Loading Data" - href: articles/Loading.html - - text: "Combining Contigs into Clones" - href: articles/Combining_Contigs.html - - text: "Additional Processing" - href: articles/Processing.html - - text: ------- - - text: "Basic Clonal Analysis" - href: articles/Clonal_Visualizations.html - - text: "Visualizing Clonal Dynamics" - href: articles/Clonal_Dynamics.html - - text: "Summarizing Repertoires" - href: articles/Repertoire_Summary.html - - text: "Clonal Diversity and Overlap" - href: articles/Clonal_Diversity.html - - text: "Clustering Sequences by Edit Distance" - href: articles/Clonal_Cluster.html - - text: ------- - - text: "Combining Clones and Single-Cell Objects" - href: articles/Attaching_SC.html - - text: "Visualizations for Single-Cell Objects" - href: articles/SC_Visualizations.html - - text: "Clonal Bias" - href: articles/Clonal_Bias.html - - text: ------- - - text: "Combining Deep Learning and TCRs with Trex" - href: articles/Trex.html - - text: "Combining Deep Learning and BCRs with Ibex" - href: articles/Ibex.html - - text: "News/Changelog" - href: news/index.html - - text: "Reference" - href: reference/index.html - - text: "FAQ" - href: articles/FAQ.html - + - text: Getting Started + href: index.html + - text: Install + href: articles/Installation.html + - text: Vignettes + menu: + - text: Loading Data + href: articles/Loading.html + - text: Combining Contigs into Clones + href: articles/Combining_Contigs.html + - text: Additional Processing + href: articles/Processing.html + - text: '-------' + - text: Basic Clonal Analysis + href: articles/Clonal_Visualizations.html + - text: Visualizing Clonal Dynamics + href: articles/Clonal_Dynamics.html + - text: Summarizing Repertoires + href: articles/Repertoire_Summary.html + - text: Clonal Diversity and Overlap + href: articles/Clonal_Diversity.html + - text: Clustering Sequences by Edit Distance + href: articles/Clonal_Cluster.html + - text: '-------' + - text: Combining Clones and Single-Cell Objects + href: articles/Attaching_SC.html + - text: Visualizations for Single-Cell Objects + href: articles/SC_Visualizations.html + - text: Clonal Bias + href: articles/Clonal_Bias.html + - text: '-------' + - text: Combining Deep Learning and TCRs with Trex + href: articles/Trex.html + - text: Combining Deep Learning and BCRs with Ibex + href: articles/Ibex.html + - text: News/Changelog + href: news/index.html + - text: Reference + href: reference/index.html + - text: FAQ + href: articles/FAQ.html reference: -- title: "Loading and Processing Contigs" - desc: "Functions that load, combine, and process the single-cell contig information." +- title: Loading and Processing Contigs + desc: Functions that load, combine, and process the single-cell contig information. - contents: - addVariable - combineBCR @@ -66,13 +68,12 @@ reference: - exportClones - loadContigs - subsetClones - -- title: "Visualizing Clones" - desc: "Functions plotting clonal data." +- title: Visualizing Clones + desc: Functions plotting clonal data. - contents: - clonalAbundance - clonalCluster - - clonalCompare + - clonalCompare - clonalDiversity - clonalHomeostasis - clonalLength @@ -83,17 +84,15 @@ reference: - clonalScatter - clonalSizeDistribution - vizGenes - -- title: "Summarizing Repertoire" - desc: "Functions to summarize clonal sequences across the repertoire" +- title: Summarizing Repertoire + desc: Functions to summarize clonal sequences across the repertoire - contents: - percentAA - percentGenes - percentKmer - percentVJ - -- title: "Single-Cell Object Processing and Visualizations" - desc: "Functions to add or visualize clonal information along a single-cell object" +- title: Single-Cell Object Processing and Visualizations + desc: Functions to add or visualize clonal information along a single-cell object - contents: - alluvialClones - clonalBias @@ -104,13 +103,12 @@ reference: - getCirclize - highlightClones - StartracDiversity - -- title: "Data" - desc: "Reference data for package functions." +- title: Data + desc: Reference data for package functions. - contents: - contig_list - mini_contig_list - scRep_example - deploy: - install_metadata: true \ No newline at end of file + install_metadata: yes + From 4c61b43adea7acf73644fe951297953abb9079af Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 24 Oct 2023 13:25:47 -0500 Subject: [PATCH 099/142] Update README.md --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 78aa6fcf..7b586281 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ ## A toolkit for single-cell immune profiling - + ### Introduction Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine RNA and immune profiling, scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. @@ -16,6 +16,10 @@ Single-cell sequencing is an emerging technology in the field of immunology and ### Applying Deep Learning to VDJ data scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/ncborcherding/Trex) for deep-learning-based autoencoding of the T cell receptor and [Ibex](https://github.com/ncborcherding/Ibex) for the B cell receptor. +## Working with scRepertoire + +scRepertoire has a comprehensive [website](https://www.borch.dev/uploads/screpertoire/) for detialed tutorials and function information. + ## Installation #### Installation of Master Branch @@ -42,10 +46,6 @@ BiocManager::install("scRepertoire") Unfortunately, Github limits the size of individual files. In order to access the seurat object paired with scRepertoire please download the .rda from [here](https://drive.google.com/file/d/1Iv6t2BScpnLLrFWaWFUGwne3XzRAwMOc/view?usp=share_link). -## Learning To Use scRepertoire - -Vignette available [here](https://www.borch.dev/uploads/vignette/scRepertoire), includes almost 13,000 T cells across 4 patients with acute respiratory distress syndrome. - ## Please Cite Feel free to use, edit, modify scRepertoire, but if you do, please cite the [manuscript](https://f1000research.com/articles/9-47/v1). If you are building your own tool based on scRepertoire, reach out, I am happy to help and make things compatible. From 25a80952574d75385fdcaa867ff6857627837612 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 24 Oct 2023 13:27:31 -0500 Subject: [PATCH 100/142] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7b586281..de04960a 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ ## A toolkit for single-cell immune profiling - + ### Introduction Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine RNA and immune profiling, scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat R package. From 46729b97febb8c9f5a56537a175377dab5559f41 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 25 Oct 2023 18:59:56 -0500 Subject: [PATCH 101/142] updated syntax --- NEWS | 4 + NEWS.html | 895 ------------------- NEWS.md | 5 +- R/addVariable.R | 24 +- R/alluvialClones.R | 14 +- R/clonalAbundance.R | 42 +- R/clonalBias.R | 26 +- R/clonalCluster.R | 36 +- R/clonalCompare.R | 24 +- R/clonalDiversity.R | 58 +- R/clonalHomeostasis.R | 34 +- R/clonalLength.R | 33 +- R/clonalNetwork.R | 18 +- R/clonalOccupy.R | 12 +- R/clonalOverlap.R | 49 +- R/clonalOverlay.R | 10 +- R/clonalProportion.R | 36 +- R/clonalQuant.R | 41 +- R/clonalRarefaction.R | 35 +- R/clonalScatter.R | 25 +- R/clonalSizeDistribution.R | 35 +- R/combineContigs.R | 74 +- R/combineExpression.R | 73 +- R/createHTOContigList.R | 14 +- R/data.R | 24 +- R/exportClones.R | 22 +- R/getCircilize.R | 13 +- R/highlightClones.R | 30 +- R/loadContigs.R | 15 +- R/percentAA.R | 32 +- R/percentGenes.R | 16 +- R/percentKmer.R | 39 +- R/percentVJ.R | 12 +- R/startracDiversity.R | 16 +- R/subsetClone.R | 23 +- R/utils.R | 16 +- R/vizGenes.R | 49 +- _pkgdown.yml | 8 +- inst/pkgdown.yml | 8 +- man/StartracDiversity.Rd | 11 +- man/addVariable.Rd | 14 +- man/alluvialClones.Rd | 8 +- man/clonalAbundance.Rd | 18 +- man/clonalBias.Rd | 14 +- man/clonalCluster.Rd | 6 +- man/clonalCompare.Rd | 12 +- man/clonalDiversity.Rd | 26 +- man/clonalHomeostasis.Rd | 10 +- man/clonalLength.Rd | 6 +- man/clonalNetwork.Rd | 8 +- man/clonalOccupy.Rd | 8 +- man/clonalOverlap.Rd | 24 +- man/clonalOverlay.Rd | 4 +- man/clonalProportion.Rd | 10 +- man/clonalQuant.Rd | 8 +- man/clonalRarefaction.Rd | 18 +- man/clonalScatter.Rd | 10 +- man/clonalSizeDistribution.Rd | 8 +- man/combineBCR.Rd | 10 +- man/combineExpression.Rd | 30 +- man/combineTCR.Rd | 8 +- man/contig_list.Rd | 9 +- man/createHTOContigList.Rd | 10 +- man/exportClones.Rd | 8 +- man/getCirclize.Rd | 9 +- man/highlightClones.Rd | 16 +- man/loadContigs.Rd | 7 +- man/mini_contig_list.Rd | 6 +- man/percentAA.Rd | 10 +- man/percentGenes.Rd | 8 +- man/percentKmer.Rd | 8 +- man/percentVJ.Rd | 4 +- man/screp_example.Rd | 9 +- man/subsetClones.Rd | 7 +- man/vizGenes.Rd | 4 +- vignettes/articles/Attaching_SC.Rmd | 8 +- vignettes/articles/Clonal_Cluster.Rmd | 10 +- vignettes/articles/Clonal_Diversity.Rmd | 79 +- vignettes/articles/Clonal_Rarefaction.Rmd | 120 --- vignettes/articles/Clonal_Visualizations.Rmd | 34 +- vignettes/articles/Combining_Contigs.Rmd | 4 +- vignettes/articles/FAQ.Rmd | 43 +- vignettes/articles/Installation.Rmd | 8 +- vignettes/articles/Loading.Rmd | 8 +- vignettes/articles/Processing.Rmd | 14 +- vignettes/articles/Repertoire_Summary.Rmd | 4 +- vignettes/vignette.Rmd | 6 +- 87 files changed, 885 insertions(+), 1779 deletions(-) delete mode 100644 NEWS.html delete mode 100644 vignettes/articles/Clonal_Rarefaction.Rmd diff --git a/NEWS b/NEWS index fc0c409d..15f40387 100644 --- a/NEWS +++ b/NEWS @@ -20,6 +20,9 @@ NEW FEATURES UNDERLYING CHANGES +* .theCall now allows for a custom header/variable and checks the colnames. +* Replaced data arguments to be more descriptive: df is now input.data, dir is now input, and sc is now sc.data +* Deep clean on the documentation for each function for increased consistency and explainability * startrac diversity metric re-implemented to remove startrac-class object intermediary * implemented powerTCR locally to reduce dependencies and continue support * Universalized underlying function language and intermediate variables @@ -27,6 +30,7 @@ UNDERLYING CHANGES * License change to MIT * group.by and split.by have been consolidated into single group.by parameter * Added support for .json, Omniscope, and MiXCR formats +* Made GitHub.io website for support/vignettes/FAQ DEPRECATED AND DEFUNCT diff --git a/NEWS.html b/NEWS.html deleted file mode 100644 index 95035920..00000000 --- a/NEWS.html +++ /dev/null @@ -1,895 +0,0 @@ - - - - - - - - - - - - - -NEWS - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
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    scRepertoire VERSION 2.0.0 (2023-10-23)

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    scRepertoire VERSION 1.11.0

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    scRepertoire VERSION 1.7.5

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    scRepertoire VERSION 1.7.4

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    UNDERLYING CHANGES

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    scRepertoire VERSION 1.7.3

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    scRepertoire VERSION 1.7.2

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    scRepertoire VERSION 1.7.0

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    scRepertoire VERSION 1.5.4

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    scRepertoire VERSION 1.5.3

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    scRepertoire VERSION 1.5.2

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    scRepertoire VERSION 1.3.5

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    scRepertoire VERSION 1.3.4

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    scRepertoire VERSION 1.3.3

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    scRepertoire VERSION 1.3.2

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    scRepertoire VERSION 1.2.2

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    scRepertoire VERSION 1.2.1

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    scRepertoire VERSION 0.99.18

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    - - - - - - - - - - - - - - - diff --git a/NEWS.md b/NEWS.md index d223cbb8..53509ae1 100644 --- a/NEWS.md +++ b/NEWS.md @@ -21,13 +21,16 @@ ## UNDERLYING CHANGES +* .theCall now allows for a custom header/variable and checks the colnames. +* Replaced data arguments to be more descriptive: *df* is now *input.data*, *dir* is now *input*, and *sc* is now *sc.data* +* Deep clean on the documentation for each function for increased consistency and explainability * startrac diversity metric re-implemented to remove startrac-class object intermediary * implemented powerTCR locally to reduce dependencies and continue support * Universalized underlying function language and intermediate variables * License change to MIT * group.by and split.by have been consolidated into single group.by parameter * Added support for .json, Omniscope, and MiXCR formats for ```loadContigs()``` -* Made GitHub.io webstie for support/vignettes/FAQ +* Made GitHub.io website for support/vignettes/FAQ * Restructured NEWS Tracking ## DEPRECATED AND DEFUNCT diff --git a/R/addVariable.R b/R/addVariable.R index 7237d671..004af001 100644 --- a/R/addVariable.R +++ b/R/addVariable.R @@ -8,24 +8,26 @@ #' #' @examples #' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", +#' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' combined <- addVariable(combined, -#' name = "Type", -#' variables = rep(c("B", "L"), 4)) +#' variable.name = "Type", +#' variables = rep(c("B", "L"), 4)) #' -#' @param df The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. -#' @param name The column header to add. +#' @param input.data The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. +#' @param variable.name The column header to add. #' @param variables The exact values to add to each element of the list. #' @export #' @concept Loading_and_Processing_Contigs -#' @return list of contigs with a new column (name). -addVariable <- function(df, name = NULL, variables = NULL) { - if (length(df) != length(variables)) { +#' @return input.data list with the variable column added to each element. +addVariable <- function(input.data, + variable.name = NULL, + variables = NULL) { + if (length(input.data) != length(variables)) { stop("Make sure the variables match the length of the contig list") } - for (i in seq_along(df)) { - df[[i]][,name] <- variables[i] + for (i in seq_along(input.data)) { + input.data[[i]][,variable.name] <- variables[i] } - return(df) + return(input.data) } \ No newline at end of file diff --git a/R/alluvialClones.R b/R/alluvialClones.R index 685e1316..375d6079 100644 --- a/R/alluvialClones.R +++ b/R/alluvialClones.R @@ -45,10 +45,10 @@ #' y.axes = c("Patient", "ident"), #' color = "ident") #' -#' @param sc The single-cell object to visualize after \code{\link{combineExpression}}. +#' @param sc.data The single-cell object to visualize after \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt) or CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param y.axes The columns that will separate the proportional . @@ -67,7 +67,7 @@ #' @export #' @concept SC_Functions #' @return Alluvial ggplot comparing clonotype distribution. -alluvialClones <- function(sc, +alluvialClones <- function(sc.data, cloneCall = "strict", chain = "both", y.axes = NULL, @@ -78,12 +78,12 @@ alluvialClones <- function(sc, palette = "inferno") { x <- alluvium <- stratum <- NULL - .checkSingleObject(sc) - cloneCall <- .theCall(cloneCall) + .checkSingleObject(sc.data) + cloneCall <- .theCall(.grabMeta(sc.data), cloneCall) if (length(y.axes) == 0) { stop("Make sure you have selected the variable(s) to visualize") } - meta <- .grabMeta(sc) + meta <- .grabMeta(sc.data) if (chain != "both") { meta <- .off.the.chain(meta, chain, cloneCall) } diff --git a/R/clonalAbundance.R b/R/clonalAbundance.R index 93e0267d..05d5b6a5 100644 --- a/R/clonalAbundance.R +++ b/R/clonalAbundance.R @@ -1,24 +1,26 @@ #' Demonstrate the relative abundance of clonotypes by group or sample. #' -#' Displays the number of clonotypes at specific requencies by sample +#' Displays the number of clonotypes at specific Frequencies by sample #' or group. Visualization can either be a line graph using -#' calculated numbers or if scale = TRUE, the output will be a +#' calculated numbers or if \strong{scale} = TRUE, the output will be a #' density plot. Multiple sequencing runs can be group together #' using the group parameter. If a matrix output for the data is -#' preferred, set exportTable = TRUE. +#' preferred, set \strong{exportTable} = TRUE. #' #' @examples #' #Making combined contig data #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' clonalAbundance(combined, cloneCall = "gene", scale = FALSE) +#' clonalAbundance(combined, +#' cloneCall = "gene", +#' scale = FALSE) #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by The variable to use for grouping @@ -33,7 +35,7 @@ #' @concept Visualizing_Clones #' @return ggplot of the total or relative abundance of clonotypes #' across quanta -clonalAbundance <- function(df, +clonalAbundance <- function(input.data, cloneCall = "strict", chain = "both", scale=FALSE, @@ -41,17 +43,19 @@ clonalAbundance <- function(df, order = TRUE, exportTable = FALSE, palette = "inferno") { - df <- .list.input.return(df,group.by) Con.df <- NULL xlab <- "Abundance" - cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, group.by, cloneCall, chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) - names <- names(df) + names <- names(input.data) if (!is.null(group.by)) { - for (i in seq_along(df)) { - data1 <- .parseContigs(df, i, names, cloneCall) - label <- df[[i]][1,group.by] + for (i in seq_along(input.data)) { + data1 <- .parseContigs(input.data, i, names, cloneCall) + label <- input.data[[i]][1,group.by] data1[,paste(group.by)] <- label Con.df<- rbind.data.frame(Con.df, data1) } Con.df <- data.frame(Con.df) @@ -73,13 +77,13 @@ clonalAbundance <- function(df, labs(color = fill) } } else { - for (i in seq_along(df)) { - data1 <- .parseContigs(df, i, names, cloneCall) + for (i in seq_along(input.data)) { + data1 <- .parseContigs(input.data, i, names, cloneCall) Con.df<- rbind.data.frame(Con.df, data1) - } + } Con.df <- data.frame(Con.df) if(order) { - Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) + Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(input.data)) } col <- length(unique(Con.df$values)) fill <- "Samples" diff --git a/R/clonalBias.R b/R/clonalBias.R index 1cf269c2..3cde5317 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -1,9 +1,9 @@ #' Examine clonotype bias #' #' The metric seeks to quantify how individual clones are skewed towards -#' a specific cellular compartment or cluster. A clonotype bias of *1* - +#' a specific cellular compartment or cluster. A clonotype bias of \emph{1}* - #' indicates that a clonotype is composed of cells from a single -#' compartment or cluster, while a clonotype bias of *0* - matches the +#' compartment or cluster, while a clonotype bias of \emph{0} - matches the #' background subtype distribution. Please read and cite the following #' \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} #' if using \code{\link{clonalBias}}. @@ -30,10 +30,10 @@ #' min.expand = 2) #' #' -#' @param df The product of \code{\link{combineExpression}}. +#' @param sc.data The single-cell object after \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param group.by The variable to use for calculating bias #' @param split.by The variable to use for calculating the baseline frequencies. #' For example, "Type" for lung vs peripheral blood comparison @@ -45,8 +45,8 @@ #' @importFrom stringr str_sort #' @export #' @concept SC_Functions -#' @return Returns ggplot of the clonotype bias -clonalBias <- function(df, +#' @return ggplot scatter plot with clonotype bias +clonalBias <- function(sc.data, cloneCall="strict", split.by=NULL, group.by=NULL, @@ -54,10 +54,10 @@ clonalBias <- function(df, min.expand=10, exportTable = FALSE, palette = "inferno") { - .checkSingleObject(df) - cloneCall <- .theCall(cloneCall) + .checkSingleObject(sc.data) + cloneCall <- .theCall(.grabMeta(sc.data), cloneCall) #Calculating bias - bias <- .get_clono_bias(df, + bias <- .get_clono_bias(sc.data, split.by = split.by, group.by = group.by , cloneCall=cloneCall, @@ -65,7 +65,7 @@ clonalBias <- function(df, df_shuffle.list <- list() #Bootstrapping for (ii in seq_len(n.boots)) { - df_shuffle.list[[ii]] <- .get_clono_bias(df, split.by = split.by, + df_shuffle.list[[ii]] <- .get_clono_bias(sc.data, split.by = split.by, group.by = group.by, cloneCall=cloneCall, min.expand=min.expand, @@ -93,7 +93,7 @@ clonalBias <- function(df, } #Attaching the cloneSize of original combineExpression() - meta <- .grabMeta(df) + meta <- .grabMeta(sc.data) meta <- meta[meta[,cloneCall] %in% bias[,"Clone"],] meta <- unique(meta[,c(cloneCall, split.by, "cloneSize")]) @@ -178,7 +178,7 @@ clonalBias <- function(df, df <- list("Object" = .grabMeta(df)) } } - cloneCall <- .theCall(cloneCall) + cloneCall <- .theCall(df, cloneCall) df <- .checkBlanks(df, cloneCall) for (s in names(bg)) { diff --git a/R/clonalCluster.R b/R/clonalCluster.R index 9cbec399..63096914 100644 --- a/R/clonalCluster.R +++ b/R/clonalCluster.R @@ -4,7 +4,7 @@ #' sequences of the CDR3 and V genes to cluster similar TCR/BCRs together. #' As a default, the function takes the input from \code{\link{combineTCR}}, #' \code{\link{combineBCR}} or \code{\link{combineExpression}} and amends a -#' cluster to the data frame or meta data. If **exportGraph** is set to TRUE, +#' cluster to the data frame or meta data. If \strong{exportGraph} is set to TRUE, #' the function returns an igraph object of the connected sequences. #' #' @examples @@ -15,7 +15,7 @@ #' #' sub_combined <- clonalCluster(combined[[2]], chain = "TRA", sequence = "aa") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} #' or \code{\link{combineExpression}}. #' @param chain Indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". @@ -39,7 +39,7 @@ #' @return Either amended input with edit-distanced clusters added #' or igraph object of connect sequences -clonalCluster <- function(df, +clonalCluster <- function(input.data, chain = "TRB", sequence = "aa", samples = NULL, @@ -51,16 +51,16 @@ clonalCluster <- function(df, stop("Please select an individual chain for clustering") } #Prepping any single-cell object - is.list <- inherits(df, "list") - if (inherits(x = df, what = c("Seurat", "SummarizedExperiment"))) { - dat <- .grabMeta(df) + is.list <- inherits(input.data, "list") + if (inherits(x = input.data, what = c("Seurat", "SummarizedExperiment"))) { + dat <- .grabMeta(input.data) } else { - dat <- df + dat <- input.data } dat <- .checkList(dat) dat <- .data.wrangle(dat, group.by, "CTgene", chain) - if (inherits(x = df, what = c("Seurat", "SummarizedExperiment"))) { + if (inherits(x = input.data, what = c("Seurat", "SummarizedExperiment"))) { for (y in seq_along(dat)) { cdr3_aa <- str_split(dat[[y]]$CTaa, "_", simplify = TRUE) cdr3_nt <- str_split(dat[[y]]$CTnt, "_", simplify = TRUE) @@ -97,14 +97,14 @@ clonalCluster <- function(df, graph.variables <- bound %>% group_by(bound[,ref2]) %>% - summarize(sample_count = n(), + dplyr::summarize(sample_count = n(), unique_samples = paste0(unique(group.by), collapse = ",")) } else { bound <- bind_rows(dat) graph.variables <- bound %>% group_by(bound[,ref2]) %>% - summarize(sample_count = n()) + dplyr::summarize(sample_count = n()) } #Generating Connected Component @@ -137,23 +137,23 @@ clonalCluster <- function(df, bound <- suppressMessages(join(tmp, output2, match = "first")) #Adding to potential single-cell object - if(inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { + if(inherits(x=input.data, what ="Seurat") | inherits(x=input.data, what ="SummarizedExperiment")) { PreMeta <- bound x <- colnames(PreMeta)[ncol(PreMeta)] PreMeta <- as.data.frame(PreMeta[,x], row.names = rownames(bound)) colnames(PreMeta) <- x - if (inherits(x=df, what ="Seurat")) { + if (inherits(x=input.data, what ="Seurat")) { col.name <- names(PreMeta) %||% colnames(PreMeta) - df[[col.name]] <- PreMeta + input.data[[col.name]] <- PreMeta } else { - rownames <- rownames(colData(df)) - colData(df) <- cbind(colData(df), PreMeta[rownames,])[, union(colnames(colData(df)), colnames(PreMeta))] - rownames(colData(df)) <- rownames + rownames <- rownames(colData(input.data)) + colData(input.data) <- cbind(colData(input.data), PreMeta[rownames,])[, union(colnames(colData(input.data)), colnames(PreMeta))] + rownames(colData(input.data)) <- rownames } } else { - df <- bound + input.data <- bound } - return(df) + return(input.data) } diff --git a/R/clonalCompare.R b/R/clonalCompare.R index f6b766b1..ad493a49 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -16,11 +16,11 @@ #' samples = c("P17B", "P17L"), #' cloneCall="aa") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param samples The specific samples to isolate for visualization. @@ -42,7 +42,7 @@ #' @concept Visualizing_Clones #' @return ggplot of the proportion of total sequencing read of #' selecting clones -clonalCompare <- function(df, +clonalCompare <- function(input.data, cloneCall = "strict", chain = "both", samples = NULL, @@ -59,19 +59,23 @@ clonalCompare <- function(df, if(!is.null(top.clones) & !is.null(clones)) { top.clones <- NULL } - cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, group.by, cloneCall, chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + Con.df <- NULL #Loop through the list to get a proportional summary - for (i in seq_along(df)) { + for (i in seq_along(input.data)) { if (chain != "both") { - df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) + input.data[[i]] <- .off.the.chain(input.data[[i]], chain, cloneCall) } - tbl <- as.data.frame(table(df[[i]][,cloneCall])) + tbl <- as.data.frame(table(input.data[[i]][,cloneCall])) tbl[,2] <- tbl[,2]/sum(tbl[,2]) colnames(tbl) <- c("clones", "Proportion") - tbl$Sample <- names(df[i]) + tbl$Sample <- names(input.data[i]) Con.df <- rbind.data.frame(Con.df, tbl) } diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index e1477c73..39c1d80b 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -1,8 +1,8 @@ #' Examine the clonal diversity of samples #' -#' This function calculates traditional measures of diversity - **Shannon**, -#' **inverse Simpson**, **normalized entropy**, **Gini-Simpson**, **Chao1 index**, and -#' **abundance-based coverage estimators (ACE)** measure of species evenness by sample or group. +#' This function calculates traditional measures of diversity - \strong{Shannon}, +#' \strong{inverse Simpson}, \strong{normalized entropy}, \strong{Gini-Simpson}, \strong{Chao1 index}, and +#' \strong{abundance-based coverage estimators (ACE)} measure of species evenness by sample or group. #' The function automatically down samples the diversity metrics using #' 100 boot straps The group parameter can be used to condense the individual #' samples. If a matrix output for the data is preferred, set exportTable = TRUE. @@ -10,22 +10,22 @@ #' @details #' The formulas for the indices and estimators are as follows: #' -#' Shannon Index: +#' \strong{Shannon Index:} #' \deqn{H = - \sum p_i \cdot \log(p_i)} #' -#' Inverse Simpson Index: +#' \strong{Inverse Simpson Index:} #' \deqn{ D^{-1} = 1 / \sum p_i^2} #' -#' Normalized Entropy: +#' \strong{Normalized Entropy:} #' \deqn{E^H = H / \log(S)} #' -#' Gini-Simpson Index: +#' \strong{Gini-Simpson Index:} #' \deqn{1 - D = 1 - \sum p_i^2} #' -#' Chao1 Index: +#' \strong{Chao1 Index:} #' \deqn{\hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} #' -#' Abundance-based Coverage Estimator (ACE): +#' \strong{Abundance-based Coverage Estimator (ACE):} #' \deqn{\hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} #' #' Where: @@ -43,11 +43,11 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalDiversity(combined, cloneCall = "gene") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param group.by Variable in which to group the diversity calculation. @@ -70,7 +70,7 @@ #' @concept Visualizing_Clones #' @return ggplot of the diversity of clones by group #' @author Andrew Malone, Nick Borcherding -clonalDiversity <- function(df, +clonalDiversity <- function(input.data, cloneCall = "strict", chain = "both", group.by = NULL, @@ -84,19 +84,23 @@ clonalDiversity <- function(df, if(return.boots) { exportTable <- TRUE } - cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, group.by, cloneCall, chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + mat <- NULL sample <- c() if (!is.null(group.by) || !is.null(x.axis)) { - df <- bind_rows(df, .id = "element.names") - df$group.element <- paste0(df[,group.by], ".", df[,x.axis]) - #group.element.uniq <- unique(df$group.element) - df <- split(df, f = df[,"group.element"]) + input.data <- bind_rows(input.data, .id = "element.names") + input.data$group.element <- paste0(input.data[,group.by], ".", input.data[,x.axis]) + #group.element.uniq <- unique(input.data$group.element) + input.data <- split(input.data, f = input.data[,"group.element"]) } - min <- .short.check(df, cloneCall) - for (i in seq_along(df)) { - data <- as.data.frame(table(df[[i]][,cloneCall])) + min <- .short.check(input.data, cloneCall) + for (i in seq_along(input.data)) { + data <- as.data.frame(table(input.data[[i]][,cloneCall])) mat_a <- NULL sample <- c() if(skip.boots == TRUE) { @@ -114,7 +118,7 @@ clonalDiversity <- function(df, mat_a[is.na(mat_a)] <- 0 if(return.boots) { mat_a <- as.data.frame(mat_a) - mat_a$sample <- names(df)[i] + mat_a$sample <- names(input.data)[i] mat <- rbind(mat, mat_a) } else { mat_b<- colMeans(mat_a) @@ -126,13 +130,13 @@ clonalDiversity <- function(df, colnames(mat) <- c("shannon", "inv.simpson", "norm.entropy", "gini.simpson", "chao1", "ACE") mat <- mat[,colnames(mat) %in% metrics] if (!is.null(group.by)) { - mat[,group.by] <- str_split(names(df), "[.]", simplify = TRUE)[,1] + mat[,group.by] <- str_split(names(input.data), "[.]", simplify = TRUE)[,1] } else { group.by <- "Group" - mat[,group.by] <- names(df) + mat[,group.by] <- names(input.data) } if (!is.null(x.axis)) { - mat[,x.axis] <- str_split(names(df), "[.]", simplify = TRUE)[,2] + mat[,x.axis] <- str_split(names(input.data), "[.]", simplify = TRUE)[,2] } else { x.axis <- "x.axis" mat[,x.axis] <- 1 @@ -140,7 +144,7 @@ clonalDiversity <- function(df, if (exportTable) { return(mat) } - rownames(mat) <- names(df) + rownames(mat) <- names(input.data) mat_melt <- suppressMessages(melt(mat, id.vars = c(group.by, x.axis))) values <- str_sort(as.character(unique(mat_melt[,group.by])), diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 725a5b2a..4dc7d51d 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -5,7 +5,7 @@ #' at default, cloneSize will group the clonotypes into bins of Rare = 0 #' to 0.0001, Small = 0.0001 to 0.001, etc. To adjust the proportions, #' change the number or labeling of the cloneSize paramter. If a matrix -#' output for the data is preferred, set exportTable = TRUE. +#' output for the data is preferred, set \strong{exportTable} = TRUE. #' #' @examples #' #Making combined contig data @@ -14,12 +14,12 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalHomeostasis(combined, cloneCall = "gene") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneSize The cutpoints of the proportions. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by The variable to use for grouping. @@ -33,7 +33,7 @@ #' @export #' @concept Visualizing_Clones #' @return ggplot of the space occupied by the specific proportion of clonotypes -clonalHomeostasis <- function(df, +clonalHomeostasis <- function(input.data, cloneSize = c(Rare = .0001, Small = .001, Medium = .01, Large = .1, Hyperexpanded = 1), cloneCall = "strict", @@ -42,26 +42,28 @@ clonalHomeostasis <- function(df, exportTable = FALSE, palette = "inferno") { cloneSize <- c(None = 0, cloneSize) - - cloneCall <- .theCall(cloneCall) - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, cloneCall, chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + sco <- is_seurat_object(input.data) | is_se_object(input.data) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } #Generating data matrix to store value - mat <- matrix(0, length(df), length(cloneSize) - 1, - dimnames = list(names(df), + mat <- matrix(0, length(input.data), length(cloneSize) - 1, + dimnames = list(names(input.data), names(cloneSize)[-1])) #Assigning the clonal grouping - df <- lapply(df, '[[', cloneCall) - df <- lapply(df, na.omit) + input.data <- lapply(input.data, '[[', cloneCall) + input.data <- lapply(input.data, na.omit) fun <- function(x) { table(x)/length(x) } - df <- lapply(df, fun) + input.data <- lapply(input.data, fun) for (i in 2:length(cloneSize)) { - mat[,i-1] <- vapply(df, function (x) sum(x[x > cloneSize[i-1] & x <= + mat[,i-1] <- vapply(input.data, function (x) sum(x[x > cloneSize[i-1] & x <= cloneSize[i]]), FUN.VALUE = numeric(1)) colnames(mat)[i-1] <- paste0(names(cloneSize[i]), ' (', cloneSize[i-1], ' < X <= ', diff --git a/R/clonalLength.R b/R/clonalLength.R index 7b9abf3c..4864c3ef 100644 --- a/R/clonalLength.R +++ b/R/clonalLength.R @@ -4,7 +4,7 @@ #' acid (aa) sequence length. The sequence length visualized can be #' selected using the chains parameter, either the combined clonotype #' (both chains) or across all single chains. Visualization can either -#' be a histogram or if scale = TRUE, the output will be a density plot. +#' be a histogram or if \strong{scale} = TRUE, the output will be a density plot. #' Multiple sequencing runs can be group together using the #' group parameter. #' @@ -15,7 +15,7 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalLength(combined, cloneCall="aa", chain = "both") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt), #' CDR3 amino acid (aa). @@ -33,7 +33,7 @@ #' @concept Visualizing_Clones #' @return ggplot of the discrete or relative length distributions of #' clonotype sequences -clonalLength <- function(df, +clonalLength <- function(input.data, cloneCall = "aa", chain = "both", group.by = NULL, @@ -42,23 +42,28 @@ clonalLength <- function(df, exportTable = FALSE, palette = "inferno") { - cloneCall <- .theCall(cloneCall) - df <- .list.input.return(df, group.by) + + + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) #Sorting out graphing parameters xlab <- "Length" if(cloneCall == "CTnt") { - ylab <- "CDR3 (NT)" + ylab <- "CDR3 (NT)" } else if (cloneCall == "CTaa") { - ylab <- "CDR3 (AA)" + ylab <- "CDR3 (AA)" } else { stop("Please make a selection of the type of CDR3 sequence to analyze by using `cloneCall`") } #Identifying and assigning chains - chain.pos <- which(colnames(df[[1]]) == "cdr3_aa1")-1 - c1 <- na.omit(unique(substr(df[[1]][seq_len(10),chain.pos], 1,3))) + chain.pos <- which(colnames(input.data[[1]]) == "cdr3_aa1")-1 + c1 <- na.omit(unique(substr(input.data[[1]][seq_len(10),chain.pos], 1,3))) c2 <- switch(c1, "TRA" = "TRB", "IGH" = "IGL", @@ -66,12 +71,12 @@ clonalLength <- function(df, #Calculating Length Con.df <- NULL - Con.df <- .lengthDF(df, cloneCall, chain, group.by, c1, c2) + Con.df <- .lengthDF(input.data, cloneCall, chain, group.by, c1, c2) if(is.null(group.by) & order) { - Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(df)) + Con.df[,"values"] <- factor(Con.df[,"values"], levels = names(input.data)) } - names <- names(df) + names <- names(input.data) #Skip plotting if want to export table if (exportTable == TRUE) { @@ -101,7 +106,7 @@ clonalLength <- function(df, fill <- "Samples" col <- length(unique(Con.df$values)) if (scale == TRUE) { - yplus <- "Percent of " + yplus <- "Density of " plot <- ggplot(Con.df, aes(x = length, y = (after_stat(count))/sum(after_stat(count))*100, fill=values)) + @@ -122,4 +127,4 @@ clonalLength <- function(df, theme_classic() return(plot) -} \ No newline at end of file +} diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index ea78cb8e..0838fa66 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -25,7 +25,7 @@ #' group.by = "seurat_clusters") #' } #' -#' @param sc The single-cell object after \code{\link{combineExpression}}. +#' @param sc.data The single-cell object after \code{\link{combineExpression}}. #' @param reduction The name of the dimensional reduction of the single-cell object. #' @param group.by The variable to use for the nodes. #' @param filter.clones Use to select the top n clones (filter.clones = 2000) or @@ -35,8 +35,8 @@ #' @param filter.graph Remove the reciprocal edges from the half of the graph, #' allowing for cleaner visualization. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param exportTable Exports a table of the data into the global @@ -57,7 +57,7 @@ #' @concept SC_Functions #' @return ggplot object #' -clonalNetwork <- function(sc, +clonalNetwork <- function(sc.data, reduction = "umap", group.by = "ident", filter.clones = NULL, @@ -70,14 +70,14 @@ clonalNetwork <- function(sc, exportClones = FALSE, palette = "inferno") { to <- from <- weight <- y <- NULL - cloneCall <- .theCall(cloneCall) - meta <- .grabMeta(sc) - coord <- data.frame(.get.coord(sc, reduction), group.by = meta[,group.by]) + meta <- .grabMeta(sc.data) + cloneCall <- .theCall(meta, cloneCall) + coord <- data.frame(.get.coord(sc.data, reduction), group.by = meta[,group.by]) min <- c() - meta <- .grabMeta(sc) + meta <- .grabMeta(sc.data) if (!is.null(filter.clones)) { if(filter.clones == "min") { - meta <- .grabMeta(sc) + meta <- .grabMeta(sc.data) id.meta <- split(meta, meta[,group.by]) for (x in seq_along(id.meta)) { min.tmp <- length(which(!is.na(unique(id.meta[[x]][,cloneCall])))) diff --git a/R/clonalOccupy.R b/R/clonalOccupy.R index 2032499f..ba70b83d 100644 --- a/R/clonalOccupy.R +++ b/R/clonalOccupy.R @@ -1,8 +1,8 @@ #' Visualize the number of single cells with cloneSizes by cluster #' -#' View the count of clonotypes frequency group in seurat or SCE object +#' View the count of clonotypes frequency group in Seurat or SCE object #' meta data after \code{\link{combineExpression}}. The visualization will take the -#' new meta data variable **"cloneSize"** and plot the number of cells with +#' new meta data variable \strong{"cloneSize"} and plot the number of cells with #' each designation using a secondary variable, like cluster. Credit to #' the idea goes to Drs. Carmona and Andreatta and their work with #' \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. @@ -23,7 +23,7 @@ #' clonalOccupy(scRep_example, x.axis = "ident") #' table <- clonalOccupy(scRep_example, x.axis = "ident", exportTable = TRUE) #' -#' @param sc The single-cell object after \code{\link{combineExpression}}. +#' @param sc.data The single-cell object after \code{\link{combineExpression}}. #' @param x.axis The variable in the meta data to graph along the x.axis. #' @param label Include the number of clonotype in each category by x.axis variable. #' @param facet.by The column header used for faceting the graph. @@ -39,7 +39,7 @@ #' @concept SC_Functions #' @return Stacked bar plot of counts of cells by clonotype frequency group -clonalOccupy <- function(sc, +clonalOccupy <- function(sc.data, x.axis = "ident", label = TRUE, facet.by = NULL, @@ -47,8 +47,8 @@ clonalOccupy <- function(sc, na.include = FALSE, exportTable = FALSE, palette = "inferno") { - .checkSingleObject(sc) - meta <- .grabMeta(sc) + .checkSingleObject(sc.data) + meta <- .grabMeta(sc.data) meta <- melt(table(meta[!is.na(meta[,"clonalFrequency"]), c(x.axis, facet.by, "cloneSize")], useNA = "ifany")) #Check for NAs diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 290528d5..5f723e8d 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -6,19 +6,19 @@ #' @details #' The formulas for the indices are as follows: #' -#' Overlap Coefficient: +#' \strong{Overlap Coefficient:} #' \deqn{overlap = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} #' -#' Raw Count Overlap: +#' \strong{Raw Count Overlap:} #' \deqn{raw = \sum \min(a_i, b_i)} #' -#' Morisita Index: +#' \strong{Morisita Index:} #' \deqn{morisita = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} #' -#' Jaccard Index: +#' \strong{Jaccard Index:} #' \deqn{jaccard = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} #' -#' Cosine Similarity: +#' \strong{Cosine Similarity:} #' \deqn{cosine = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} #' #' Where: @@ -33,19 +33,18 @@ #' "P19B","P19L", "P20B", "P20L")) #' #' clonalOverlap(combined, -#' cloneCall = "gene", +#' cloneCall = "aa", #' method = "jaccard") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param method The method to calculate the "overlap", -# "morisita", "jaccard", "cosine" indices or "raw" -#' for the base numbers. +#' @param method The method to calculate the "overlap", "morisita", +#' "jaccard", "cosine" indices or "raw" for the base numbers. #' @param group.by The variable to use for grouping. #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. @@ -55,7 +54,7 @@ #' @export #' @concept Visualizing_Clones #' @return ggplot of the overlap of clones by group -clonalOverlap <- function(df, +clonalOverlap <- function(input.data, cloneCall = "strict", method = NULL, chain = "both", @@ -67,18 +66,22 @@ clonalOverlap <- function(df, } else { return_type = "unique" } - cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, group.by, cloneCall, chain) - df <- df[order(names(df))] - values <- str_sort(as.character(unique(names(df))), numeric = TRUE) - df <- df[.quiet(dput(values))] - num_samples <- length(df[]) - names_samples <- names(df) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + + input.data <- input.data[order(names(input.data))] + values <- str_sort(as.character(unique(names(input.data))), numeric = TRUE) + input.data <- input.data[.quiet(dput(values))] + num_samples <- length(input.data[]) + names_samples <- names(input.data) length <- seq_len(num_samples) if (chain != "both") { - for (i in seq_along(df)) { - df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) + for (i in seq_along(input.data)) { + input.data[[i]] <- .off.the.chain(input.data[[i]], chain, cloneCall) } } @@ -93,7 +96,7 @@ clonalOverlap <- function(df, #Calculating Index coef_matrix <- data.frame(matrix(NA, num_samples, num_samples)) - coef_matrix <- .calculateIndex(df, length, cloneCall, coef_matrix, indexFunc, return_type) + coef_matrix <- .calculateIndex(input.data, length, cloneCall, coef_matrix, indexFunc, return_type) #Data manipulation colnames(coef_matrix) <- names_samples diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index 2de77031..4b561d21 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -23,7 +23,7 @@ #' freq.cutpoint = 0.3, #' bins = 5) #' -#' @param sc The single-cell object after \code{\link{combineExpression}}. +#' @param sc.data The single-cell object after \code{\link{combineExpression}}. #' @param reduction The dimensional reduction to visualize #' @param freq.cutpoint The overlay cut point to include, this corresponds to the #' Frequency variable in the single-cell object @@ -38,15 +38,15 @@ #' #' @return ggplot object -clonalOverlay <- function(sc, +clonalOverlay <- function(sc.data, reduction = NULL, freq.cutpoint = 30, bins = 25, facet.by = NULL) { - .checkSingleObject(sc) + .checkSingleObject(sc.data) #Forming the data frame to plot - tmp <- data.frame(.grabMeta(sc), identity = sc@active.ident, .get.coord(sc, reduction)) + tmp <- data.frame(.grabMeta(sc.data), .get.coord(sc.data, reduction)) #Add facet variable if present if (!is.null(facet.by)) { facet.by <- tmp[,facet.by] @@ -57,7 +57,7 @@ clonalOverlay <- function(sc, #Plotting plot <- ggplot(tmp2, mapping = aes(x=tmp2[,(ncol(tmp2)-2)], y = tmp2[,(ncol(tmp2)-1)])) + - geom_point(tmp, mapping = aes(x=as.numeric(tmp[,(ncol(tmp)-2)]), y = as.numeric(tmp[,(ncol(tmp)-1)]), color = tmp[,"identity"]), size= 0.5) + + geom_point(tmp, mapping = aes(x=as.numeric(tmp[,(ncol(tmp)-2)]), y = as.numeric(tmp[,(ncol(tmp)-1)]), color = tmp[,"ident"]), size= 0.5) + geom_density_2d(color = "black", lwd=0.25, bins = bins) + theme_classic() + labs(color = "Active Identity") + diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 200ac130..12acd733 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -2,7 +2,7 @@ #' #' This function calculates the relative clonal space occupied by the #' clonotypes. The grouping of these clonotypes is based on the parameter -#' **clonalSplit**, at default, **clonalSplit** will group the clonotypes +#' \strong{clonalSplit}, at default, \strong{clonalSplit} will group the clonotypes #' into bins of 1:10, 11:100, 101:1001, etc. To adjust the clonotypes #' selected, change the numbers in the variable split. If a matrix output #' for the data is preferred, set exportTable = TRUE. @@ -14,12 +14,12 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalProportion(combined, cloneCall = "gene") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param clonalSplit The cut points for the specific clonotypes. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param group.by The variable to use for grouping. @@ -35,7 +35,7 @@ #' @export #' @concept Visualizing_Clones #' @return ggplot of the space occupied by the specific rank of clonotypes -clonalProportion <- function(df, +clonalProportion <- function(input.data, clonalSplit = c(10, 100, 1000, 10000, 30000, 100000), cloneCall = "strict", chain = "both", @@ -43,27 +43,31 @@ clonalProportion <- function(df, exportTable = FALSE, palette = "inferno") { Con.df <- NULL - cloneCall <- .theCall(cloneCall) - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, cloneCall, chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } #Generating data matrix to store value - mat <- matrix(0, length(df), length(clonalSplit), dimnames = list(names(df), + mat <- matrix(0, length(input.data), length(clonalSplit), dimnames = list(names(input.data), paste0('[', c(1, clonalSplit[-length(clonalSplit)] + 1), ':', clonalSplit, ']'))) #Assigning the clonal grouping - df <- lapply(df, '[[', cloneCall) - df <- lapply(df, na.omit) - df <- lapply(df, as.data.frame(table)) - for (i in seq_along(df)) { - df[[i]] <- rev(sort(as.numeric(df[[i]][,2]))) + input.data <- lapply(input.data, '[[', cloneCall) + input.data <- lapply(input.data, na.omit) + input.data <- lapply(input.data, as.data.frame(table)) + for (i in seq_along(input.data)) { + input.data[[i]] <- rev(sort(as.numeric(input.data[[i]][,2]))) } cut <- c(1, clonalSplit[-length(clonalSplit)] + 1) for (i in seq_along(clonalSplit)) { - mat[,i] <- vapply(df, function (x) sum(na.omit(x[cut[i]:clonalSplit[i]])), + mat[,i] <- vapply(input.data, function (x) sum(na.omit(x[cut[i]:clonalSplit[i]])), FUN.VALUE = numeric(1)) } if (exportTable == TRUE) { diff --git a/R/clonalQuant.R b/R/clonalQuant.R index d77bb520..86947b5b 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -11,11 +11,11 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalQuant(combined, cloneCall="strict", scale = TRUE) #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by The column header used for grouping. @@ -27,7 +27,7 @@ #' @export #' @concept Visualizing_Clones #' @return ggplot of the total or relative unique clonotypes -clonalQuant <- function(df, +clonalQuant <- function(input.data, cloneCall = "strict", chain = "both", scale=FALSE, @@ -39,12 +39,15 @@ clonalQuant <- function(df, if (length(group.by) > 1) { stop("Only one item in the group.by variable can be listed.") } - - cloneCall <- .theCall(cloneCall) - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTgene", chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } mat.names <- c("contigs","values", "total", group.by) @@ -55,17 +58,17 @@ clonalQuant <- function(df, } else { x <- "values" labs <- "Samples" - col <- length(unique(names(df))) + col <- length(unique(names(input.data))) } - mat <- data.frame(matrix(NA, length(df), length(mat.names))) + mat <- data.frame(matrix(NA, length(input.data), length(mat.names))) colnames(mat) <- mat.names - for (i in seq_along(df)) { - mat[i,1] <- length(na.omit(unique(df[[i]][,cloneCall]))) - mat[i,2] <- names(df)[i] - mat[i,3] <- length(na.omit(df[[i]][,cloneCall])) + for (i in seq_along(input.data)) { + mat[i,1] <- length(na.omit(unique(input.data[[i]][,cloneCall]))) + mat[i,2] <- names(input.data)[i] + mat[i,3] <- length(na.omit(input.data[[i]][,cloneCall])) if (!is.null(group.by)) { - location <- which(colnames(df[[i]]) == group.by) - mat[i,4] <- df[[i]][1,location] + location <- which(colnames(input.data[[i]]) == group.by) + mat[i,4] <- input.data[[i]][1,location] } } if (scale) { @@ -79,7 +82,7 @@ clonalQuant <- function(df, } if (exportTable) { - if (length(df) > 1) { + if (length(input.data) > 1) { return(mat) } # if a single sample, remove the "values" column if NA @@ -109,7 +112,7 @@ clonalQuant <- function(df, scale_fill_manual(values = .colorizer(palette, col)) # if it is a single run, remove x axis labels if sample name missing - if ((length(df) == 1) && identical(names(df), NA_character_)) { + if ((length(input.data) == 1) && identical(names(input.data), NA_character_)) { plot <- plot + ggplot2::theme( axis.title.x = element_blank(), diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index 321da0e9..1035bdda 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -1,10 +1,10 @@ #' Calculate rarefaction based on the abundance of clones #' -#' This functions uses the Hill numbers of order q: species richness (**q = 0&&), -#' Shannon diversity (**q = 1**, the exponential of Shannon entropy) and Simpson -#' diversity (**q = 2**, the inverse of Simpson concentration) to compute diversity -#' estimates for rarefaction and extrapolation. The function relies on -#' \code{\link[iNEXT]{iNEXT}}. Please read and cite the +#' This functions uses the Hill numbers of order q: species richness (\strong{q = 0}), +#' Shannon diversity (\strong{q = 1}), the exponential of Shannon entropy and Simpson +#' diversity (\strong{q = 2}, the inverse of Simpson concentration) to compute diversity +#' estimates for rarefaction and extrapolation. The function relies on the +#' \code{\link[iNEXT]{iNEXT}} R package. Please read and cite the #' \href{https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12613}{manuscript} #' if using this function. #' @@ -16,11 +16,11 @@ #' clonalRarefaction(combined[c(1,2)], cloneCall = "gene", n.boots = 3) #' #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param group.by The variable to use for grouping. @@ -36,7 +36,7 @@ #' @import ggplot2 #' @export #' @concept Visualizing_Clones -clonalRarefaction <- function(df, +clonalRarefaction <- function(input.data, cloneCall = "strict", chain = "both", group.by = NULL, @@ -45,18 +45,21 @@ clonalRarefaction <- function(df, n.boots = 20, exportTable = FALSE, palette = "inferno") { - cloneCall <- .theCall(cloneCall) - - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTgene", chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } - mat.list <- lapply(df, function(x) { + mat.list <- lapply(input.data, function(x) { table(x[,cloneCall]) }) - col <- length(df) + col <- length(input.data) mat <- iNEXT(mat.list, q=hill.numbers, datatype="abundance",nboot = n.boots) plot <- ggiNEXT(mat, type=plot.type) + scale_shape_manual(values = rep(16,col)) + diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 791e00bb..c461c7b1 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -14,11 +14,11 @@ #' y.axis = "P17L", #' graph = "proportion") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param x.axis name of the list element to appear on the x.axis. @@ -34,9 +34,9 @@ #' #' @export #' @concept Visualizing_Clones -#' @return ggplot of the relative clonotype numbers +#' @return ggplot of the relative clonotype numbers between two sequencing runs -clonalScatter <- function(df, +clonalScatter <- function(input.data, cloneCall ="strict", x.axis = NULL, y.axis = NULL, @@ -46,20 +46,23 @@ clonalScatter <- function(df, graph = "proportion", exportTable = FALSE, palette = "inferno") { - cloneCall <- .theCall(cloneCall) - df <- .data.wrangle(df, group.by, cloneCall, chain) - axes <- which(names(df) %in% c(x.axis, y.axis, dot.size)) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + axes <- which(names(input.data) %in% c(x.axis, y.axis, dot.size)) #Making new data frame and adding variables to graph - x.df <- as.data.frame(table(df[[x.axis]][,cloneCall])) + x.df <- as.data.frame(table(input.data[[x.axis]][,cloneCall])) colnames(x.df)[2] <- x.axis - y.df <- as.data.frame(table(df[[y.axis]][,cloneCall])) + y.df <- as.data.frame(table(input.data[[y.axis]][,cloneCall])) colnames(y.df)[2] <- y.axis mat <- merge(x.df, y.df, by = "Var1", all = TRUE) if (dot.size != "total") { if (dot.size %!in% colnames(mat)) { - size.df <- as.data.frame(table(df[[dot.size]][,cloneCall])) + size.df <- as.data.frame(table(input.data[[dot.size]][,cloneCall])) colnames(size.df)[2] <- dot.size mat <- merge(mat, size.df, by = "Var1", all = TRUE) } mat[is.na(mat)] <- 0 diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 931bb346..f417546d 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -6,7 +6,7 @@ #' \href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. #' #' @details -#' The probability density function (pdf) for the Generalized Pareto Distribution (GPD) is given by: +#' The probability density function (pdf) for the \strong{Generalized Pareto Distribution (GPD)} is given by: #' \deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ #' f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) #' } @@ -19,7 +19,7 @@ #' \item{\eqn{x \ge \mu} if \eqn{\xi \ge 0} and \eqn{\mu \le x \le \mu - \sigma/\xi} if \eqn{\xi < 0}} #' } #' -#' The probability density function (pdf) for the Gamma Distribution is given by: +#' The probability density function (pdf) for the \strong{Gamma Distribution} is given by: #' \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ #' f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) #' } @@ -39,7 +39,7 @@ #' "P19B","P19L", "P20B", "P20L")) #' clonalSizeDistribution(combined, cloneCall = "strict", method="ward.D2") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -61,7 +61,7 @@ #' @return ggplot dendrogram of the clone size distribution #' @author Hillary Koch -clonalSizeDistribution <- function(df, +clonalSizeDistribution <- function(input.data, cloneCall ="strict", chain = "both", method = "ward.D2", @@ -70,21 +70,24 @@ clonalSizeDistribution <- function(df, exportTable = FALSE, palette = "inferno") { x <- xend <- yend <- mpg_div_hp <- NULL - cloneCall <- .theCall(cloneCall) - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTgene", chain) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) + sco <- is_seurat_object(input.data) | is_se_object(input.data) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } - data <- bind_rows(df) - unique_df <- unique(data[,cloneCall]) + data <- bind_rows(input.data) + unique_df<- unique(data[,cloneCall]) #Forming data frame to store values - Con.df <- data.frame(matrix(NA, length(unique_df), length(df))) + Con.df <- data.frame(matrix(NA, length(unique_df), length(input.data))) Con.df <- data.frame(unique_df, Con.df, stringsAsFactors = FALSE) colnames(Con.df)[1] <- "clonotype" - for (i in seq_along(df)) { - data <- df[[i]] + for (i in seq_along(input.data)) { + data <- input.data[[i]] data <- data.frame(table(data[,cloneCall]), stringsAsFactors = FALSE) colnames(data) <- c(cloneCall, "Freq") @@ -94,13 +97,13 @@ clonalSizeDistribution <- function(df, Con.df[y,i+1] <- data[location.y[1],"Freq"] } } - colnames(Con.df)[2:(length(df)+1)] <- names(df) + colnames(Con.df)[2:(length(input.data)+1)] <- names(input.data) Con.df[is.na(Con.df)] <- 0 list <- list() - for (i in seq_along(df)) { + for (i in seq_along(input.data)) { list[[i]] <- suppressWarnings(.fdiscgammagpd(Con.df[,i+1], useq = threshold)) } - names(list) <- names(df) + names(list) <- names(input.data) grid <- 0:10000 distances <- .get_distances(list, grid, modelType="Spliced") mat_melt <- dendro_data(hclust(as.dist(distances), method = method), type = "rectangle") diff --git a/R/combineContigs.R b/R/combineContigs.R index a9397f11..bfc8cbe8 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -25,8 +25,8 @@ utils::globalVariables(c( #' parameters, the function will add the strings as prefixes to prevent issues #' with repeated barcodes. The resulting new barcodes will need to match the #' Seurat or SCE object in order to use, \code{\link{combineExpression}}. -#' Several levels of filtering exist - `removeNA`, `removeMulti`, or -#' `filterMulti` are parameters that control how the function deals with +#' Several levels of filtering exist - \emph{removeNA}, \emph{removeMulti}, or +#' \emph{filterMulti} are parameters that control how the function deals with #' barcodes with multiple chains recovered. #' #' @examples @@ -34,7 +34,7 @@ utils::globalVariables(c( #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' -#' @param df List of filtered contig annotations or outputs from \code{\link{loadContigs}} +#' @param input.data List of filtered contig annotations or outputs from \code{\link{loadContigs}} #' @param samples The labels of samples #' @param ID The additional sample labeling #' @param removeNA This will remove any chain without values. @@ -47,33 +47,33 @@ utils::globalVariables(c( #' @concept Loading_and_Processing_Contigs #' @return List of clonotypes for individual cell barcodes #' -combineTCR <- function(df, +combineTCR <- function(input.data, samples = NULL, ID = NULL, removeNA = FALSE, removeMulti = FALSE, filterMulti = FALSE) { - df <- .checkList(df) - df <- .checkContigs(df) + input.data <- .checkList(input.data) + input.data <- .checkContigs(input.data) out <- NULL final <- NULL - for (i in seq_along(df)) { - if(c("chain") %in% colnames(df[[i]])) { - df[[i]] <- subset(df[[i]], chain != "Multi") + for (i in seq_along(input.data)) { + if(c("chain") %in% colnames(input.data[[i]])) { + input.data[[i]] <- subset(input.data[[i]], chain != "Multi") } - if(c("productive") %in% colnames(df[[i]])) { - df[[i]] <- subset(df[[i]], productive %in% c(TRUE, "TRUE", "True", "true")) + if(c("productive") %in% colnames(input.data[[i]])) { + input.data[[i]] <- subset(input.data[[i]], productive %in% c(TRUE, "TRUE", "True", "true")) } - df[[i]]$sample <- samples[i] - df[[i]]$ID <- ID[i] + input.data[[i]]$sample <- samples[i] + input.data[[i]]$ID <- ID[i] if (filterMulti) { - df[[i]] <- .filteringMulti(df[[i]]) + input.data[[i]] <- .filteringMulti(input.data[[i]]) } } #Prevents error caused by list containing elements with 0 rows - blank.rows <- which(unlist(lapply(df, nrow)) == 0) + blank.rows <- which(unlist(lapply(input.data, nrow)) == 0) if(length(blank.rows) > 0) { - df <- df[-blank.rows] + input.data <- input.data[-blank.rows] if(!is.null(samples)) { samples <- samples[-blank.rows] } @@ -82,9 +82,9 @@ combineTCR <- function(df, } } if (!is.null(samples)) { - out <- .modifyBarcodes(df, samples, ID) + out <- .modifyBarcodes(input.data, samples, ID) } else { - out <- df + out <- input.data } for (i in seq_along(out)) { # ideally the nested code could be in a function for a better development/testing experience data2 <- out[[i]] @@ -95,7 +95,7 @@ combineTCR <- function(df, Con.df$barcode <- unique_df Con.df <- .parseTCR(Con.df, unique_df, data2) Con.df <- .assignCT(cellType = "T", Con.df) - Con.df[Con.df == "NA_NA" | Con.df == "NA_NA_NA_NA"] <- NA + Con.df[Con.df == "NA_NA" | Con.df == "NA;NA_NA;NA"] <- NA data3 <- merge(data2[,-which(names(data2) %in% c("TCR1","TCR2"))], Con.df, by = "barcode") if (!is.null(samples) & !is.null(ID)) { @@ -137,14 +137,14 @@ combineTCR <- function(df, #' the function will add the strings as prefixes to prevent issues with #' repeated barcodes. The resulting new barcodes will need to match the #' Seurat or SCE object in order to use, \code{\link{combineExpression}}. -#' Unlike \code{\link{combineTCR}}, combineBCR produces a column **CTstrict** +#' Unlike \code{\link{combineTCR}}, combineBCR produces a column \strong{CTstrict} #' of an index of nucleotide sequence and the corresponding V gene. #' This index automatically calculates the Levenshtein distance between #' sequences with the same V gene and will index sequences using a normalized #' Levenshtein distance with the same ID. After which, clonotype clusters -#' are called using the \code{\link[igraph]{components}} function. Clonotype +#' are called using the \code{\link[igraph]{components}} function. Clones #' that are clustered across multiple sequences will then be labeled -#' with "LD" in the CTstrict header. +#' with "Cluster" in the CTstrict header. #' #' @examples #' #Data derived from the 10x Genomics intratumoral NSCLC B cells @@ -153,7 +153,7 @@ combineTCR <- function(df, #' samples = "Patient1", #' threshold = 0.85) #' -#' @param df List of filtered contig annotations or outputs from \code{\link{loadContigs}}. +#' @param input.data List of filtered contig annotations or outputs from \code{\link{loadContigs}}. #' @param samples The labels of samples #' @param ID The additional sample labeling #' @param call.related.clones Use the nucleotide sequence and V gene to call related clones. @@ -168,7 +168,7 @@ combineTCR <- function(df, #' @export #' @concept Loading_and_Processing_Contigs #' @return List of clonotypes for individual cell barcodes -combineBCR <- function(df, +combineBCR <- function(input.data, samples = NULL, ID = NULL, call.related.clones = TRUE, @@ -176,30 +176,30 @@ combineBCR <- function(df, removeNA = FALSE, removeMulti = FALSE, filterMulti = TRUE) { - df <- .checkList(df) - df <- .checkContigs(df) + input.data <- .checkList(input.data) + input.data <- .checkContigs(input.data) out <- NULL final <- list() chain1 <- "heavy" chain2 <- "light" - for (i in seq_along(df)) { - df[[i]] <- subset(df[[i]], chain %in% c("IGH", "IGK", "IGL")) - df[[i]]$ID <- ID[i] + for (i in seq_along(input.data)) { + input.data[[i]] <- subset(input.data[[i]], chain %in% c("IGH", "IGK", "IGL")) + input.data[[i]]$ID <- ID[i] if (filterMulti) { # Keep IGH / IGK / IGL info in save_chain - df[[i]]$save_chain <- df[[i]]$chain + input.data[[i]]$save_chain <- input.data[[i]]$chain # Collapse IGK and IGL chains - df[[i]]$chain <- ifelse(df[[i]]$chain=="IGH","IGH","IGLC") - df[[i]] <- .filteringMulti(df[[i]]) + input.data[[i]]$chain <- ifelse(input.data[[i]]$chain=="IGH","IGH","IGLC") + input.data[[i]] <- .filteringMulti(input.data[[i]]) # Get back IGK / IGL distinction - df[[i]]$chain <- df[[i]]$save_chain - df[[i]]$save_chain <- NULL + input.data[[i]]$chain <- input.data[[i]]$save_chain + input.data[[i]]$save_chain <- NULL } } if (!is.null(samples)) { - out <- .modifyBarcodes(df, samples, ID) + out <- .modifyBarcodes(input.data, samples, ID) } else { - out <- df + out <- input.data } for (i in seq_along(out)) { data2 <- data.frame(out[[i]]) @@ -231,7 +231,7 @@ combineBCR <- function(df, } final[[i]]$sample <- samples[i] final[[i]]$ID <- ID[i] - final[[i]][final[[i]] == "NA_NA" | final[[i]] == "NA_NA_NA_NA"] <- NA + final[[i]][final[[i]] == "NA_NA" | final[[i]] == "NA;NA_NA;NA" | ] <- NA if (!is.null(sample) & !is.null(ID)) { final[[i]]<- final[[i]][, c("barcode", "sample", "ID", heavy_lines[c(1,2,3)], light_lines[c(1,2,3)], CT_lines)] diff --git a/R/combineExpression.R b/R/combineExpression.R index 38e52c3b..58fbbdaf 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -3,8 +3,8 @@ #' This function adds the immune receptor information to the Seurat or #' SCE object to the meta data. By default this function also calculates #' the frequencies and proportion of the clonotypes by sequencing -#' run (**group.by** = "none"). To change how the frequencies/proportions -#' are calculated, select a column header for the **group.by** variable. +#' run (\strong{group.by} = NULL). To change how the frequencies/proportions +#' are calculated, select a column header for the \strong{group.by} variable. #' Importantly, before using \code{\link{combineExpression}} ensure the #' barcodes of the single-cell object object match the barcodes in the output #' of the \code{\link{combineTCR}} or \code{\link{combineBCR}}. @@ -17,18 +17,16 @@ #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) -#' sce <- Seurat::as.SingleCellExperiment(sce) #' #' #Using combineExpresion() -#' sce <- combineExpression(combined, sce) +#' scRep_example <- combineExpression(combined, scRep_example) #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or a list of -#' both c(\code{\link{combineTCR}}, c\code{\link{combineBCR}}) -#' @param sc The seurat or SingleCellExperiment (SCE) object to attach +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or a list of +#' both c(\code{\link{combineTCR}}, \code{\link{combineBCR}}). +#' @param sc.data The Seurat or Single-Cell Experiment (SCE) object to attach #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt) CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL" #' @param group.by The column label in the combined clones in which @@ -49,14 +47,14 @@ #' @importFrom SummarizedExperiment colData<- colData #' @export #' @concept SC_Functions -#' @return seurat or SingleCellExperiment object with attached clonotype +#' @return Single-cell object with clonotype information added to meta data #' information #' -combineExpression <- function(df, - sc, +combineExpression <- function(input.data, + sc.data, cloneCall ="strict", chain = "both", - group.by ="none", + group.by = NULL, proportion = TRUE, filterNA = FALSE, cloneSize = c(Rare = 1e-4,Small = 0.001,Medium = 0.01,Large = 0.1,Hyperexpanded = 1), @@ -69,19 +67,19 @@ combineExpression <- function(df, } cloneSize <- c(None = 0, cloneSize) if (chain != "both") { - df[[i]] <- .off.the.chain(df[[i]], chain, cloneCall) + input.data[[i]] <- .off.the.chain(input.data[[i]], chain, cloneCall) } - df <- .checkList(df) - cloneCall <- .theCall(cloneCall) + input.data <- .checkList(input.data) + cloneCall <- .theCall(input.data, cloneCall) #Getting Summaries of clones from combineTCR() or combineBCR() Con.df <- NULL - meta <- .grabMeta(sc) + meta <- .grabMeta(sc.data) cell.names <- rownames(meta) - if (group.by == "none" | !is.null(group.by)) { - for (i in seq_along(df)) { + if (group.by == "none" || is.null(group.by)) { + for (i in seq_along(input.data)) { - data <- data.frame(df[[i]], stringsAsFactors = FALSE) + data <- data.frame(input.data[[i]], stringsAsFactors = FALSE) data2 <- unique(data[,c("barcode", cloneCall)]) data2 <- na.omit(data2[data2[,"barcode"] %in% cell.names,]) data2 <- data2 %>% @@ -96,7 +94,7 @@ combineExpression <- function(df, Con.df <- rbind.data.frame(Con.df, data) } } else if (group.by != "none" | !is.null(group.by)) { - data <- data.frame(bind_rows(df), stringsAsFactors = FALSE) + data <- data.frame(bind_rows(input.data), stringsAsFactors = FALSE) data2 <- na.omit(unique(data[,c("barcode", cloneCall, group.by)])) data2 <- data2[data2[,"barcode"] %in% cell.names, ] data2 <- as.data.frame(data2 %>% @@ -156,38 +154,35 @@ combineExpression <- function(df, - if (is_seurat_object(sc)) { + if (is_seurat_object(sc.data)) { if (length(which(rownames(PreMeta) %in% - rownames(sc[[]])))/length(rownames(sc[[]])) < 0.01) { + rownames(sc.data[[]])))/length(rownames(sc.data[[]])) < 0.01) { warning(.warn_str) } col.name <- names(PreMeta) %||% colnames(PreMeta) - sc[[col.name]] <- PreMeta + sc.data[[col.name]] <- PreMeta } else { - rownames <- rownames(colData(sc)) + rownames <- rownames(colData(sc.data)) if (length(which(rownames(PreMeta) %in% rownames))/length(rownames) < 0.01) { warning(.warn_str) } - colData(sc) <- cbind(colData(sc), PreMeta[rownames,])[, union(colnames(colData(sc)), colnames(PreMeta))] - rownames(colData(sc)) <- rownames + colData(sc.data) <- cbind(colData(sc.data), PreMeta[rownames,])[, union(colnames(colData(sc.data)), colnames(PreMeta))] + rownames(colData(sc.data)) <- rownames } - if (filterNA) { sc <- .filteringNA(sc) } - sc$cloneSize <- factor(sc$cloneSize, levels = rev(names(cloneSize))) + if (filterNA) { sc.data <- .filteringNA(sc.data) } + sc.data$cloneSize <- factor(sc.data$cloneSize, levels = rev(names(cloneSize))) - if(is_seurat_object(sc)) { - sc@commands[["combineExpression"]] <- make_screp_seurat_cmd( - call_time, sc@active.assay + if(is_seurat_object(sc.data)) { + sc.data@commands[["combineExpression"]] <- make_screp_seurat_cmd( + call_time, sc.data@active.assay ) } - sc + return(sc.data) } -.warn_str <- "< 1% of barcodes match: Ensure the barcodes in - the Seurat object match the barcodes in the combined immune receptor - list from scRepertoire - most common issue is the addition of the - prefixes corresponding to 'samples' and 'ID' in the combineTCR/BCR() - functions" +.warn_str <- "< 1% of barcodes match: Ensure the barcodes in the single-cell object match the barcodes in the combined immune receptor output from scRepertoire. If getting this error, please check https://www.borch.dev/uploads/screpertoire/articles/faq." + diff --git a/R/createHTOContigList.R b/R/createHTOContigList.R index 2ad405a7..82c0ce5a 100644 --- a/R/createHTOContigList.R +++ b/R/createHTOContigList.R @@ -5,7 +5,7 @@ #' annotation output and the single-cell RNA object to create the list. If using an #' integrated single-cell object, it is recommended to split the object by sequencing run #' and remove extra prefixes and suffixes on the barcode before using -#' \code{\link{createHTOContigList}}. Alternatively, the variable **multi.run** can +#' \code{\link{createHTOContigList}}. Alternatively, the variable \strong{multi.run} can #' be used to separate a list of contigs by a meta data variable. This may have #' issues with the repeated barcodes. #' @@ -14,28 +14,28 @@ #' filtered.contig <- read.csv(".../Sample/outs/filtered_contig_annotations.csv") #' #' contig.list <- createHTOContigList(contig = filtered.contig, -#' sc = Seurat.Obj, +#' sc.data = Seurat.Obj, #' group.by = "HTO_maxID") #' } #' #' @param contig The filtered contig annotation file from multiplexed experiment -#' @param sc The Seurat or SCE object. +#' @param sc.data The Seurat or Single-Cell Experiment object. #' @param group.by One or more meta data headers to create the contig list based on. #' If more than one header listed, the function combines them into a single variable. #' @param multi.run If using integrated single-cell object, the meta data variable #' that indicates the sequencing run. #' @export #' @concept Loading_and_Processing_Contigs -#' @return Returns a list of contigs corresponding to the multiplexed single-cell object +#' @return Returns a list of contigs as input for \code{\link{combineBCR}} or \code{\link{combineTCR}} createHTOContigList <- function(contig, - sc, + sc.data, group.by = NULL, multi.run = NULL){ contig.list <- NULL - .checkSingleObject(sc) - meta <- .grabMeta(sc) + .checkSingleObject(sc.data) + meta <- .grabMeta(sc.data) if (length(group.by) > 1) { meta["group.by"] <- apply(meta[ , group.by] , 1 , paste , collapse = "." ) } else { diff --git a/R/data.R b/R/data.R index 1a46f4a9..612d99a4 100644 --- a/R/data.R +++ b/R/data.R @@ -1,16 +1,24 @@ -#' A data set of T cell contigs as a list outputed from the -#' filter_contig_annotation files. +#' A list of 8 single-cell T cell receptor sequences runs. +#' +#' @description A list of 8 filtered_contig_annotation.csv files +#' outputted from 10X Cell Ranger. More information on the +#' data can be found in the following +#' \href{https://pubmed.ncbi.nlm.nih.gov/33622974/}{manuscript}. +#' #' @docType data #' @concept Data #' @name contig_list #' NULL -#' A seurat object of 500 single T cells derived -#' from 4 patients with acute respiratory distress (ARDS). +#' A Seurat object of 500 single T cells, #' #' @description The object is compatible with `contig_list` and the TCR -#' sequencing data can be added with `combineExpression`. +#' sequencing data can be added with `combineExpression`. The data is +#' from 4 patients with acute respiratory distress, with samples taken +#' from both the lung and peripheral blood. More information on the +#' data can be found in the following +#' \href{https://pubmed.ncbi.nlm.nih.gov/33622974/}{manuscript}. #' #' @name scRep_example #' @concept Data @@ -20,9 +28,11 @@ NULL #' Processed subset of `contig_list` #' -#' @description A list of 8 dataframes of T cell contigs outputted from the +#' @description A list of 8 data frames of T cell contigs outputted from the #' `filtered_contig_annotation` files, but subsetted to 365 valid T cells -#' which correspond to the same barcodes found in `scRep_example` +#' which correspond to the same barcodes found in `scRep_example`. The +#' data is originally derived from the following +#' \href{https://pubmed.ncbi.nlm.nih.gov/33622974/}{manuscript}. #' #' @usage data("mini_contig_list") #' diff --git a/R/exportClones.R b/R/exportClones.R index 43e782e7..22aa3534 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -12,10 +12,10 @@ #' exportClones(combined) #' } #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param group.by The variable to use for grouping. -#' @param write.file TRUE, save the file, FALSE, return a data.frame +#' @param write.file \strong{TRUE}, save the file or \strong{FALSE}, return a data.frame #' @param dir directory location to save the csv #' @param file.name the csv file name #' @importFrom dplyr bind_rows @@ -23,31 +23,31 @@ #' @importFrom utils write.csv #' @export #' @concept Loading_and_Processing_Contigs -#' @return ggplot of percentage of V and J gene pairings as a heatmap +#' @return CSV file of the paired sequences. #' @author Jonathan Noonan, Nick Borcherding -exportClones <- function(df, +exportClones <- function(input.data, group.by = NULL, write.file = TRUE, dir = NULL, file.name = "clones.csv") { - df <- .data.wrangle(df, group.by, "CTgene", "both") + input.data <- .data.wrangle(input.data, group.by, "CTgene", "both") - df <- bind_rows(df, .id = "group") + input.data <- bind_rows(input.data, .id = "group") - genes <- str_split(df[,"CTgene"], "_", simplify = TRUE) - aa <- str_split(df[,"CTaa"], "_", simplify = TRUE) - nt <- str_split(df[,"CTnt"], "_", simplify = TRUE) + genes <- str_split(input.data[,"CTgene"], "_", simplify = TRUE) + aa <- str_split(input.data[,"CTaa"], "_", simplify = TRUE) + nt <- str_split(input.data[,"CTnt"], "_", simplify = TRUE) chain1_gene <- str_split(genes[,1], "[.]", simplify = TRUE) - mat <- data.frame(row.names = df[,"barcode"], + mat <- data.frame(row.names = input.data[,"barcode"], chain1_aa = aa[,1], chain1_nt = nt[,1], chain1_genes = genes[,1], chain2_aa = aa[,2], chain2_nt = nt[,2], chain2_genes = genes[,2], - group = df[,"group"]) + group = input.data[,"group"]) mat[mat == "NA"] <- NA if(!write.file) { return(mat) diff --git a/R/getCircilize.R b/R/getCircilize.R index 6783f434..dc745a15 100644 --- a/R/getCircilize.R +++ b/R/getCircilize.R @@ -4,7 +4,7 @@ #' This function will take the meta data from the product of #' \code{\link{combineExpression}} and generate a relational data frame to #' be used for a chord diagram. Each cord will represent the number of -#' clonotype unique and shared across the multiple **group.by** variable. +#' clonotype unique and shared across the multiple \strong{group.by} variable. #' If using the downstream circlize R package, please read and cite the #' following \href{https://pubmed.ncbi.nlm.nih.gov/24930139/}{manuscript}. #' If looking for more advance ways for circular visualizations, there @@ -19,14 +19,15 @@ #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' scRep_example <- combineExpression(combined, scRep_example) +#' scRep_example <- combineExpression(combined, +#' scRep_example) #' #' #Getting data frame output for Circlize #' circles <- getCirclize(scRep_example, #' group.by = "seurat_clusters") #' #' -#' @param sc The single-cell object after \code{\link{combineExpression}}. +#' @param sc.data The single-cell object after \code{\link{combineExpression}}. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -41,12 +42,12 @@ #' @concept SC_Functions #' @return data frame of shared clonotypes between groups #' @author Dillon Corvino, Nick Borcherding -getCirclize <- function(sc, +getCirclize <- function(sc.data, cloneCall = "strict", group.by = NULL, proportion = FALSE) { - meta <- .grabMeta(sc) - cloneCall <- .theCall(cloneCall) + meta <- .grabMeta(sc.data) + cloneCall <- .theCall(meta, cloneCall) #Quantifying clonotypes across group.by variable dat <- meta[, c(cloneCall, group.by)] diff --git a/R/highlightClones.R b/R/highlightClones.R index 0bcfe156..9c2386be 100644 --- a/R/highlightClones.R +++ b/R/highlightClones.R @@ -13,30 +13,32 @@ #' scRep_example <- get(data("scRep_example")) #' #' #Using combineExpresion() -#' scRep_example <- combineExpression(combined, scRep_example) +#' scRep_example <- combineExpression(combined, +#' scRep_example) #' #' #Using highlightClonotype() -#' scRep_example <- highlightClones(scRep_example , cloneCall= "aa", -#' sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) +#' scRep_example <- highlightClones(scRep_example, +#' cloneCall= "aa", +#' sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) #' -#' @param sc The single-cell object to attach after \code{\link{combineExpression}} +#' @param sc.data The single-cell object to attach after \code{\link{combineExpression}} #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param sequence The specific sequence or sequence to highlight #' @importFrom S4Vectors DataFrame #' @export #' @concept SC_Functions #' @return Single-cell object object with new meta data column for indicated clones -highlightClones <- function(sc, +highlightClones <- function(sc.data, cloneCall = c("gene", "nt", "aa", "strict"), sequence = NULL){ - if (!is_seurat_or_se_object(sc)) { + if (!is_seurat_or_se_object(sc.data)) { stop("Please select a single-cell object") } - cloneCall <- .theCall(cloneCall) - meta <- .grabMeta(sc) + cloneCall <- .theCall(.grabMeta(sc.data), cloneCall) + meta <- .grabMeta(sc.data) meta$highlight <- NA for(i in seq_along(sequence)) { meta$highlight <- ifelse(meta[,cloneCall] == sequence[i], @@ -45,11 +47,11 @@ highlightClones <- function(sc, meta <- meta[,-(which(colnames(meta) == "ident"))] - if(is_se_object(sc)) { - colData(sc) <- DataFrame(meta) + if(is_se_object(sc.data)) { + colData(sc.data) <- DataFrame(meta) } else { col.name <- names(meta) %||% colnames(meta) - sc[[col.name]] <- meta + sc.data[[col.name]] <- meta } - return(sc) + return(sc.data) } \ No newline at end of file diff --git a/R/loadContigs.R b/R/loadContigs.R index 41719735..0de96619 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -18,7 +18,6 @@ #' } #' #' @examples -#' #' TRUST4 <- read.csv("https://www.borch.dev/uploads/contigs/TRUST4_contigs.csv") #' contig.list <- loadContigs(TRUST4, format = "TRUST4") #' @@ -28,18 +27,18 @@ #' WAT3R <- read.csv("https://www.borch.dev/uploads/contigs/WAT3R_contigs.csv") #' contig.list <- loadContigs(WAT3R, format = "WAT3R") #' -#' @param dir The directory in which contigs are located or a list with contig elements +#' @param input The directory in which contigs are located or a list with contig elements #' @param format The format of the single-cell contig, currently supporting: #' "10X", "AIRR", "BD", "JSON", "MiXCR", "Omniscope", "TRUST4", and "WAT3R" #' @importFrom utils read.csv read.delim #' @importFrom rjson fromJSON #' @export #' @concept Loading_and_Processing_Contigs -#' @return List of contigs for further processing in scRepertoire -loadContigs <- function(dir, +#' @return List of contigs for compatibility with \code{\link{combineTCR}} or \code{\link{combineBCR}} +loadContigs <- function(input, format = "10X") { #Loading from directory, recursively - if (inherits(x=dir, what ="character")) { + if (inherits(x=input, what ="character")) { format.list <- list("WAT3R" = "barcode_results.csv", "10X" = "filtered_contig_annotation.csv", "AIRR" = "airr_rearrangement.tsv", @@ -49,7 +48,7 @@ loadContigs <- function(dir, "BD" = "Contigs_AIRR.tsv", "Omniscope" =c("_OSB.csv", "_OST.csv")) file.pattern <- format.list[[format]] - contig.files <- list.files(dir, paste0(file.pattern, collapse = "|"), recursive = TRUE, full.names = TRUE) + contig.files <- list.files(input, paste0(file.pattern, collapse = "|"), recursive = TRUE, full.names = TRUE) if (format %in% c("10X", "WAT3R", "Omniscope")) { df <- lapply(contig.files, read.csv) @@ -61,8 +60,8 @@ loadContigs <- function(dir, df <- lapply(contig.files, read.delim) } #Already loaded list or data frame - } else if (inherits(x=dir, what ="list") | inherits(x=dir, what ="data.frame")) { - df <- .checkList(dir) + } else if (inherits(x=input, what ="list") | inherits(x=input, what ="data.frame")) { + df <- .checkList(input) } loadFunc <- switch(format, diff --git a/R/percentAA.R b/R/percentAA.R index 385c065d..6d6d9a33 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -1,16 +1,18 @@ #' Examining the relative amino acid composition by position #' #' This function the proportion of amino acids along the residues -#' of the cdr3 amino acid sequence. +#' of the CDR3 amino acid sequence. #' #' @examples #' #Making combined contig data #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' percentAA(combined, chain = "TRB", aa.length = 20) +#' percentAA(combined, +#' chain = "TRB", +#' aa.length = 20) -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". #' @param group.by The variable to use for grouping. @@ -22,22 +24,22 @@ #' @export #' @concept Summarize_Repertoire #' @return ggplot of stacked bar graphs of amino acid proportions -percentAA <- function(df, - chain = "TRB", - group.by = NULL, - aa.length = 20, - exportTable = FALSE, - palette = "inferno") { +percentAA <- function(input.data, + chain = "TRB", + group.by = NULL, + aa.length = 20, + exportTable = FALSE, + palette = "inferno") { - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTaa", chain) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + input.data <- .data.wrangle(input.data, group.by, "CTaa", chain) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } res.list <- list() - for (i in seq_along(df)) { - strings <- df[[i]][,"CTaa"] + for (i in seq_along(input.data)) { + strings <- input.data[[i]][,"CTaa"] strings <- do.call(c,str_split(strings, ";")) strings <- strings[strings != "NA"] strings <- strings[nchar(strings) < aa.length] @@ -54,7 +56,7 @@ percentAA <- function(df, colnames(res) <- c("AA", paste0("pos.", seq_len(aa.length))) res[1:20,][is.na(res[1:20,])] <- 0 melt.res <- suppressMessages(melt(res)) - melt.res$group <- names(df)[i] + melt.res$group <- names(input.data)[i] res.list[[i]] <- melt.res } mat_melt <- do.call(rbind, res.list) diff --git a/R/percentGenes.R b/R/percentGenes.R index 7544b2bf..91c25650 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -8,9 +8,11 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' percentGenes(combined, chain = "TRB", gene = "Vgene") +#' percentGenes(combined, +#' chain = "TRB", +#' gene = "Vgene") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". #' @param gene "V", "D" or "J". @@ -24,17 +26,17 @@ #' @concept Summarize_Repertoire #' @return ggplot of percentage of indicated genes as a heatmap #' -percentGenes <- function(df, +percentGenes <- function(input.data, chain = "TRB", gene = "Vgene", group.by = NULL, exportTable = FALSE, palette = "inferno") { - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTgene", chain) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + input.data <- .data.wrangle(input.data, group.by, "CTgene", chain) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } #Parsing gene input if (gene %in% c("Vgene", "V", "v", "v.gene")) { @@ -49,7 +51,7 @@ percentGenes <- function(df, } } #Getting indicated genes across list - gene_counts <- lapply(df, function(x) { + gene_counts <- lapply(input.data, function(x) { tmp <- unlist(str_split(x[,"CTgene"], ";")) tmp <- str_split(tmp, "[.]", simplify = TRUE) diff --git a/R/percentKmer.R b/R/percentKmer.R index d5bd84ee..2d6e2ea8 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -8,9 +8,11 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' percentKmer(combined, chain = "TRB", motif.length = 3) +#' percentKmer(combined, +#' chain = "TRB", +#' motif.length = 3) -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". #' @param cloneCall How to call the clonotype - CDR3 nucleotide (nt) or @@ -28,7 +30,7 @@ #' @concept Summarize_Repertoire #' @return ggplot of percentage of kmers as a heatmap -percentKmer <- function(df, +percentKmer <- function(input.data, chain = "TRB", cloneCall = "aa", group.by = NULL, @@ -36,12 +38,19 @@ percentKmer <- function(df, top.motifs = 30, exportTable = FALSE, palette = "inferno") { + + if(cloneCall %!in% c("aa", "nt")) { + stop("Please select either nucleotide (nt) or amino acid (aa) sequences for cloneCall") + } motifs.to.save <- NULL - cloneCall <- .theCall(cloneCall) - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, cloneCall, chain) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + input.data <- .data.wrangle(input.data, + group.by, + .theCall(input.data, cloneCall, check.df = FALSE), + chain) + cloneCall <- .theCall(input.data, cloneCall) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } #Determining the function to generate motifs kmerFunc <- switch(cloneCall, @@ -50,15 +59,15 @@ percentKmer <- function(df, stop("Please select 'nt' or 'aa' for the cloneCall")) #Getting motifs and matrix to place the counts unique.motifs <- kmerFunc(motif.length) - mat <- matrix(NA, ncol = length(unique.motifs), nrow = length(df)) + mat <- matrix(NA, ncol = length(unique.motifs), nrow = length(input.data)) colnames(mat) <- unique.motifs - rownames(mat) <- names(df) + rownames(mat) <- names(input.data) #Looping through the counts of motifs - for(i in seq_along(df)) { - df[[i]][,cloneCall][which(df[[i]][,cloneCall] == "NA")] <- NA - df[[i]] <- df[[i]][!is.na(df[[i]][,cloneCall]),] - motifs <- .tokenize_multiple_sequences(df[[i]][,cloneCall], motif.length) + for(i in seq_along(input.data)) { + input.data[[i]][,cloneCall][which(input.data[[i]][,cloneCall] == "NA")] <- NA + input.data[[i]] <- input.data[[i]][!is.na(input.data[[i]][,cloneCall]),] + motifs <- .tokenize_multiple_sequences(input.data[[i]][,cloneCall], motif.length) motif.table <- table(unlist(motifs)) if(any(grepl("\\;", names(motif.table)))) { motif.table <- motif.table[!grepl("\\;", names(motif.table))] @@ -68,8 +77,8 @@ percentKmer <- function(df, mat[i,] <- mat[i,]/sum(na.omit(mat[i,])) } #Removing any column that is all NAs - if(any(colSums(is.na(mat)) == length(df))) { - mat <- mat[,-which(colSums(is.na(mat)) == length(df))] + if(any(colSums(is.na(mat)) == length(input.data))) { + mat <- mat[,-which(colSums(is.na(mat)) == length(input.data))] } mat[is.na(mat)] <- 0 diff --git a/R/percentVJ.R b/R/percentVJ.R index d6bcee44..f3e81714 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -10,7 +10,7 @@ #' "P19B","P19L", "P20B", "P20L")) #' percentVJ(combined, chain = "TRB") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL" #' @param group.by The variable to use for grouping. @@ -23,16 +23,16 @@ #' @concept Summarize_Repertoire #' @return ggplot of percentage of V and J gene pairings as a heatmap #' -percentVJ <- function(df, +percentVJ <- function(input.data, chain = "TRB", group.by = NULL, exportTable = FALSE, palette = "inferno") { - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, group.by, "CTgene", chain) + sco <- is_seurat_object(input.data) | is_se_object(input.data) + input.data <- .data.wrangle(input.data, group.by, "CTgene", chain) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } if(chain %in% c("TRA", "TRG", "IGL")) { @@ -42,7 +42,7 @@ percentVJ <- function(df, } #Getting indicated genes across list - gene_counts <- lapply(df, function(x) { + gene_counts <- lapply(input.data, function(x) { tmp <- unlist(str_split(x[,"CTgene"], ";")) tmp <- str_split(tmp, "[.]", simplify = TRUE) strings <- paste0(tmp[,positions[1]], ";", tmp[,positions[2]]) diff --git a/R/startracDiversity.R b/R/startracDiversity.R index fecb2fa2..9dbcfea5 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -17,7 +17,7 @@ #' #' #Getting a sample of a Seurat object #' scRep_example <- get(data("scRep_example")) -#' scRep_example <- combineExpression(combined, scRep_example ) +#' scRep_example <- combineExpression(combined, scRep_example) #' scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) #' scRep_example$Type <- substring(scRep_example$orig.ident,4,4) #' @@ -26,13 +26,12 @@ #' type = "Type", #' group.by = "Patient") #' -#' -#' @param sc The seurat or SCE object to visualize after combineExpression(). +#' @param sc.data The single-cell object after \code{\link{combineExpression}}. #' For SCE objects, the cluster variable must be in the meta data under #' "cluster". #' @param cloneCall How to call the clonotype - VDJC gene (gene), -#' CDR3 nucleotide (nt), CDR3 amino acid (aa), or -#' VDJC gene + CDR3 nucleotide (strict). +#' CDR3 nucleotide (nt), CDR3 amino acid (aa), +#' VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data. #' @param chain indicate if both or a specific chain should be used - #' e.g. "both", "TRA", "TRG", "IGH", "IGL". #' @param type The variable in the meta data that provides tissue type. @@ -45,7 +44,7 @@ #' @concept SC_Functions #' @return ggplot object of Startrac diversity metrics #' @author Liangtao Zheng -StartracDiversity <- function(sc, +StartracDiversity <- function(sc.data, cloneCall = "strict", chain = "both", type = NULL, @@ -53,8 +52,8 @@ StartracDiversity <- function(sc, exportTable = FALSE, palette = "inferno") { majorCluster <- NULL - cloneCall <- .theCall(cloneCall) - df <- .grabMeta(sc) + df <- .grabMeta(sc.data) + cloneCall <- .theCall(df, cloneCall) barcodes <- rownames(df) colnames(df)[ncol(df)] <- "majorCluster" @@ -66,7 +65,6 @@ StartracDiversity <- function(sc, } group.levels = unique(df[,group.by]) - cloneCall <- .theCall(cloneCall) if (chain != "both") { df <- .off.the.chain(df, chain, cloneCall) } diff --git a/R/subsetClone.R b/R/subsetClone.R index aec9323f..b0d1b720 100644 --- a/R/subsetClone.R +++ b/R/subsetClone.R @@ -5,32 +5,33 @@ #' by the name of the individual list element. #' #' @examples -#' x <- contig_list #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' subset <- subsetClones(combined, name = "sample", variables = c("P17B")) #' -#' @param df The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. +#' @param input.data The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. #' @param name The column header you'd like to use to subset. -#' @param variables The values to subset by, must be in the names(df). +#' @param variables The values to subset by, must be in the names(input.data). #' @export #' @concept Loading_and_Processing_Contigs #' @return list of contigs that have been filtered for the name parameter -subsetClones <- function(df, name, variables = NULL) { +subsetClones <- function(input.data, + name, + variables = NULL) { names2 <- NULL - df2 <- list() - for (i in seq_along(df)) { - if (df[[i]][1,name] %in% variables) { - df2 <- append(df2, list(df[[i]])) - n2 <- names(df)[i] + input.data2 <- list() + for (i in seq_along(input.data)) { + if (input.data[[i]][1,name] %in% variables) { + input.data2 <- append(input.data2, list(input.data[[i]])) + n2 <- names(input.data)[i] names2 <- c(names2, n2) } else { next() } } - names(df2) <- names2 - return(df2) + names(input.data2) <- names2 + return(input.data2) } \ No newline at end of file diff --git a/R/utils.R b/R/utils.R index fc7e6173..02310bb8 100644 --- a/R/utils.R +++ b/R/utils.R @@ -8,6 +8,7 @@ is_seurat_or_se_object <- function(obj) { #Use to shuffle between chains #' @keywords internal +#' @author Ye-Lin Son Nick Borcherding .off.the.chain <- function(dat, chain, cloneCall) { chain1 <- toupper(chain) #to just make it easier if (chain1 %in% c("TRA", "TRG", "IGH")) { @@ -18,6 +19,8 @@ is_seurat_or_se_object <- function(obj) { warning("It looks like ", chain, " does not match the available options for `chain = `") } dat[,cloneCall] <- str_split(dat[,cloneCall], "_", simplify = TRUE)[,x] + dat[,cloneCall][dat[,cloneCall] == "NA"] <- NA + dat[,cloneCall][dat[,cloneCall] == "NA;NA"] <- NA return(dat) } @@ -254,7 +257,7 @@ is_seurat_or_se_object <- function(obj) { # This is to help sort the type of clonotype data to use #' @keywords internal -.theCall <- function(x) { +.theCall <- function(df, x, check.df = TRUE) { x <- switch(x, "gene" = "CTgene", "genes" = "CTgene", @@ -268,6 +271,13 @@ is_seurat_or_se_object <- function(obj) { "strict" = "CTstrict", "gene+nt" = "CTstrict", "CTstrict" = "CTstrict") + if(check.df) { + if(inherits(df, "list") & !any(colnames(df[[1]]) %in% x)) { + stop("Check the clonal variabe (cloneCall) being used in the function, it does not appear in the data provided.") + } else if (inherits(df, "data.frame") & !any(colnames(df) %in% x)) { + stop("Check the clonal variabe (cloneCall) being used in the function, it does not appear in the data provided.") + } + } return(x) } @@ -399,8 +409,8 @@ is_seurat_or_se_object <- function(obj) { Con.df$CTgene <- paste(Con.df$TCR1, Con.df$TCR2, sep="_") Con.df$CTnt <- paste(Con.df$cdr3_nt1, Con.df$cdr3_nt2, sep="_") Con.df$CTaa <- paste(Con.df$cdr3_aa1, Con.df$cdr3_aa2, sep="_") - Con.df$CTstrict <- paste(Con.df$TCR1, Con.df$cdr3_nt1, - Con.df$TCR2, Con.df$cdr3_nt2, sep="_") + Con.df$CTstrict <- paste0(Con.df$TCR1, ";", Con.df$cdr3_nt1, "_", + Con.df$TCR2, ";", Con.df$cdr3_nt2) } else { # assume cellType = B Con.df$CTgene <- paste(Con.df$IGH, Con.df$IGLC, sep="_") Con.df$CTnt <- paste(Con.df$cdr3_nt1, Con.df$cdr3_nt2, sep="_") diff --git a/R/vizGenes.R b/R/vizGenes.R index 8d1221b9..b85ec5ce 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -17,7 +17,7 @@ #' y.axis = NULL, #' plot = "heatmap") #' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +#' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param plot The type of plot to return - heatmap or barplot #' @param x.axis Gene segments to separate the x-axis, such as "TRAV", "TRBD", "IGKJ". @@ -37,7 +37,7 @@ #' @concept Visualizing_Clones #' @return ggplot bar diagram or heatmap of gene usage -vizGenes <- function(df, +vizGenes <- function(input.data, x.axis = "TRBV", y.axis = NULL, group.by = NULL, @@ -47,7 +47,7 @@ vizGenes <- function(df, exportTable = FALSE, palette = "inferno") { element.names <- NULL - sco <- is_seurat_object(df) | is_se_object(df) + sco <- is_seurat_object(input.data) | is_se_object(input.data) #Extracting group.by in case of null if(!grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", y.axis) && !is.null(y.axis)) { @@ -58,25 +58,25 @@ vizGenes <- function(df, group.by <- "ident" } - df <- .list.input.return(df, split.by = group.by) + input.data <- .list.input.return(input.data, split.by = group.by) if(!is.null(group.by) & !sco) { - df <- .groupList(df, group.by) + input.data <- .groupList(input.data, group.by) } - df <- .bound.input.return(df) + input.data <- .bound.input.return(input.data) #Parsing x.axis if gene used - if (x.axis %!in% colnames(df)) { + if (x.axis %!in% colnames(input.data)) { if (grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", x.axis)) { - df <- .select.gene(df, x.axis, x.axis) - colnames(df)[ncol(df)] <- x.axis ######## Check if need this + input.data <- .select.gene(input.data, x.axis, x.axis) + colnames(input.data)[ncol(input.data)] <- x.axis ######## Check if need this } } #Parsing y.axis if gene used - if (any(y.axis %!in% colnames(df)) | !is.null(y.axis)) { + if (any(y.axis %!in% colnames(input.data)) | !is.null(y.axis)) { if (grepl("TRA|TRB|TRG|TRD|IGH|IGL|IGK", y.axis)) { - df <- .select.gene(df, y.axis, y.axis) - colnames(df)[ncol(df)] <- y.axis ######## Check if need this + input.data <- .select.gene(input.data, y.axis, y.axis) + colnames(input.data)[ncol(input.data)] <- y.axis ######## Check if need this } else { y.axis <- "element.names" } @@ -85,13 +85,13 @@ vizGenes <- function(df, } #Filtering steps - df[df == "NA"] <- NA - df[df == ""] <- NA - if(!is.null(y.axis) & any(is.na(df[,c(y.axis)]))) { - df <- subset(df, !is.na(df[,c(y.axis)])) + input.data[input.data == "NA"] <- NA + input.data[input.data == ""] <- NA + if(!is.null(y.axis) & any(is.na(input.data[,c(y.axis)]))) { + input.data <- subset(input.data, !is.na(input.data[,c(y.axis)])) } - if(!is.null(x.axis) & any(is.na(df[,c(x.axis)]))) { - df <- subset(df, !is.na(df[,c(x.axis)])) + if(!is.null(x.axis) & any(is.na(input.data[,c(x.axis)]))) { + input.data <- subset(input.data, !is.na(input.data[,c(x.axis)])) } if (!scale) { @@ -104,8 +104,8 @@ vizGenes <- function(df, #Calculating the summary values if (!is.null(y.axis) && y.axis != "element.names") { - mat <- df %>% - group_by(element.names, df[,x.axis], df[,y.axis]) %>% + mat <- input.data %>% + group_by(element.names, input.data[,x.axis], input.data[,y.axis]) %>% summarise(count = n()) %>% ungroup() %>% group_by(element.names) %>% @@ -123,8 +123,8 @@ vizGenes <- function(df, as.data.frame() } else { - mat <- df %>% - group_by(element.names, df[,x.axis]) %>% + mat <- input.data %>% + group_by(element.names, input.data[,x.axis]) %>% summarise(count = n()) %>% ungroup() %>% group_by(element.names) %>% @@ -180,11 +180,6 @@ vizGenes <- function(df, return(mat) } return(plot) - - #Need to test: - #Different chains - both heatmap and barplot - #grouped elements - both heatmap and barplot - } #Parsing the CTgene diff --git a/_pkgdown.yml b/_pkgdown.yml index e7e70e77..18dff81e 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -1,4 +1,4 @@ -url: https://ncborcherding.github.io/scRepertoire/ +url: https://www.borch.dev/uploads/scRepertoire/ Template: params: bootswatch: flatly @@ -35,7 +35,7 @@ navbar: href: articles/Clonal_Dynamics.html - text: Summarizing Repertoires href: articles/Repertoire_Summary.html - - text: Clonal Diversity and Overlap + - text: Clonal Diversity, Rarefaction, and Overlap href: articles/Clonal_Diversity.html - text: Clustering Sequences by Edit Distance href: articles/Clonal_Cluster.html @@ -85,14 +85,14 @@ reference: - clonalSizeDistribution - vizGenes - title: Summarizing Repertoire - desc: Functions to summarize clonal sequences across the repertoire + desc: Functions to summarize clonal sequences across the repertoire. - contents: - percentAA - percentGenes - percentKmer - percentVJ - title: Single-Cell Object Processing and Visualizations - desc: Functions to add or visualize clonal information along a single-cell object + desc: Functions to add or visualize clonal information along a single-cell object. - contents: - alluvialClones - clonalBias diff --git a/inst/pkgdown.yml b/inst/pkgdown.yml index abc352d5..32aaaca9 100644 --- a/inst/pkgdown.yml +++ b/inst/pkgdown.yml @@ -7,7 +7,6 @@ articles: Clonal_Cluster: Clonal_Cluster.html Clonal_Diversity: Clonal_Diversity.html Clonal_Dynamics: Clonal_Dynamics.html - Clonal_Rarefaction: Clonal_Rarefaction.html Clonal_Visualizations: Clonal_Visualizations.html Combining_Contigs: Combining_Contigs.html FAQ: FAQ.html @@ -18,9 +17,8 @@ articles: Repertoire_Summary: Repertoire_Summary.html SC_Visualizations: SC_Visualizations.html Trex: Trex.html - vignette: vignette.html -last_built: 2023-10-23T17:39Z +last_built: 2023-10-25T23:29Z urls: - reference: https://ncborcherding.github.io/scRepertoire/reference - article: https://ncborcherding.github.io/scRepertoire/articles + reference: https://www.borch.dev/uploads/scRepertoire/reference + article: https://www.borch.dev/uploads/scRepertoire/articles diff --git a/man/StartracDiversity.Rd b/man/StartracDiversity.Rd index 775ef8ea..82ffbfed 100644 --- a/man/StartracDiversity.Rd +++ b/man/StartracDiversity.Rd @@ -5,7 +5,7 @@ \title{Startrac-based diversity indices for single-cell RNA-seq} \usage{ StartracDiversity( - sc, + sc.data, cloneCall = "strict", chain = "both", type = NULL, @@ -15,13 +15,13 @@ StartracDiversity( ) } \arguments{ -\item{sc}{The seurat or SCE object to visualize after combineExpression(). +\item{sc.data}{The single-cell object after \code{\link{combineExpression}}. For SCE objects, the cluster variable must be in the meta data under "cluster".} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} @@ -55,7 +55,7 @@ combined <- combineTCR(contig_list, #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example")) -scRep_example <- combineExpression(combined, scRep_example ) +scRep_example <- combineExpression(combined, scRep_example) scRep_example$Patient <- substring(scRep_example$orig.ident,1,3) scRep_example$Type <- substring(scRep_example$orig.ident,4,4) @@ -64,7 +64,6 @@ StartracDiversity(scRep_example, type = "Type", group.by = "Patient") - } \author{ Liangtao Zheng diff --git a/man/addVariable.Rd b/man/addVariable.Rd index 88bf42fb..69a89aca 100644 --- a/man/addVariable.Rd +++ b/man/addVariable.Rd @@ -4,17 +4,17 @@ \alias{addVariable} \title{Adding variables after the combination of clonotypes.} \usage{ -addVariable(df, name = NULL, variables = NULL) +addVariable(input.data, variable.name = NULL, variables = NULL) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} +\item{input.data}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} -\item{name}{The column header to add.} +\item{variable.name}{The column header to add.} \item{variables}{The exact values to add to each element of the list.} } \value{ -list of contigs with a new column (name). +input.data list with the variable column added to each element. } \description{ This function adds variables to the product of \code{\link{combineTCR}}, @@ -25,11 +25,11 @@ the length of the combined object. } \examples{ combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", + samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) combined <- addVariable(combined, - name = "Type", - variables = rep(c("B", "L"), 4)) + variable.name = "Type", + variables = rep(c("B", "L"), 4)) } \concept{Loading_and_Processing_Contigs} diff --git a/man/alluvialClones.Rd b/man/alluvialClones.Rd index f36492d9..a2fea77a 100644 --- a/man/alluvialClones.Rd +++ b/man/alluvialClones.Rd @@ -5,7 +5,7 @@ \title{Exploring interaction of clonotypes by single-cell object dynamics} \usage{ alluvialClones( - sc, + sc.data, cloneCall = "strict", chain = "both", y.axes = NULL, @@ -17,11 +17,11 @@ alluvialClones( ) } \arguments{ -\item{sc}{The single-cell object to visualize after \code{\link{combineExpression}}.} +\item{sc.data}{The single-cell object to visualize after \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt) or CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} diff --git a/man/clonalAbundance.Rd b/man/clonalAbundance.Rd index 47a705d7..bf27d2d9 100644 --- a/man/clonalAbundance.Rd +++ b/man/clonalAbundance.Rd @@ -5,7 +5,7 @@ \title{Demonstrate the relative abundance of clonotypes by group or sample.} \usage{ clonalAbundance( - df, + input.data, cloneCall = "strict", chain = "both", scale = FALSE, @@ -16,12 +16,12 @@ clonalAbundance( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} @@ -43,19 +43,21 @@ ggplot of the total or relative abundance of clonotypes across quanta } \description{ -Displays the number of clonotypes at specific requencies by sample +Displays the number of clonotypes at specific Frequencies by sample or group. Visualization can either be a line graph using -calculated numbers or if scale = TRUE, the output will be a +calculated numbers or if \strong{scale} = TRUE, the output will be a density plot. Multiple sequencing runs can be group together using the group parameter. If a matrix output for the data is -preferred, set exportTable = TRUE. +preferred, set \strong{exportTable} = TRUE. } \examples{ #Making combined contig data combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -clonalAbundance(combined, cloneCall = "gene", scale = FALSE) +clonalAbundance(combined, + cloneCall = "gene", + scale = FALSE) } \concept{Visualizing_Clones} diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index a0ae13b1..780f1d5a 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -5,7 +5,7 @@ \title{Examine clonotype bias} \usage{ clonalBias( - df, + sc.data, cloneCall = "strict", split.by = NULL, group.by = NULL, @@ -16,11 +16,11 @@ clonalBias( ) } \arguments{ -\item{df}{The product of \code{\link{combineExpression}}.} +\item{sc.data}{The single-cell object after \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{split.by}{The variable to use for calculating the baseline frequencies. For example, "Type" for lung vs peripheral blood comparison} @@ -36,13 +36,13 @@ For example, "Type" for lung vs peripheral blood comparison} \item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ -Returns ggplot of the clonotype bias +ggplot scatter plot with clonotype bias } \description{ The metric seeks to quantify how individual clones are skewed towards -a specific cellular compartment or cluster. A clonotype bias of *1* - +a specific cellular compartment or cluster. A clonotype bias of \emph{1}* - indicates that a clonotype is composed of cells from a single -compartment or cluster, while a clonotype bias of *0* - matches the +compartment or cluster, while a clonotype bias of \emph{0} - matches the background subtype distribution. Please read and cite the following \href{https://pubmed.ncbi.nlm.nih.gov/35829695/}{manuscript} if using \code{\link{clonalBias}}. diff --git a/man/clonalCluster.Rd b/man/clonalCluster.Rd index e0064925..184307c9 100644 --- a/man/clonalCluster.Rd +++ b/man/clonalCluster.Rd @@ -5,7 +5,7 @@ \title{Clustering adaptive receptor sequences} \usage{ clonalCluster( - df, + input.data, chain = "TRB", sequence = "aa", samples = NULL, @@ -15,7 +15,7 @@ clonalCluster( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or \code{\link{combineExpression}}.} \item{chain}{Indicate if both or a specific chain should be used - @@ -42,7 +42,7 @@ This function uses edit distances of either the nucleotide or amino acid sequences of the CDR3 and V genes to cluster similar TCR/BCRs together. As a default, the function takes the input from \code{\link{combineTCR}}, \code{\link{combineBCR}} or \code{\link{combineExpression}} and amends a -cluster to the data frame or meta data. If **exportGraph** is set to TRUE, +cluster to the data frame or meta data. If \strong{exportGraph} is set to TRUE, the function returns an igraph object of the connected sequences. } \examples{ diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index ad95d7c8..6711bd00 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -5,7 +5,7 @@ \title{Demonstrate the difference in clonal proportion between clones} \usage{ clonalCompare( - df, + input.data, cloneCall = "strict", chain = "both", samples = NULL, @@ -20,13 +20,9 @@ clonalCompare( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} - \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} @@ -50,6 +46,10 @@ to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph.} \item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} + +\item{cloneCallHow}{to call the clonotype - VDJC gene (gene), +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} } \value{ ggplot of the proportion of total sequencing read of diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index 51d5295a..96afa07c 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -5,7 +5,7 @@ \title{Examine the clonal diversity of samples} \usage{ clonalDiversity( - df, + input.data, cloneCall = "strict", chain = "both", group.by = NULL, @@ -19,12 +19,12 @@ clonalDiversity( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} @@ -52,9 +52,9 @@ will automatically exportTable = TRUE.} ggplot of the diversity of clones by group } \description{ -This function calculates traditional measures of diversity - **Shannon**, -**inverse Simpson**, **normalized entropy**, **Gini-Simpson**, **Chao1 index**, and -**abundance-based coverage estimators (ACE)** measure of species evenness by sample or group. +This function calculates traditional measures of diversity - \strong{Shannon}, +\strong{inverse Simpson}, \strong{normalized entropy}, \strong{Gini-Simpson}, \strong{Chao1 index}, and +\strong{abundance-based coverage estimators (ACE)} measure of species evenness by sample or group. The function automatically down samples the diversity metrics using 100 boot straps The group parameter can be used to condense the individual samples. If a matrix output for the data is preferred, set exportTable = TRUE. @@ -62,22 +62,22 @@ samples. If a matrix output for the data is preferred, set exportTable = TRUE. \details{ The formulas for the indices and estimators are as follows: -Shannon Index: +\strong{Shannon Index:} \deqn{H = - \sum p_i \cdot \log(p_i)} -Inverse Simpson Index: +\strong{Inverse Simpson Index:} \deqn{ D^{-1} = 1 / \sum p_i^2} -Normalized Entropy: +\strong{Normalized Entropy:} \deqn{E^H = H / \log(S)} -Gini-Simpson Index: +\strong{Gini-Simpson Index:} \deqn{1 - D = 1 - \sum p_i^2} -Chao1 Index: +\strong{Chao1 Index:} \deqn{\hat{S}_{Chao1} = S + \left( \frac{n_1(n_1 - 1)}{2(n_2 + 1)} \right)} -Abundance-based Coverage Estimator (ACE): +\strong{Abundance-based Coverage Estimator (ACE):} \deqn{\hat{S}_{ACE} = S_{abundant} + \frac{S_{rare}}{C_{rare}} + \left( \frac{S_{rare} - 1}{C_{rare}} \right) \cdot F_1} Where: diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index 9b7adaa3..1c5ca0bc 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -5,7 +5,7 @@ \title{Examining the clonal homeostasis} \usage{ clonalHomeostasis( - df, + input.data, cloneSize = c(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1), cloneCall = "strict", @@ -16,14 +16,14 @@ clonalHomeostasis( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneSize}{The cutpoints of the proportions.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} @@ -44,7 +44,7 @@ The grouping of these clonotypes is based on the parameter cloneSize, at default, cloneSize will group the clonotypes into bins of Rare = 0 to 0.0001, Small = 0.0001 to 0.001, etc. To adjust the proportions, change the number or labeling of the cloneSize paramter. If a matrix -output for the data is preferred, set exportTable = TRUE. +output for the data is preferred, set \strong{exportTable} = TRUE. } \examples{ #Making combined contig data diff --git a/man/clonalLength.Rd b/man/clonalLength.Rd index b22dd7a3..0e054878 100644 --- a/man/clonalLength.Rd +++ b/man/clonalLength.Rd @@ -5,7 +5,7 @@ \title{Demonstrate the distribution of lengths filtered clones} \usage{ clonalLength( - df, + input.data, cloneCall = "aa", chain = "both", group.by = NULL, @@ -16,7 +16,7 @@ clonalLength( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - CDR3 nucleotide (nt), @@ -45,7 +45,7 @@ This function displays either the nucleotide (nt) or amino acid (aa) sequence length. The sequence length visualized can be selected using the chains parameter, either the combined clonotype (both chains) or across all single chains. Visualization can either -be a histogram or if scale = TRUE, the output will be a density plot. +be a histogram or if \strong{scale} = TRUE, the output will be a density plot. Multiple sequencing runs can be group together using the group parameter. } diff --git a/man/clonalNetwork.Rd b/man/clonalNetwork.Rd index 8729becf..ed749751 100644 --- a/man/clonalNetwork.Rd +++ b/man/clonalNetwork.Rd @@ -5,7 +5,7 @@ \title{Visualize clonal network along reduced dimensions} \usage{ clonalNetwork( - sc, + sc.data, reduction = "umap", group.by = "ident", filter.clones = NULL, @@ -20,7 +20,7 @@ clonalNetwork( ) } \arguments{ -\item{sc}{The single-cell object after \code{\link{combineExpression}}.} +\item{sc.data}{The single-cell object after \code{\link{combineExpression}}.} \item{reduction}{The name of the dimensional reduction of the single-cell object.} @@ -37,8 +37,8 @@ n of clones based on the minimum number of all the comparators (filter.clone = " allowing for cleaner visualization.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} diff --git a/man/clonalOccupy.Rd b/man/clonalOccupy.Rd index 251bbd23..2fdffaa0 100644 --- a/man/clonalOccupy.Rd +++ b/man/clonalOccupy.Rd @@ -5,7 +5,7 @@ \title{Visualize the number of single cells with cloneSizes by cluster} \usage{ clonalOccupy( - sc, + sc.data, x.axis = "ident", label = TRUE, facet.by = NULL, @@ -16,7 +16,7 @@ clonalOccupy( ) } \arguments{ -\item{sc}{The single-cell object after \code{\link{combineExpression}}.} +\item{sc.data}{The single-cell object after \code{\link{combineExpression}}.} \item{x.axis}{The variable in the meta data to graph along the x.axis.} @@ -37,9 +37,9 @@ environment in addition to the visualization.} Stacked bar plot of counts of cells by clonotype frequency group } \description{ -View the count of clonotypes frequency group in seurat or SCE object +View the count of clonotypes frequency group in Seurat or SCE object meta data after \code{\link{combineExpression}}. The visualization will take the -new meta data variable **"cloneSize"** and plot the number of cells with +new meta data variable \strong{"cloneSize"} and plot the number of cells with each designation using a secondary variable, like cluster. Credit to the idea goes to Drs. Carmona and Andreatta and their work with \href{https://github.com/carmonalab/ProjecTILs}{ProjectTIL}. diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 57de979b..0ec18bd3 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -5,7 +5,7 @@ \title{Examining the clonal overlap between groups or samples} \usage{ clonalOverlap( - df, + input.data, cloneCall = "strict", method = NULL, chain = "both", @@ -15,15 +15,15 @@ clonalOverlap( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} -\item{method}{The method to calculate the "overlap", -for the base numbers.} +\item{method}{The method to calculate the "overlap", "morisita", +"jaccard", "cosine" indices or "raw" for the base numbers.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} @@ -44,19 +44,19 @@ various overlap metrics for clonotypes. \details{ The formulas for the indices are as follows: -Overlap Coefficient: +\strong{Overlap Coefficient:} \deqn{overlap = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} -Raw Count Overlap: +\strong{Raw Count Overlap:} \deqn{raw = \sum \min(a_i, b_i)} -Morisita Index: +\strong{Morisita Index:} \deqn{morisita = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} -Jaccard Index: +\strong{Jaccard Index:} \deqn{jaccard = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} -Cosine Similarity: +\strong{Cosine Similarity:} \deqn{cosine = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} Where: @@ -71,7 +71,7 @@ combined <- combineTCR(contig_list, "P19B","P19L", "P20B", "P20L")) clonalOverlap(combined, - cloneCall = "gene", + cloneCall = "aa", method = "jaccard") } diff --git a/man/clonalOverlay.Rd b/man/clonalOverlay.Rd index 05480d03..285e686e 100644 --- a/man/clonalOverlay.Rd +++ b/man/clonalOverlay.Rd @@ -5,7 +5,7 @@ \title{Visualize distribution of clonal frequency overlaid on dimensional reduction plots} \usage{ clonalOverlay( - sc, + sc.data, reduction = NULL, freq.cutpoint = 30, bins = 25, @@ -13,7 +13,7 @@ clonalOverlay( ) } \arguments{ -\item{sc}{The single-cell object after \code{\link{combineExpression}}.} +\item{sc.data}{The single-cell object after \code{\link{combineExpression}}.} \item{reduction}{The dimensional reduction to visualize} diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index 7b940864..6a7a3032 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -5,7 +5,7 @@ \title{Examining the clonal space occupied by specific clonotypes} \usage{ clonalProportion( - df, + input.data, clonalSplit = c(10, 100, 1000, 10000, 30000, 1e+05), cloneCall = "strict", chain = "both", @@ -15,14 +15,14 @@ clonalProportion( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{clonalSplit}{The cut points for the specific clonotypes.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} @@ -40,7 +40,7 @@ ggplot of the space occupied by the specific rank of clonotypes \description{ This function calculates the relative clonal space occupied by the clonotypes. The grouping of these clonotypes is based on the parameter -**clonalSplit**, at default, **clonalSplit** will group the clonotypes +\strong{clonalSplit}, at default, \strong{clonalSplit} will group the clonotypes into bins of 1:10, 11:100, 101:1001, etc. To adjust the clonotypes selected, change the numbers in the variable split. If a matrix output for the data is preferred, set exportTable = TRUE. diff --git a/man/clonalQuant.Rd b/man/clonalQuant.Rd index 1bc97e7e..0e005afa 100644 --- a/man/clonalQuant.Rd +++ b/man/clonalQuant.Rd @@ -5,7 +5,7 @@ \title{Quantify the unique clonotypes} \usage{ clonalQuant( - df, + input.data, cloneCall = "strict", chain = "both", scale = FALSE, @@ -16,12 +16,12 @@ clonalQuant( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd index b06c543b..8ed06e79 100644 --- a/man/clonalRarefaction.Rd +++ b/man/clonalRarefaction.Rd @@ -5,7 +5,7 @@ \title{Calculate rarefaction based on the abundance of clones} \usage{ clonalRarefaction( - df, + input.data, cloneCall = "strict", chain = "both", group.by = NULL, @@ -17,12 +17,12 @@ clonalRarefaction( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} @@ -42,11 +42,11 @@ environment in addition to the visualization.} \item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \description{ -This functions uses the Hill numbers of order q: species richness (**q = 0&&), -Shannon diversity (**q = 1**, the exponential of Shannon entropy) and Simpson -diversity (**q = 2**, the inverse of Simpson concentration) to compute diversity -estimates for rarefaction and extrapolation. The function relies on -\code{\link[iNEXT]{iNEXT}}. Please read and cite the +This functions uses the Hill numbers of order q: species richness (\strong{q = 0}), +Shannon diversity (\strong{q = 1}), the exponential of Shannon entropy and Simpson +diversity (\strong{q = 2}, the inverse of Simpson concentration) to compute diversity +estimates for rarefaction and extrapolation. The function relies on the +\code{\link[iNEXT]{iNEXT}} R package. Please read and cite the \href{https://besjournals.onlinelibrary.wiley.com/doi/10.1111/2041-210X.12613}{manuscript} if using this function. } diff --git a/man/clonalScatter.Rd b/man/clonalScatter.Rd index 2999175d..a37708d9 100644 --- a/man/clonalScatter.Rd +++ b/man/clonalScatter.Rd @@ -5,7 +5,7 @@ \title{Scatter plot comparing the expansion of two samples} \usage{ clonalScatter( - df, + input.data, cloneCall = "strict", x.axis = NULL, y.axis = NULL, @@ -18,12 +18,12 @@ clonalScatter( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{x.axis}{name of the list element to appear on the x.axis.} @@ -44,7 +44,7 @@ use for size of dots.} \item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} } \value{ -ggplot of the relative clonotype numbers +ggplot of the relative clonotype numbers between two sequencing runs } \description{ This function produces a scatter plot directly comparing the specific clonotypes diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index db6c6730..7a608e52 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -5,7 +5,7 @@ \title{Hierarchical clustering of clones using Gamma-GPD spliced threshold model} \usage{ clonalSizeDistribution( - df, + input.data, cloneCall = "strict", chain = "both", method = "ward.D2", @@ -16,7 +16,7 @@ clonalSizeDistribution( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), @@ -47,7 +47,7 @@ model. If using this model please read and cite \href{https://pubmed.ncbi.nlm.nih.gov/30485278/}{PMID: 30485278}. } \details{ -The probability density function (pdf) for the Generalized Pareto Distribution (GPD) is given by: +The probability density function (pdf) for the \strong{Generalized Pareto Distribution (GPD)} is given by: \deqn{f(x|\mu, \sigma, \xi) = \frac{1}{\sigma} \left(1 + \xi \left( \frac{x - \mu}{\sigma} \right) \right)^{-\left( \frac{1}{\xi} + 1 \right)}}{ f(x|mu, sigma, xi) = 1/sigma * (1 + xi * ((x - mu)/sigma))^(-1/xi - 1) } @@ -60,7 +60,7 @@ Where: \item{\eqn{x \ge \mu} if \eqn{\xi \ge 0} and \eqn{\mu \le x \le \mu - \sigma/\xi} if \eqn{\xi < 0}} } -The probability density function (pdf) for the Gamma Distribution is given by: +The probability density function (pdf) for the \strong{Gamma Distribution} is given by: \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) } diff --git a/man/combineBCR.Rd b/man/combineBCR.Rd index b1fd8c19..5c2e3c41 100644 --- a/man/combineBCR.Rd +++ b/man/combineBCR.Rd @@ -5,7 +5,7 @@ \title{Combining the list of B cell receptor contigs into clonotypes} \usage{ combineBCR( - df, + input.data, samples = NULL, ID = NULL, call.related.clones = TRUE, @@ -16,7 +16,7 @@ combineBCR( ) } \arguments{ -\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContigs}}.} +\item{input.data}{List of filtered contig annotations or outputs from \code{\link{loadContigs}}.} \item{samples}{The labels of samples} @@ -44,14 +44,14 @@ of the individual cell barcodes. Using the samples and ID parameters, the function will add the strings as prefixes to prevent issues with repeated barcodes. The resulting new barcodes will need to match the Seurat or SCE object in order to use, \code{\link{combineExpression}}. -Unlike \code{\link{combineTCR}}, combineBCR produces a column **CTstrict** +Unlike \code{\link{combineTCR}}, combineBCR produces a column \strong{CTstrict} of an index of nucleotide sequence and the corresponding V gene. This index automatically calculates the Levenshtein distance between sequences with the same V gene and will index sequences using a normalized Levenshtein distance with the same ID. After which, clonotype clusters -are called using the \code{\link[igraph]{components}} function. Clonotype +are called using the \code{\link[igraph]{components}} function. Clones that are clustered across multiple sequences will then be labeled -with "LD" in the CTstrict header. +with "Cluster" in the CTstrict header. } \examples{ #Data derived from the 10x Genomics intratumoral NSCLC B cells diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index d51c858d..a2870ff4 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -5,11 +5,11 @@ \title{Adding clonotype information to a single-cell object} \usage{ combineExpression( - df, - sc, + input.data, + sc.data, cloneCall = "strict", chain = "both", - group.by = "none", + group.by = NULL, proportion = TRUE, filterNA = FALSE, cloneSize = c(Rare = 1e-04, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = @@ -18,14 +18,10 @@ combineExpression( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or a list of -both c(\code{\link{combineTCR}}, c\code{\link{combineBCR}})} +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} or a list of +both c(\code{\link{combineTCR}}, \code{\link{combineBCR}}).} -\item{sc}{The seurat or SingleCellExperiment (SCE) object to attach} - -\item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt) CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +\item{sc.data}{The Seurat or Single-Cell Experiment (SCE) object to attach} \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} @@ -47,17 +43,21 @@ on frequency, the upper limit of *cloneSizes* will be automatically amended.} \item{addLabel}{This will add a label to the frequency header, allowing the user to try multiple group.by variables or recalculate frequencies after subsetting the data.} + +\item{cloneCallHow}{to call the clonotype - VDJC gene (gene), +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} } \value{ -seurat or SingleCellExperiment object with attached clonotype +Single-cell object with clonotype information added to meta data information } \description{ This function adds the immune receptor information to the Seurat or SCE object to the meta data. By default this function also calculates the frequencies and proportion of the clonotypes by sequencing -run (**group.by** = "none"). To change how the frequencies/proportions -are calculated, select a column header for the **group.by** variable. +run (\strong{group.by} = NULL). To change how the frequencies/proportions +are calculated, select a column header for the \strong{group.by} variable. Importantly, before using \code{\link{combineExpression}} ensure the barcodes of the single-cell object object match the barcodes in the output of the \code{\link{combineTCR}} or \code{\link{combineBCR}}. @@ -70,11 +70,9 @@ combined <- combineTCR(contig_list, #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example")) -sce <- suppressMessages(Seurat::UpdateSeuratObject(scRep_example)) -sce <- Seurat::as.SingleCellExperiment(sce) #Using combineExpresion() -sce <- combineExpression(combined, sce) +scRep_example <- combineExpression(combined, scRep_example) } \concept{SC_Functions} diff --git a/man/combineTCR.Rd b/man/combineTCR.Rd index 9792296d..816b947f 100644 --- a/man/combineTCR.Rd +++ b/man/combineTCR.Rd @@ -5,7 +5,7 @@ \title{Combining the list of T cell receptor contigs into clonotypes} \usage{ combineTCR( - df, + input.data, samples = NULL, ID = NULL, removeNA = FALSE, @@ -14,7 +14,7 @@ combineTCR( ) } \arguments{ -\item{df}{List of filtered contig annotations or outputs from \code{\link{loadContigs}}} +\item{input.data}{List of filtered contig annotations or outputs from \code{\link{loadContigs}}} \item{samples}{The labels of samples} @@ -36,8 +36,8 @@ the level of the individual cell barcodes. Using the samples and ID parameters, the function will add the strings as prefixes to prevent issues with repeated barcodes. The resulting new barcodes will need to match the Seurat or SCE object in order to use, \code{\link{combineExpression}}. -Several levels of filtering exist - `removeNA`, `removeMulti`, or -`filterMulti` are parameters that control how the function deals with +Several levels of filtering exist - \emph{removeNA}, \emph{removeMulti}, or +\emph{filterMulti} are parameters that control how the function deals with barcodes with multiple chains recovered. } \examples{ diff --git a/man/contig_list.Rd b/man/contig_list.Rd index 79532b51..abebe7b5 100644 --- a/man/contig_list.Rd +++ b/man/contig_list.Rd @@ -3,10 +3,11 @@ \docType{data} \name{contig_list} \alias{contig_list} -\title{A data set of T cell contigs as a list outputed from the -filter_contig_annotation files.} +\title{A list of 8 single-cell T cell receptor sequences runs.} \description{ -A data set of T cell contigs as a list outputed from the -filter_contig_annotation files. +A list of 8 filtered_contig_annotation.csv files +outputted from 10X Cell Ranger. More information on the +data can be found in the following + \href{https://pubmed.ncbi.nlm.nih.gov/33622974/}{manuscript}. } \concept{Data} diff --git a/man/createHTOContigList.Rd b/man/createHTOContigList.Rd index a6dbd570..8f80715e 100644 --- a/man/createHTOContigList.Rd +++ b/man/createHTOContigList.Rd @@ -4,12 +4,12 @@ \alias{createHTOContigList} \title{Generate a contig list from a multiplexed experiment} \usage{ -createHTOContigList(contig, sc, group.by = NULL, multi.run = NULL) +createHTOContigList(contig, sc.data, group.by = NULL, multi.run = NULL) } \arguments{ \item{contig}{The filtered contig annotation file from multiplexed experiment} -\item{sc}{The Seurat or SCE object.} +\item{sc.data}{The Seurat or Single-Cell Experiment object.} \item{group.by}{One or more meta data headers to create the contig list based on. If more than one header listed, the function combines them into a single variable.} @@ -18,7 +18,7 @@ If more than one header listed, the function combines them into a single variabl that indicates the sequencing run.} } \value{ -Returns a list of contigs corresponding to the multiplexed single-cell object +Returns a list of contigs as input for \code{\link{combineBCR}} or \code{\link{combineTCR}} } \description{ This function reprocess and forms a list of contigs for downstream analysis @@ -26,7 +26,7 @@ in scRepertoire, \code{\link{createHTOContigList}} take the filtered contig annotation output and the single-cell RNA object to create the list. If using an integrated single-cell object, it is recommended to split the object by sequencing run and remove extra prefixes and suffixes on the barcode before using -\code{\link{createHTOContigList}}. Alternatively, the variable **multi.run** can +\code{\link{createHTOContigList}}. Alternatively, the variable \strong{multi.run} can be used to separate a list of contigs by a meta data variable. This may have issues with the repeated barcodes. } @@ -35,7 +35,7 @@ issues with the repeated barcodes. filtered.contig <- read.csv(".../Sample/outs/filtered_contig_annotations.csv") contig.list <- createHTOContigList(contig = filtered.contig, - sc = Seurat.Obj, + sc.data = Seurat.Obj, group.by = "HTO_maxID") } diff --git a/man/exportClones.Rd b/man/exportClones.Rd index d4fe4030..ea0496fd 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -5,7 +5,7 @@ \title{Exporting clonotypes} \usage{ exportClones( - df, + input.data, group.by = NULL, write.file = TRUE, dir = NULL, @@ -13,19 +13,19 @@ exportClones( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{group.by}{The variable to use for grouping.} -\item{write.file}{TRUE, save the file, FALSE, return a data.frame} +\item{write.file}{\strong{TRUE}, save the file or \strong{FALSE}, return a data.frame} \item{dir}{directory location to save the csv} \item{file.name}{the csv file name} } \value{ -ggplot of percentage of V and J gene pairings as a heatmap +CSV file of the paired sequences. } \description{ This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) diff --git a/man/getCirclize.Rd b/man/getCirclize.Rd index 2f31f2e1..3e4212c7 100644 --- a/man/getCirclize.Rd +++ b/man/getCirclize.Rd @@ -5,10 +5,10 @@ \title{Generate data frame to be used with circlize R package to visualize clonotypes as a chord diagram.} \usage{ -getCirclize(sc, cloneCall = "strict", group.by = NULL, proportion = FALSE) +getCirclize(sc.data, cloneCall = "strict", group.by = NULL, proportion = FALSE) } \arguments{ -\item{sc}{The single-cell object after \code{\link{combineExpression}}.} +\item{sc.data}{The single-cell object after \code{\link{combineExpression}}.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -28,7 +28,7 @@ data frame of shared clonotypes between groups This function will take the meta data from the product of \code{\link{combineExpression}} and generate a relational data frame to be used for a chord diagram. Each cord will represent the number of -clonotype unique and shared across the multiple **group.by** variable. +clonotype unique and shared across the multiple \strong{group.by} variable. If using the downstream circlize R package, please read and cite the following \href{https://pubmed.ncbi.nlm.nih.gov/24930139/}{manuscript}. If looking for more advance ways for circular visualizations, there @@ -43,7 +43,8 @@ combined <- combineTCR(contig_list, #Getting a sample of a Seurat object scRep_example <- get(data("scRep_example")) -scRep_example <- combineExpression(combined, scRep_example) +scRep_example <- combineExpression(combined, + scRep_example) #Getting data frame output for Circlize circles <- getCirclize(scRep_example, diff --git a/man/highlightClones.Rd b/man/highlightClones.Rd index 81f3d022..4caff655 100644 --- a/man/highlightClones.Rd +++ b/man/highlightClones.Rd @@ -5,17 +5,17 @@ \title{Highlighting specific clonotypes in Seurat} \usage{ highlightClones( - sc, + sc.data, cloneCall = c("gene", "nt", "aa", "strict"), sequence = NULL ) } \arguments{ -\item{sc}{The single-cell object to attach after \code{\link{combineExpression}}} +\item{sc.data}{The single-cell object to attach after \code{\link{combineExpression}}} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), or -VDJC gene + CDR3 nucleotide (strict).} +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} \item{sequence}{The specific sequence or sequence to highlight} } @@ -36,11 +36,13 @@ combined <- combineTCR(contig_list, scRep_example <- get(data("scRep_example")) #Using combineExpresion() -scRep_example <- combineExpression(combined, scRep_example) +scRep_example <- combineExpression(combined, + scRep_example) #Using highlightClonotype() -scRep_example <- highlightClones(scRep_example , cloneCall= "aa", -sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) +scRep_example <- highlightClones(scRep_example, + cloneCall= "aa", + sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) } \concept{SC_Functions} diff --git a/man/loadContigs.Rd b/man/loadContigs.Rd index c88643e7..6c8494b3 100644 --- a/man/loadContigs.Rd +++ b/man/loadContigs.Rd @@ -4,16 +4,16 @@ \alias{loadContigs} \title{Loading the contigs derived from single-cell sequencing} \usage{ -loadContigs(dir, format = "10X") +loadContigs(input, format = "10X") } \arguments{ -\item{dir}{The directory in which contigs are located or a list with contig elements} +\item{input}{The directory in which contigs are located or a list with contig elements} \item{format}{The format of the single-cell contig, currently supporting: "10X", "AIRR", "BD", "JSON", "MiXCR", "Omniscope", "TRUST4", and "WAT3R"} } \value{ -List of contigs for further processing in scRepertoire +List of contigs for compatibility with \code{\link{combineTCR}} or \code{\link{combineBCR}} } \description{ This function generates a contig list and formats the data to allow for @@ -35,7 +35,6 @@ The files that this function parses includes: } } \examples{ - TRUST4 <- read.csv("https://www.borch.dev/uploads/contigs/TRUST4_contigs.csv") contig.list <- loadContigs(TRUST4, format = "TRUST4") diff --git a/man/mini_contig_list.Rd b/man/mini_contig_list.Rd index c754c39a..f5269d53 100644 --- a/man/mini_contig_list.Rd +++ b/man/mini_contig_list.Rd @@ -11,9 +11,11 @@ An R `list` of `data.frame` objects data("mini_contig_list") } \description{ -A list of 8 dataframes of T cell contigs outputted from the +A list of 8 data frames of T cell contigs outputted from the `filtered_contig_annotation` files, but subsetted to 365 valid T cells -which correspond to the same barcodes found in `scRep_example` +which correspond to the same barcodes found in `scRep_example`. The +data is originally derived from the following + \href{https://pubmed.ncbi.nlm.nih.gov/33622974/}{manuscript}. } \seealso{ \code{\link{contig_list}} diff --git a/man/percentAA.Rd b/man/percentAA.Rd index cf481fc5..628f85ad 100644 --- a/man/percentAA.Rd +++ b/man/percentAA.Rd @@ -5,7 +5,7 @@ \title{Examining the relative amino acid composition by position} \usage{ percentAA( - df, + input.data, chain = "TRB", group.by = NULL, aa.length = 20, @@ -14,7 +14,7 @@ percentAA( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL".} @@ -32,13 +32,15 @@ ggplot of stacked bar graphs of amino acid proportions } \description{ This function the proportion of amino acids along the residues -of the cdr3 amino acid sequence. +of the CDR3 amino acid sequence. } \examples{ #Making combined contig data combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -percentAA(combined, chain = "TRB", aa.length = 20) +percentAA(combined, + chain = "TRB", + aa.length = 20) } \concept{Summarize_Repertoire} diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd index d2b9a61b..7584ed95 100644 --- a/man/percentGenes.Rd +++ b/man/percentGenes.Rd @@ -5,7 +5,7 @@ \title{Examining the V or J gene usage across groupings} \usage{ percentGenes( - df, + input.data, chain = "TRB", gene = "Vgene", group.by = NULL, @@ -14,7 +14,7 @@ percentGenes( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL".} @@ -39,7 +39,9 @@ grouping variables combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -percentGenes(combined, chain = "TRB", gene = "Vgene") +percentGenes(combined, + chain = "TRB", + gene = "Vgene") } \concept{Summarize_Repertoire} diff --git a/man/percentKmer.Rd b/man/percentKmer.Rd index 6f91ce90..fee9fcd7 100644 --- a/man/percentKmer.Rd +++ b/man/percentKmer.Rd @@ -5,7 +5,7 @@ \title{Examining the relative composition of kmer motifs} \usage{ percentKmer( - df, + input.data, chain = "TRB", cloneCall = "aa", group.by = NULL, @@ -16,7 +16,7 @@ percentKmer( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL".} @@ -47,6 +47,8 @@ in the CDR3 sequence. combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -percentKmer(combined, chain = "TRB", motif.length = 3) +percentKmer(combined, + chain = "TRB", + motif.length = 3) } \concept{Summarize_Repertoire} diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd index 7e765ef1..50c4a8c6 100644 --- a/man/percentVJ.Rd +++ b/man/percentVJ.Rd @@ -5,7 +5,7 @@ \title{Examining the V or J gene usage across groupings} \usage{ percentVJ( - df, + input.data, chain = "TRB", group.by = NULL, exportTable = FALSE, @@ -13,7 +13,7 @@ percentVJ( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{chain}{"TRA", "TRB", "TRG", "TRG", "IGH", "IGL"} diff --git a/man/screp_example.Rd b/man/screp_example.Rd index d0923146..944f7deb 100644 --- a/man/screp_example.Rd +++ b/man/screp_example.Rd @@ -3,10 +3,13 @@ \docType{data} \name{scRep_example} \alias{scRep_example} -\title{A seurat object of 500 single T cells derived -from 4 patients with acute respiratory distress (ARDS).} +\title{A Seurat object of 500 single T cells,} \description{ The object is compatible with `contig_list` and the TCR -sequencing data can be added with `combineExpression`. +sequencing data can be added with `combineExpression`. The data is +from 4 patients with acute respiratory distress, with samples taken +from both the lung and peripheral blood. More information on the +data can be found in the following + \href{https://pubmed.ncbi.nlm.nih.gov/33622974/}{manuscript}. } \concept{Data} diff --git a/man/subsetClones.Rd b/man/subsetClones.Rd index 05f7bb31..d4b81efb 100644 --- a/man/subsetClones.Rd +++ b/man/subsetClones.Rd @@ -4,14 +4,14 @@ \alias{subsetClones} \title{Subset the product of combineTCR() or combineBCR()} \usage{ -subsetClones(df, name, variables = NULL) +subsetClones(input.data, name, variables = NULL) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} +\item{input.data}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} \item{name}{The column header you'd like to use to subset.} -\item{variables}{The values to subset by, must be in the names(df).} +\item{variables}{The values to subset by, must be in the names(input.data).} } \value{ list of contigs that have been filtered for the name parameter @@ -22,7 +22,6 @@ This function allows for the subsetting of the product of by the name of the individual list element. } \examples{ -x <- contig_list combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index b555119d..ff345b69 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -5,7 +5,7 @@ \title{Visualizing the distribution of gene usage} \usage{ vizGenes( - df, + input.data, x.axis = "TRBV", y.axis = NULL, group.by = NULL, @@ -17,7 +17,7 @@ vizGenes( ) } \arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or +\item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} \item{x.axis}{Gene segments to separate the x-axis, such as "TRAV", "TRBD", "IGKJ".} diff --git a/vignettes/articles/Attaching_SC.Rmd b/vignettes/articles/Attaching_SC.Rmd index 4aa69491..7e5608ce 100644 --- a/vignettes/articles/Attaching_SC.Rmd +++ b/vignettes/articles/Attaching_SC.Rmd @@ -76,10 +76,10 @@ We can check the default cloneSize groupings using the Single-Cell Experiment ob ```{r} sce <- combineExpression(combined.TCR, - sce, - cloneCall="gene", - group.by = "sample", - proportion = TRUE) + sce, + cloneCall="gene", + group.by = "sample", + proportion = TRUE) #Define color palette colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) diff --git a/vignettes/articles/Clonal_Cluster.Rmd b/vignettes/articles/Clonal_Cluster.Rmd index 8ba8723a..bb79f4ef 100644 --- a/vignettes/articles/Clonal_Cluster.Rmd +++ b/vignettes/articles/Clonal_Cluster.Rmd @@ -110,11 +110,11 @@ Using ```clonalCluster()```, we can also return an igraph object of all the rela ```{r} #Clustering Patient 19 samples igraph.object <- clonalCluster(combined.TCR[c(5,6)], - chain = "TRB", - sequence = "aa", - group.by = "sample", - threshold = 0.85, - exportGraph = TRUE) + chain = "TRB", + sequence = "aa", + group.by = "sample", + threshold = 0.85, + exportGraph = TRUE) #Setting color scheme col_legend <- factor(igraph::V(igraph.object)$group) diff --git a/vignettes/articles/Clonal_Diversity.Rmd b/vignettes/articles/Clonal_Diversity.Rmd index 25d2f268..84595966 100644 --- a/vignettes/articles/Clonal_Diversity.Rmd +++ b/vignettes/articles/Clonal_Diversity.Rmd @@ -1,11 +1,11 @@ --- -title: "Comparing Clonal Diversity" +title: "Comparing Clonal Diversity and Overlap" date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' output: rmarkdown::html_vignette theme: united df_print: kable vignette: > - %\VignetteIndexEntry{Comparing Clonal Diversity} + %\VignetteIndexEntry{Comparing Clonal Diversity and Overlap} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- @@ -73,6 +73,59 @@ clonalDiversity(combined.TCR, cloneCall = "gene", n.boots = 20) ``` +## clonaRarefaction + +We can also use Hill numbers to estimate the rarefaction, or estimating species richness, using the the abundance of clones across groupings. Underlying the rarefaction calculation is the use of observed receptor of abundance to compute diversity. + +**hill.numbers** ++ 0 - species-richness ++ 1 - Shannon ++ 2 - Simpson + +**plot.type** ++ 1 - sample-size-based rarefaction/extrapolation ++ 2 - sample completeness curve ++ 3 - coverage-based rarefaction/extrapolation curve + +This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). Like the other wrapping functions in scRepetoire, please cite the original work. + +### Rarefaction using Shannon Diversity (q = 0) + +```{r, message=FALSE} +clonalRarefaction(combined.TCR, + plot.type = 1, + hill.numbers = 0, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 2, + hill.numbers = 0, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 3, + hill.numbers = 0, + n.boots = 2) +``` + +### Rarefaction using Shannon Diversity (q = 1) + +```{r} +clonalRarefaction(combined.TCR, + plot.type = 1, + hill.numbers = 1, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 2, + hill.numbers = 1, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 3, + hill.numbers = 1, + n.boots = 2) +``` ## clonalSizeDistribution @@ -90,28 +143,6 @@ clonalSizeDistribution(combined.TCR, If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. -The methods for calculating overlap are described below: - -Overlap Coefficient: -\deqn{overlap = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} - -Raw Count Overlap: -\deqn{raw = \sum \min(a_i, b_i)} - -Morisita Index: -\deqn{morisita = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} - -Jaccard Index: -\deqn{jaccard = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} - -Cosine Similarity: -\deqn{cosine = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} - -Where: -\itemize{ -\item{\eqn{a_i} and \eqn{b_i} are the abundances of species \eqn{i} in groups A and B, respectively.} -} - ```{r} clonalOverlap(combined.TCR, cloneCall = "strict", diff --git a/vignettes/articles/Clonal_Rarefaction.Rmd b/vignettes/articles/Clonal_Rarefaction.Rmd deleted file mode 100644 index 18a9762f..00000000 --- a/vignettes/articles/Clonal_Rarefaction.Rmd +++ /dev/null @@ -1,120 +0,0 @@ ---- -title: "Clonal Rarefaction and Extrapolation" -date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' -output: rmarkdown::html_vignette -theme: united -df_print: kable -vignette: > - %\VignetteIndexEntry{Clonal Rarefaction and Extrapolation} - %\VignetteEngine{knitr::rmarkdown} - %\VignetteEncoding{UTF-8} ---- -*** - - - -```{r setup, include=FALSE} -all_times <- list() # store the time for each chunk -knitr::knit_hooks$set(time_it = local({ - now <- NULL - function(before, options) { - if (before) { - now <<- Sys.time() - } else { - res <- difftime(Sys.time(), now, units = "secs") - all_times[[options$label]] <<- res - } - } -})) -knitr::opts_chunk$set( - tidy = TRUE, - tidy.opts = list(width.cutoff = 95), - message = FALSE, - warning = FALSE, - time_it = TRUE -) - -suppressMessages(library(scRepertoire)) -data("contig_list") -combined.TCR <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) -``` - -## clonaRarefaction - -We can also use Hill numbers to estimate the rarefaction, or estimating species richness, using the the abundance of clones across groupings. Underlying the rarefaction calculation is the use of observed receptor of abundance to compute diversity. - -**hill.numbers** -+ 0 - species-richness -+ 1 - Shannon -+ 2 - Simpson - -**plot.type** -+ 1 - sample-size-based rarefaction/extrapolation -+ 2 - sample completeness curve -+ 3 - coverage-based rarefaction/extrapolation curve - -This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). Like the other wrapping functions in scRepetoire, please cite the original work. - -### Rarefaction using Shannon Diversity (q = 0) - -```{r, message=FALSE} -clonalRarefaction(combined.TCR, - plot.type = 1, - hill.numbers = 0, - n.boots = 2) - -clonalRarefaction(combined.TCR, - plot.type = 2, - hill.numbers = 0, - n.boots = 2) - -clonalRarefaction(combined.TCR, - plot.type = 3, - hill.numbers = 0, - n.boots = 2) -``` - - -### Rarefaction using Shannon Diversity (q = 1) - -```{r} -clonalRarefaction(combined.TCR, - plot.type = 1, - hill.numbers = 1, - n.boots = 2) - -clonalRarefaction(combined.TCR, - plot.type = 2, - hill.numbers = 1, - n.boots = 2) - -clonalRarefaction(combined.TCR, - plot.type = 3, - hill.numbers = 1, - n.boots = 2) -``` - -### Rarefaction using Simpson Diversity (q = 2) - -```{r} -clonalRarefaction(combined.TCR, - plot.type = 1, - hill.numbers = 2, - n.boots = 2) - -clonalRarefaction(combined.TCR, - plot.type = 2, - hill.numbers = 2, - n.boots = 2) - -clonalRarefaction(combined.TCR, - plot.type = 3, - hill.numbers = 2, - n.boots = 2) -``` diff --git a/vignettes/articles/Clonal_Visualizations.Rmd b/vignettes/articles/Clonal_Visualizations.Rmd index 0c621144..b91a9e3c 100644 --- a/vignettes/articles/Clonal_Visualizations.Rmd +++ b/vignettes/articles/Clonal_Visualizations.Rmd @@ -44,8 +44,8 @@ combined.TCR <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) combined.TCR <- addVariable(combined.TCR, - name = "Type", - variables = rep(c("B", "L"), 4)) + variable.name = "Type", + variables = rep(c("B", "L"), 4)) ``` ## clonalQuant @@ -67,35 +67,7 @@ clonalQuant(combined.TCR, scale = TRUE) ``` -For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme: -+ Change the **palette** parameter to the desired color scheme. This approach uses the built-in palettes of grDevices and we can access the list of color schemes using ``hcl.pals()``. -+ Add a ggplot layer with a new color scheme. - -```{r} -clonalQuant(combined.TCR, - cloneCall="strict", - chain = "both", - scale = TRUE, - palette = "Zissou 1") - -clonalQuant(combined.TCR, - cloneCall="strict", - chain = "both", - scale = TRUE) + - scale_fill_manual(values = hcl.colors(8,"geyser")) -``` - -Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph instead of the visual output. - -```{r} -clonalQuant_output <- clonalQuant(combined.TCR, - cloneCall="strict", - scale = TRUE, - exportTable = TRUE) -clonalQuant_output -``` - -Another option here is to be able to define the visualization by data classes. Here, we used the `combineTCR()` to define the **Type** variable as part of the naming structure. We can use the **group.by** to specifically use a column in the data set to organize the visualization. +Another option here is to be able to define the visualization by data classes. Here, we used the `combineTCR()` list to define the **Type** variable as part of the naming structure. We can use the **group.by** to specifically use a column in the data set to organize the visualization. ```{r} clonalQuant(combined.TCR, diff --git a/vignettes/articles/Combining_Contigs.Rmd b/vignettes/articles/Combining_Contigs.Rmd index a356b19c..34b2138f 100644 --- a/vignettes/articles/Combining_Contigs.Rmd +++ b/vignettes/articles/Combining_Contigs.Rmd @@ -42,13 +42,13 @@ suppressMessages(library(scRepertoire)) data("contig_list") ``` -There are varying definitions of clones or clonotypes in the literature. For the purposes of scRepertoire, we will use **clone** and define this as the cells with shared/trackable complementarity-determining region 3 (CDR3) sequences. Within this definition, one might use amino acid (**aa**) sequences of one or both chains to define a clone. Alternatively, we could use nucleotide (**nt**) or the V(D)JC genes (**genes**) to define a clone. The latter **genes** would be a more permissive definition of *"clones"*, as multiple amino acid or nucleotide sequences can results from the same gene combination. Another option to define clone is use of the V(D)JC and nucleotide sequence (**strict**). scRepertoire allows for the use of all these definitions of clones and allows for users to select both or individual chains to examine. +There are varying definitions of clones or clonotypes in the literature. For the purposes of scRepertoire, we will use **clone** and define this as the cells with shared/trackable complementarity-determining region 3 (CDR3) sequences. Within this definition, one might use amino acid (**aa**) sequences of one or both chains to define a clone. Alternatively, we could use nucleotide (**nt**) or the V(D)JC genes (**genes**) to define a clone. The latter **genes** would be a more permissive definition of *"clones"*, as multiple amino acid or nucleotide sequences can result from the same gene combination. Another option to define clone is the use of the V(D)JC and nucleotide sequence (**strict**). scRepertoire allows for the use of all these definitions of clones and allows for users to select both or individual chains to examine. The first step in getting clones is to use the single-cell barcodes to organize cells into paired sequences. This is accomplished using `combineTCR()` and `combineBCR()`. ## combineTCR -**df** +**input.data** * List of *filtered_contig_annotations.csv* data frames from the 10x Cell Ranger. * List of data processed using `loadContigs()`. diff --git a/vignettes/articles/FAQ.Rmd b/vignettes/articles/FAQ.Rmd index fdf45ab4..4a7f9c9a 100644 --- a/vignettes/articles/FAQ.Rmd +++ b/vignettes/articles/FAQ.Rmd @@ -37,6 +37,12 @@ knitr::opts_chunk$set( warning = FALSE, time_it = TRUE ) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) ``` ### Barcode Mismatch @@ -64,16 +70,45 @@ cell.barcodes <- paste0(seuratObj$orig.ident, "_", cell.barcodes) #This is addin seuratObj <- RenameCells(seuratObj, new.names = cell.barcodes) ``` +### Adjusting Color Palettes + +For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme: ++ Change the **palette** parameter to the desired color scheme. This approach uses the built-in palettes of grDevices and we can access the list of color schemes using ``hcl.pals()``. ++ Add a ggplot layer with a new color scheme. + +```{r} +clonalQuant(combined.TCR, + cloneCall="strict", + chain = "both", + scale = TRUE, + palette = "Zissou 1") + +clonalQuant(combined.TCR, + cloneCall="strict", + chain = "both", + scale = TRUE) + + scale_fill_manual(values = hcl.colors(8,"geyser")) +``` + +### Getting Data Used in Plots + +Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph instead of the visual output. + +```{r} +clonalQuant_output <- clonalQuant(combined.TCR, + cloneCall="strict", + scale = TRUE, + exportTable = TRUE) +clonalQuant_output +``` + ### Citing scRepertoire ***Borcherding, N. & Wang, Q. scRepertoire v2: Expanded toolkit for single-cell immune repertoire analysis*** ### Bug Reports/New Features -##### [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) - -If possible please include a [reproducible example](https://reprex.tidyverse.org/). -Alternatively, an example with the internal **scRep_example** and **contig_list** would +Submit a [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) - if possible please include a [reproducible example](https://reprex.tidyverse.org/). Alternatively, an example with the internal **scRep_example** and **contig_list** would be extremely helpful. ### Features or Enhancements Requests diff --git a/vignettes/articles/Installation.Rmd b/vignettes/articles/Installation.Rmd index c84a6089..b7a867f6 100644 --- a/vignettes/articles/Installation.Rmd +++ b/vignettes/articles/Installation.Rmd @@ -39,9 +39,9 @@ knitr::opts_chunk$set( ) ``` -sscRepertoire requires [R](https://www.r-project.org/) version 4.0 or greater. Highly recommend installing [R Studio](https://posit.co/downloads/). +scRepertoire requires [R](https://www.r-project.org/) version 4.0 or greater. Highly recommend installing [R Studio](https://posit.co/downloads/). -## Install from CRAN +## Install from Bioconductor scRepertoire can be installed from [Bioconductor](https://www.bioconductor.org/packages/release/bioc/html/scRepertoire.html) for all platforms using standard installation methods: ``` if (!require("BiocManager", quietly = TRUE)) @@ -51,11 +51,11 @@ BiocManager::install("scRepertoire") ``` ## Release Notes -A full copy of the changes in each version can be found in the [NEWS/ChangeLog](https://ncborcherding.github.io/scRepertoire/news/index.html). +A full copy of the changes in each version can be found in the [NEWS/ChangeLog](https://borch.dev/uploads/scRepertoire/news/index.html). ## GitHub Branches -scRepertoire is an active project, that recieves a number of changes based on user feedback, the most up-to-date versions are available via the GitHub repositories. These versions can be installed using ```devtools::install_github()``` or ```remotes::install_github()```. The **dev** version is the active development branch and might be less stable than the main branch or bioconductor versions. +scRepertoire is an active project, that receives a number of changes based on user feedback, the most up-to-date versions are available via the GitHub repositories. These versions can be installed using ```devtools::install_github()``` or ```remotes::install_github()```. The **dev** version is the active development branch and might be less stable than the main branch or bioconductor versions. ``` devtools::install_github(repo = "ncborcherding/scRepertoire") diff --git a/vignettes/articles/Loading.Rmd b/vignettes/articles/Loading.Rmd index ca7c7568..ba058b36 100644 --- a/vignettes/articles/Loading.Rmd +++ b/vignettes/articles/Loading.Rmd @@ -74,7 +74,7 @@ Beyond the default 10x Genomic Cell Ranger pipeline outputs, scRepertoire suppor ```{r, eval=FALSE} #Directory example contig.output <- c("~/Documents/MyExperiment") -contig.list <- loadContigs(dir = contig.output, +contig.list <- loadContigs(input = contig.output, format = "TRUST4") #List of data frames example @@ -84,7 +84,7 @@ S3 <- read.csv("~/Documents/MyExperiment/Sample3/outs/barcode_results.csv") S4 <- read.csv("~/Documents/MyExperiment/Sample4/outs/barcode_results.csv") contig_list <- list(S1, S2, S3, S4) -contig.list <- loadContigs(dir = contig.output, +contig.list <- loadContigs(input = contig.output, format = "WAT3R") ``` @@ -97,7 +97,9 @@ This function depends on the match of barcodes between the single-cell object an ```{r, eval = F} contigs <- read.csv(".../outs/filtered_contig_annotations.csv") -contig.list <- createHTOContigList(contigs, Seurat.Obj, group.by = "HTO_maxID") +contig.list <- createHTOContigList(contigs, + Seurat.Obj, + group.by = "HTO_maxID") ``` ## Example Data in scRepertoire diff --git a/vignettes/articles/Processing.Rmd b/vignettes/articles/Processing.Rmd index 344596f7..5b163c36 100644 --- a/vignettes/articles/Processing.Rmd +++ b/vignettes/articles/Processing.Rmd @@ -47,14 +47,14 @@ combined.TCR <- combineTCR(contig_list, ## addVariable -What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. +What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **variable.name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. ```{r} -combined <- addVariable(combined.TCR, - name = "Type", - variables = rep(c("B", "L"), 4)) +combined.TCR <- addVariable(combined.TCR, + variable.name = "Type", + variables = rep(c("B", "L"), 4)) -combined[[1]][1:5,ncol(combined.TCR[[1]])] # This is showing the first 5 values of the new column added +head(combined.TCR[[1]]) ``` ## subsetClones @@ -71,8 +71,8 @@ subset1 <- subsetClones(combined.TCR, #Subset using list elements subset2 <- combined.TCR[c(3,4)] -head(subset1) -head(subset2) +head(subset1[[1]]) +head(subset2[[1]]) ``` diff --git a/vignettes/articles/Repertoire_Summary.Rmd b/vignettes/articles/Repertoire_Summary.Rmd index d62fa562..28ed19c0 100644 --- a/vignettes/articles/Repertoire_Summary.Rmd +++ b/vignettes/articles/Repertoire_Summary.Rmd @@ -1,11 +1,11 @@ --- -title: "Basic Clonal Visualizations" +title: "Summarizing Repertoires" date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' output: rmarkdown::html_vignette theme: united df_print: kable vignette: > - %\VignetteIndexEntry{Basic Clonal Visualizations} + %\VignetteIndexEntry{Summarizing Repertoires} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 41adc173..1a0444ad 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -128,12 +128,12 @@ The output of `combineTCR()` will be a list of contig data frames that will be r ## addVariable -What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. +What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **variable.name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. ```{r} combined <- addVariable(combined, - name = "Type", - variables = rep(c("B", "L"), 4)) + variable.name = "Type", + variables = rep(c("B", "L"), 4)) combined[[1]][1:5,ncol(combined[[1]])] # This is showing the first 5 values of the new column added ``` From b76dc9bfd238e43302f34fb93ede029d625f216f Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 26 Oct 2023 09:26:41 -0500 Subject: [PATCH 102/142] exportClones added support --- R/combineContigs.R | 2 +- R/exportClones.R | 150 +++++++++++++++++++++++++++++++++++---- R/global.R | 3 + R/percentAA.R | 4 +- man/clonalCompare.Rd | 8 +-- man/combineExpression.Rd | 8 +-- man/exportClones.Rd | 11 ++- 7 files changed, 159 insertions(+), 27 deletions(-) diff --git a/R/combineContigs.R b/R/combineContigs.R index bfc8cbe8..d8136681 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -231,7 +231,7 @@ combineBCR <- function(input.data, } final[[i]]$sample <- samples[i] final[[i]]$ID <- ID[i] - final[[i]][final[[i]] == "NA_NA" | final[[i]] == "NA;NA_NA;NA" | ] <- NA + final[[i]][final[[i]] == "NA_NA" | final[[i]] == "NA;NA_NA;NA"] <- NA if (!is.null(sample) & !is.null(ID)) { final[[i]]<- final[[i]][, c("barcode", "sample", "ID", heavy_lines[c(1,2,3)], light_lines[c(1,2,3)], CT_lines)] diff --git a/R/exportClones.R b/R/exportClones.R index 22aa3534..48825669 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -1,7 +1,10 @@ #' Exporting clonotypes #' #' This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) -#' by barcodes. +#' by barcodes. \strong{format} determines the structure of the csv file - \emph{paired} will +#' export sequences by barcodes and include multiple chians, \emph{airr} will export a data +#' frame that is consistent with the AIRR format, and \emph{TCRMatch} will +#' export a data frame that has the TRB chain with count information. #' #' @examples #' \dontrun{ @@ -9,11 +12,13 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' exportClones(combined) +#' exportClones(combined, +#' format = "paired") #' } #' #' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. +#' @param format The format to export the clones - "paired", "airr", or "TCRMatch". #' @param group.by The variable to use for grouping. #' @param write.file \strong{TRUE}, save the file or \strong{FALSE}, return a data.frame #' @param dir directory location to save the csv @@ -26,19 +31,65 @@ #' @return CSV file of the paired sequences. #' @author Jonathan Noonan, Nick Borcherding exportClones <- function(input.data, + format = "paired", group.by = NULL, write.file = TRUE, dir = NULL, file.name = "clones.csv") { - input.data <- .data.wrangle(input.data, group.by, "CTgene", "both") + summaryFunc <- switch(format, + "paired" = .PairedExport, + "airr" = .AIRRExport, + "TCRMatch" = .TCRmatchExport, + stop("Invalid format provided")) + mat <- summaryFunc(input.data) + + mat[mat == "NA"] <- NA + if(!write.file) { + return(mat) + } + + if(is.null(dir)) { + dir <- "./" + } + filepath <- paste0(dir, file.name) + write.csv(mat, file = filepath) +} + +.TCRmatchExport<- function(input.data) { + input.data <- .data.wrangle(input.data, group.by, "CTgene", "TRB") + + for(i in seq_along(input.data)) { + input.data[[i]] <- .off.the.chain(input.data[[i]], "TRB", "CTaa") + input.data[[i]] <- .off.the.chain(input.data[[i]], "TRB", "CTnt") + } + + input.data <- bind_rows(input.data, .id = "group") + #Remove cells without TRB + if(any(c("IGH", "IGL", "IGK", "TRG", "TRA", "TRD") %in% unique(substr(input.data$CTgene, 1,3)))) { + input.data <- input.data[-grep("IGH|TRG|IGL|IGK|TRA|TRD", substr(input.data$CTgene, 1,3)),] + } + + mat <- data.frame(chain2_aa = substring(input.data[,"CTaa"], 2, nchar(input.data[,"CTaa"])), + group = input.data[,"group"]) + mat <- na.omit(mat) + mat <- mat %>% + group_by(group, chain2_aa) %>% + dplyr::mutate(clonalFrequency = n()) + mat <- unique(mat) + + return(mat) +} + +.PairedExport <- function(input.data) { + input.data <- .data.wrangle(input.data, group.by, "CTgene", "both") + input.data <- bind_rows(input.data, .id = "group") genes <- str_split(input.data[,"CTgene"], "_", simplify = TRUE) aa <- str_split(input.data[,"CTaa"], "_", simplify = TRUE) nt <- str_split(input.data[,"CTnt"], "_", simplify = TRUE) - chain1_gene <- str_split(genes[,1], "[.]", simplify = TRUE) mat <- data.frame(row.names = input.data[,"barcode"], chain1_aa = aa[,1], @@ -48,14 +99,85 @@ exportClones <- function(input.data, chain2_nt = nt[,2], chain2_genes = genes[,2], group = input.data[,"group"]) - mat[mat == "NA"] <- NA - if(!write.file) { - return(mat) - } + return(mat) +} + +.AIRRExport <- function(input.data) { + input.data <- .data.wrangle(input.data, group.by, "CTgene", "both") + input.data <- bind_rows(input.data, .id = "group") - if(is.null(dir)) { - dir <- "./" - } - filepath <- paste0(dir, file.name) - write.csv(mat, file = filepath) -} \ No newline at end of file + mat <- list() + + + mat <- list() + + # Define a function for processing each row + .process_row <- function(row) { + # Split gene, amino acid, and nucleotide columns + genes <- str_split(row$CTgene, "_", simplify = TRUE) + aa <- str_split(row$CTaa, "_", simplify = TRUE) + nt <- str_split(row$CTnt, "_", simplify = TRUE) + + # Remove secondary chain if multiple chains + if (any(grepl(";", aa))) { + multi.chain.pos <- grep(";", aa) + genes[, multi.chain.pos] <- sapply(genes[, multi.chain.pos], function(x) str_split(x, ";", simplify = TRUE)[, 1]) + aa[, multi.chain.pos] <- sapply(aa[, multi.chain.pos], function(x) str_split(x, ";", simplify = TRUE)[, 1]) + nt[, multi.chain.pos] <- sapply(nt[, multi.chain.pos], function(x) str_split(x, ";", simplify = TRUE)[, 1]) + } + + # Extract locus information + chain1_gene <- str_split(genes[, 1], "[.]", simplify = TRUE) + chain2_gene <- str_split(genes[, 2], "[.]", simplify = TRUE) + locus1 <- substr(chain1_gene[, 1], 1, 3) + locus2 <- substr(chain2_gene[, 1], 1, 3) + + # Define a function to sort gene calls + .sort_gene_calls <- function(gene) { + if (length(gene) == 3) { + gene <- cbind(gene, NA) + colnames(gene) <- c("v_call", "j_call", "c_call", "d_call") + } else if (length(gene) == 4) { + colnames(gene) <- c("v_call", "d_call", "j_call", "c_call") + } else if (all(gene == "NA")) { + gene <- matrix(ncol = 4, nrow = 1, NA) + colnames(gene) <- c("v_call", "j_call", "c_call", "d_call") + } + return(gene) + } + + # Sort gene calls for both chains + chain1_gene <- .sort_gene_calls(chain1_gene) + chain2_gene <- .sort_gene_calls(chain2_gene) + + # Create the formatted data frame + tmp.out <- data.frame( + cell_id = row$barcode, + locus = c(locus1, locus2), + v_call = c(chain1_gene[,"v_call"], chain2_gene[,"v_call"]), + d_call = c(chain1_gene[,"d_call"], chain2_gene[,"d_call"]), + j_call = c(chain1_gene[,"j_call"], chain2_gene[,"j_call"]), + c_call = c(chain1_gene[,"c_call"], chain2_gene[,"c_call"]), + junction = c(nt[, 1], nt[, 2]), + junction_aa = c(aa[, 1], aa[, 2]) + ) + + # Replace "NA" with NA in the data frame + tmp.out[tmp.out == "NA"] <- NA + + # Identify rows with more than 4 NA values and remove them + na.to.remove <- which(rowSums(is.na(tmp.out)) > 4) + if (length(na.to.remove) >= 1) { + tmp.out <- tmp.out[-na.to.remove, ] + } + + return(tmp.out) + } + + # Process each row and store the results in the list + for (i in seq_len(nrow(input.data))) { + mat[[i]] <- .process_row(input.data[i, ]) + } + mat <- do.call(rbind, mat) + return(mat) +} diff --git a/R/global.R b/R/global.R index 482ca90a..a4de2616 100644 --- a/R/global.R +++ b/R/global.R @@ -39,5 +39,8 @@ utils::globalVariables ("Var2") utils::globalVariables ("variable") utils::globalVariables ("weights") + utils::globalVariables ("group.by") + utils::globalVariables ("group") + utils::globalVariables ("chain2_aa") invisible () } diff --git a/R/percentAA.R b/R/percentAA.R index 6d6d9a33..d1fa5ae9 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -43,6 +43,7 @@ percentAA <- function(input.data, strings <- do.call(c,str_split(strings, ";")) strings <- strings[strings != "NA"] strings <- strings[nchar(strings) < aa.length] + strings <- na.omit(strings) strings <- .padded_strings(strings, aa.length) strings <- do.call(rbind, strings) @@ -77,8 +78,7 @@ percentAA <- function(input.data, return(plot) } -.padded_strings <- function(strings, max.length) { - max_length <- max.length +.padded_strings <- function(strings, max_length) { x <- lapply(strings, function(str) { str_len <- nchar(str) diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index 6711bd00..7bbaf85b 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -23,6 +23,10 @@ clonalCompare( \item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} +\item{cloneCall}{How to call the clonotype - VDJC gene (gene), +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} + \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL".} @@ -46,10 +50,6 @@ to group the new list. NULL will return clusters.} \item{exportTable}{Returns the data frame used for forming the graph.} \item{palette}{Colors to use in visualization - input any \link[grDevices]{hcl.pals}.} - -\item{cloneCallHow}{to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), -VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} } \value{ ggplot of the proportion of total sequencing read of diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index a2870ff4..473f3880 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -23,6 +23,10 @@ both c(\code{\link{combineTCR}}, \code{\link{combineBCR}}).} \item{sc.data}{The Seurat or Single-Cell Experiment (SCE) object to attach} +\item{cloneCall}{How to call the clonotype - VDJC gene (gene), +CDR3 nucleotide (nt), CDR3 amino acid (aa), +VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} + \item{chain}{indicate if both or a specific chain should be used - e.g. "both", "TRA", "TRG", "IGH", "IGL"} @@ -43,10 +47,6 @@ on frequency, the upper limit of *cloneSizes* will be automatically amended.} \item{addLabel}{This will add a label to the frequency header, allowing the user to try multiple group.by variables or recalculate frequencies after subsetting the data.} - -\item{cloneCallHow}{to call the clonotype - VDJC gene (gene), -CDR3 nucleotide (nt), CDR3 amino acid (aa), -VDJC gene + CDR3 nucleotide (strict) or a custom variable in the data.} } \value{ Single-cell object with clonotype information added to meta data diff --git a/man/exportClones.Rd b/man/exportClones.Rd index ea0496fd..602e9a69 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -6,6 +6,7 @@ \usage{ exportClones( input.data, + format = "paired", group.by = NULL, write.file = TRUE, dir = NULL, @@ -16,6 +17,8 @@ exportClones( \item{input.data}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} +\item{format}{The format to export the clones - "paired", "airr", or "TCRMatch".} + \item{group.by}{The variable to use for grouping.} \item{write.file}{\strong{TRUE}, save the file or \strong{FALSE}, return a data.frame} @@ -29,7 +32,10 @@ CSV file of the paired sequences. } \description{ This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) -by barcodes. +by barcodes. \strong{format} determines the structure of the csv file - \emph{paired} will +export sequences by barcodes and include multiple chians, \emph{airr} will export a data +frame that is consistent with the AIRR format, and \emph{TCRMatch} will +export a data frame that has the TRB chain with count information. } \examples{ \dontrun{ @@ -37,7 +43,8 @@ by barcodes. combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -exportClones(combined) +exportClones(combined, + format = "paired") } } From 7347904ce94b650f0ba2fde8e2b8387d4d49b922 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 27 Oct 2023 11:33:47 -0500 Subject: [PATCH 103/142] Finalize clonalCluster --- R/addVariable.R | 2 +- R/clonalCluster.R | 66 ++++++++++++++++++--------- R/utils.R | 2 +- man/clonalCluster.Rd | 2 +- vignettes/articles/Clonal_Cluster.Rmd | 10 ++-- vignettes/articles/Processing.Rmd | 4 +- vignettes/vignette.Rmd | 12 ++--- 7 files changed, 56 insertions(+), 42 deletions(-) diff --git a/R/addVariable.R b/R/addVariable.R index 004af001..6e5a849c 100644 --- a/R/addVariable.R +++ b/R/addVariable.R @@ -30,4 +30,4 @@ addVariable <- function(input.data, input.data[[i]][,variable.name] <- variables[i] } return(input.data) -} \ No newline at end of file +} diff --git a/R/clonalCluster.R b/R/clonalCluster.R index 63096914..93580e65 100644 --- a/R/clonalCluster.R +++ b/R/clonalCluster.R @@ -13,7 +13,7 @@ #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) #' -#' sub_combined <- clonalCluster(combined[[2]], chain = "TRA", sequence = "aa") +#' sub_combined <- clonalCluster(combined[c(1,2)], chain = "TRA", sequence = "aa") #' #' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} #' or \code{\link{combineExpression}}. @@ -46,19 +46,15 @@ clonalCluster <- function(input.data, threshold = 0.85, group.by = NULL, exportGraph = FALSE) { - output.list <- list() if(chain == "both") { stop("Please select an individual chain for clustering") } #Prepping any single-cell object is.list <- inherits(input.data, "list") - if (inherits(x = input.data, what = c("Seurat", "SummarizedExperiment"))) { - dat <- .grabMeta(input.data) - } else { - dat <- input.data + if(!is.list && !inherits(x = input.data, what = c("Seurat", "SummarizedExperiment"))) { + input.data <- .checkList(input.data) } - dat <- .checkList(dat) - dat <- .data.wrangle(dat, group.by, "CTgene", chain) + dat <- .data.wrangle(input.data, group.by, "CTgene", chain) if (inherits(x = input.data, what = c("Seurat", "SummarizedExperiment"))) { for (y in seq_along(dat)) { @@ -74,7 +70,6 @@ clonalCluster <- function(input.data, if (!is.null(samples)) { dat <- dat[which(names(dat) %in% samples)] } - dat <- .checkList(dat) #Getting column labels to pull from if (grepl("TRA|TRG", chain)) { @@ -94,27 +89,33 @@ clonalCluster <- function(input.data, if (!is.null(group.by)) { bound <- bind_rows(dat, .id = "group.by") - graph.variables <- bound %>% group_by(bound[,ref2]) %>% dplyr::summarize(sample_count = n(), unique_samples = paste0(unique(group.by), collapse = ",")) - + dictionary <- list(bound) } else { bound <- bind_rows(dat) - graph.variables <- bound %>% - group_by(bound[,ref2]) %>% - dplyr::summarize(sample_count = n()) + graph.variables <- bind_rows(dat) %>% + group_by(bound[,ref2]) %>% + dplyr::summarize(sample_count = n()) + dictionary <- dat } - #Generating Connected Component - cluster <- .lvCompare(bound, - gene = "CTgene", - chain = ref2, - threshold = threshold, - exportGraph = exportGraph) + output.list <- lapply(dictionary, function(x) { + cluster <- .lvCompare(x, + gene = "CTgene", + chain = ref2, + threshold = threshold, + exportGraph = exportGraph) + cluster + }) + #Grabbing column order for later return + column.order <- colnames(bound) + #Returning the igraph object if eexportGraph = TRUE if(exportGraph) { + cluster <- output.list[[1]] vertex <- names(V(cluster)) data_df <- unique(data.frame( id = V(cluster)$name @@ -127,8 +128,16 @@ clonalCluster <- function(input.data, return(cluster) } - cluster[,1] <- str_replace_all(cluster[,1], "CTgene", chain) - colnames(cluster) <- c(paste0(chain, "_cluster"), ref2) + cluster.list <- lapply(seq_len(length(output.list)), function(x) { + output.list[[x]][,1] <- str_replace_all(output.list[[x]][,1], "CTgene", chain) + if(!is.null(group.by)) { + output.list[[x]][,1] <- paste0(names(dat)[x], ":", output.list[[x]][,1]) + } + colnames(output.list[[x]]) <- c(paste0(chain, "_cluster"), ref2) + output.list[[x]] + + }) + cluster <- bind_rows(cluster.list) #Merging with contig info tmp <- bound @@ -136,6 +145,9 @@ clonalCluster <- function(input.data, output2 <- cluster[cluster[,2] %in% tmp[,ref2],] bound <- suppressMessages(join(tmp, output2, match = "first")) + #Removing unconnected calls + bound[!grepl(".Cluster", bound[,colnames(cluster)[1]]), colnames(cluster)[1]] <- NA + #Adding to potential single-cell object if(inherits(x=input.data, what ="Seurat") | inherits(x=input.data, what ="SummarizedExperiment")) { PreMeta <- bound @@ -151,6 +163,16 @@ clonalCluster <- function(input.data, rownames(colData(input.data)) <- rownames } } else { + #Reorder columns + bound <- bound[,c(column.order, colnames(cluster)[1])] + #Split back into list + if(is.list) { + if(!is.null(group.by)) { + bound <- split(bound, bound[,group.by]) + } else { + bound <- split(bound, bound[,"sample"]) + } + } input.data <- bound } return(input.data) diff --git a/R/utils.R b/R/utils.R index 02310bb8..2e865900 100644 --- a/R/utils.R +++ b/R/utils.R @@ -583,7 +583,7 @@ is_df_or_list_of_df <- function(x) { if (nrow(edge.list) > 0) { edge.list <- unique(edge.list) g <- graph_from_data_frame(edge.list) - components <- components(g, mode = c("weak")) + components <- igraph::components(g, mode = c("weak")) out <- data.frame("cluster" = components$membership, "filtered" = names(components$membership)) filter <- which(table(out$cluster) > 1) diff --git a/man/clonalCluster.Rd b/man/clonalCluster.Rd index 184307c9..27be9f15 100644 --- a/man/clonalCluster.Rd +++ b/man/clonalCluster.Rd @@ -51,7 +51,7 @@ combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -sub_combined <- clonalCluster(combined[[2]], chain = "TRA", sequence = "aa") +sub_combined <- clonalCluster(combined[c(1,2)], chain = "TRA", sequence = "aa") } \concept{Visualizing_Clones} diff --git a/vignettes/articles/Clonal_Cluster.Rmd b/vignettes/articles/Clonal_Cluster.Rmd index bb79f4ef..d65b7eb4 100644 --- a/vignettes/articles/Clonal_Cluster.Rmd +++ b/vignettes/articles/Clonal_Cluster.Rmd @@ -59,12 +59,10 @@ scRep_example <- combineExpression(combined.TCR, The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the B or T cell receptors at the level of nucleotides (**sequence** = "nt) or amino acids (**sequence** = "aa). It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. -Clonotype cluster by distance: +Edit-distance based clusters will have the following format: +* [chain:] + :Cluster + [number] [e.g., TRA:Cluster.1] -* TCRA:Cluster.1 [chain:] + :Cluster + [number] -* TCRA.123 [chain:] + [number] - -Cluster denotes if the cluster was called using the normalized Levenshtein distance, which takes the edit distance calculated between 2 sequences and divides that by the mean of the sequence lengths. +*Cluster* denotes if the cluster was called using the normalized Levenshtein distance, which takes the edit distance calculated between 2 sequences and divides that by the mean of the sequence lengths. Unconnected sequences will have *NA* values. ### Basic use: @@ -75,7 +73,7 @@ sub_combined <- clonalCluster(combined.TCR[[2]], threshold = 0.85, group.by = NULL) -head(sub_combined) +head(sub_combined[,c(1,2,13)]) ``` If performed over the number of samples, such as the list elements, **group.by** can used to calculate only the clusters on the setting of patient sample (**group.by** = "Patient") or tissue type (**group.by** = "Type"). This will add the selected group to the beginning of the cluster designation. We can also call ```clonalCluster()``` directly on a Single-Cell Object. diff --git a/vignettes/articles/Processing.Rmd b/vignettes/articles/Processing.Rmd index 5b163c36..e8b7329a 100644 --- a/vignettes/articles/Processing.Rmd +++ b/vignettes/articles/Processing.Rmd @@ -51,8 +51,8 @@ What if there are more variables to add than just sample and ID? We can add them ```{r} combined.TCR <- addVariable(combined.TCR, - variable.name = "Type", - variables = rep(c("B", "L"), 4)) + variable.name = "Type", + variables = rep(c("B", "L"), 4)) head(combined.TCR[[1]]) ``` diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 1a0444ad..11918c51 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -838,16 +838,10 @@ chordDiagram(circles, ## clonalCluster -The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the B or T cell receptors at the level of nucleotides or amino acids. It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. +The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the B or T cell receptors at the level of nucleotides (**sequence** = "nt) or amino acids (**sequence** = "aa). It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. -Clonotype cluster by distance: - -* TCRA:Cluster.1 [chain:] + :Cluster + [number] -* TCRA.123 [chain:] + [number] - -Cluster denotes if the cluster was called using the normalized Levenshtein distance, which takes the edit distance calculated between 2 sequences and divides that by the mean of the sequence lengths. - -If performed over the number of samples, such as the list elements, **group.by** can used to calculate only the clusters on the setting of patient sample (**group.by** = "Patient") or tissue type (group.by = "Type"). This will add the selected group to the beginning of the cluster designation. +Edit-distance based clusters will have the following format: +* [chain:] + :Cluster + [number] [e.g., TRA:Cluster.1] ```{r} From 9b3afb077b93a0de84c7e998b3a4b2ccce1ea64b Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Sat, 28 Oct 2023 11:19:49 -0700 Subject: [PATCH 104/142] preliminary rcppGetNtKmerPercent --- .gitignore | 1 + R/RcppExports.R | 10 +- R/percentKmer.R | 53 +- man/{screp_example.Rd => scRep_example.Rd} | 0 src/RcppExports.cpp | 25 + src/kmer.cpp | 100 + .../viz/abundancecontig-scaled-plot.svg | 70 - .../viz/clonalabundance-scaled-plot.svg | 78 + .../viz/clonallength-both-chain-plot.svg | 503 +++ .../_snaps/viz/clonalquant-scaled-plot.svg | 90 + .../clonalsizedistribution-vignette-plot.svg | 94 + .../clonesizedistribution-vignette-plot.svg | 82 - .../viz/compareclonotypes-alluvial-plot.svg | 141 - .../viz/compareclonotypes-area-plot.svg | 95 - .../viz/lengthcontig-both-chain-plot.svg | 393 -- .../_snaps/viz/quantcontig-scaled-plot.svg | 80 - .../viz/quantcontig-single-sample-plot.svg | 44 - .../viz/scatterclonotype-vignette-plot.svg | 3841 ----------------- .../_snaps/viz/vizgenes-bar-vignette-plot.svg | 686 --- .../viz/vizgenes-heatmap-vignette-plot.svg | 1186 ----- tests/testthat/test-percentKmer.R | 27 + .../top_30_aa_3mer_composition_matrix.rds | Bin 0 -> 1950 bytes .../top_30_nt_3mer_composition_matrix.rds | Bin 0 -> 2076 bytes 23 files changed, 955 insertions(+), 6644 deletions(-) rename man/{screp_example.Rd => scRep_example.Rd} (100%) create mode 100644 src/kmer.cpp delete mode 100644 tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonallength-both-chain-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalquant-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalsizedistribution-vignette-plot.svg delete mode 100644 tests/testthat/_snaps/viz/clonesizedistribution-vignette-plot.svg delete mode 100644 tests/testthat/_snaps/viz/compareclonotypes-alluvial-plot.svg delete mode 100644 tests/testthat/_snaps/viz/compareclonotypes-area-plot.svg delete mode 100644 tests/testthat/_snaps/viz/lengthcontig-both-chain-plot.svg delete mode 100644 tests/testthat/_snaps/viz/quantcontig-scaled-plot.svg delete mode 100644 tests/testthat/_snaps/viz/quantcontig-single-sample-plot.svg delete mode 100644 tests/testthat/_snaps/viz/scatterclonotype-vignette-plot.svg delete mode 100644 tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg delete mode 100644 tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg create mode 100644 tests/testthat/test-percentKmer.R create mode 100644 tests/testthat/testdata/percentKmer/top_30_aa_3mer_composition_matrix.rds create mode 100644 tests/testthat/testdata/percentKmer/top_30_nt_3mer_composition_matrix.rds diff --git a/.gitignore b/.gitignore index dba52245..89ff4ce0 100644 --- a/.gitignore +++ b/.gitignore @@ -6,3 +6,4 @@ local_tests.R .RData docs vignettes/articles/scRep_example_full.rds +.vscode \ No newline at end of file diff --git a/R/RcppExports.R b/R/RcppExports.R index 0f85f4b5..71062fe8 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -1,7 +1,15 @@ # Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 -#' @keywords internal + rcpp_hello_world <- function() { invisible(.Call(`_scRepertoire_rcpp_hello_world`)) } +rcppGenerateUniqueNtMotifs <- function(k) { + .Call(`_scRepertoire_rcppGenerateUniqueNtMotifs`, k) +} + +rcppGetNtKmerPercent <- function(seqs, k) { + .Call(`_scRepertoire_rcppGetNtKmerPercent`, seqs, k) +} + diff --git a/R/percentKmer.R b/R/percentKmer.R index d5bd84ee..3223d4b4 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -40,7 +40,7 @@ percentKmer <- function(df, cloneCall <- .theCall(cloneCall) sco <- is_seurat_object(df) | is_se_object(df) df <- .data.wrangle(df, group.by, cloneCall, chain) - if(!is.null(group.by) & !sco) { + if(!is.null(group.by) && !sco) { df <- .groupList(df, group.by) } #Determining the function to generate motifs @@ -55,18 +55,25 @@ percentKmer <- function(df, rownames(mat) <- names(df) #Looping through the counts of motifs - for(i in seq_along(df)) { - df[[i]][,cloneCall][which(df[[i]][,cloneCall] == "NA")] <- NA - df[[i]] <- df[[i]][!is.na(df[[i]][,cloneCall]),] + for (i in seq_along(df)) { + df[[i]][,cloneCall][which(df[[i]][, cloneCall] == "NA")] <- NA + df[[i]] <- df[[i]][!is.na(df[[i]][, cloneCall]),] + + # if (identical(cloneCall, "CTnt")) { + # mat[i, ] <- rcppGetNtKmerPercent(df[[i]][, cloneCall], motif.length) + # next + # } + motifs <- .tokenize_multiple_sequences(df[[i]][,cloneCall], motif.length) motif.table <- table(unlist(motifs)) if(any(grepl("\\;", names(motif.table)))) { motif.table <- motif.table[!grepl("\\;", names(motif.table))] } mat.pos <- match(names(motif.table), colnames(mat)) - mat[i,mat.pos] <- as.vector(motif.table) - mat[i,] <- mat[i,]/sum(na.omit(mat[i,])) + mat[i, mat.pos] <- as.vector(motif.table) + mat[i, ] <- mat[i, ] / sum(na.omit(mat[i, ])) } + #Removing any column that is all NAs if(any(colSums(is.na(mat)) == length(df))) { mat <- mat[,-which(colSums(is.na(mat)) == length(df))] @@ -77,28 +84,28 @@ percentKmer <- function(df, if(!is.null(top.motifs)) { mads <- apply(mat, 2, mad) motifs.to.save <- names(sort(mads, decreasing = TRUE))[seq_len(top.motifs)] - mat <- mat[,colnames(mat) %in% motifs.to.save] + mat <- mat[, colnames(mat) %in% motifs.to.save] } + #Getting mat into a ggplot-compliant form mat_melt <- melt(mat) if (!is.null(motifs.to.save)) { mat_melt$Var2 <- factor(mat_melt$Var2, levels = rev(motifs.to.save)) } - #Plotting - plot <- ggplot(mat_melt, aes(x=Var2, y = Var1, fill=value)) + - geom_tile(lwd= 0.1, color = "black") + - scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + - theme_classic() + - coord_flip() + - theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), - axis.title = element_blank()) - if (exportTable == TRUE) { - return(mat) + if (exportTable) { + return(mat) } - return(plot) -} + #Plotting + ggplot(mat_melt, aes(x=Var2, y = Var1, fill=value)) + + geom_tile(lwd= 0.1, color = "black") + + scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + + theme_classic() + + coord_flip() + + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1), + axis.title = element_blank()) +} .generate_unique_aa_motifs <- function(motif_length) { amino_acids <- c("A", "C", "D", "E", "F", "G", "H", "I", "K", "L", "M", "N", "P", "Q", "R", "S", "T", "V", "W", "Y") @@ -108,10 +115,7 @@ percentKmer <- function(df, } .generate_unique_nt_motifs <- function(motif_length) { - nt <- c("A", "T", "C", "G") - all_motifs <- expand.grid(replicate(motif_length, nt, simplify = FALSE)) - unique_motifs <- unique(apply(all_motifs, 1, paste, collapse = "")) - return(unique_motifs) + rcppGenerateUniqueNtMotifs(motif_length) } .tokenize_sequence <- function(sequence, motif_length) { @@ -127,6 +131,5 @@ percentKmer <- function(df, } .tokenize_multiple_sequences <- function(sequences, motif_length) { - tokenized_sequences <- sapply(sequences, .tokenize_sequence, motif_length = motif_length) - return(tokenized_sequences) + sapply(sequences, .tokenize_sequence, motif_length = motif_length) } diff --git a/man/screp_example.Rd b/man/scRep_example.Rd similarity index 100% rename from man/screp_example.Rd rename to man/scRep_example.Rd diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 0dc7fc4c..8405d326 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -19,9 +19,34 @@ BEGIN_RCPP return R_NilValue; END_RCPP } +// rcppGenerateUniqueNtMotifs +Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k); +RcppExport SEXP _scRepertoire_rcppGenerateUniqueNtMotifs(SEXP kSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< int >::type k(kSEXP); + rcpp_result_gen = Rcpp::wrap(rcppGenerateUniqueNtMotifs(k)); + return rcpp_result_gen; +END_RCPP +} +// rcppGetNtKmerPercent +const Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k); +RcppExport SEXP _scRepertoire_rcppGetNtKmerPercent(SEXP seqsSEXP, SEXP kSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const std::vector& >::type seqs(seqsSEXP); + Rcpp::traits::input_parameter< const int >::type k(kSEXP); + rcpp_result_gen = Rcpp::wrap(rcppGetNtKmerPercent(seqs, k)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_scRepertoire_rcpp_hello_world", (DL_FUNC) &_scRepertoire_rcpp_hello_world, 0}, + {"_scRepertoire_rcppGenerateUniqueNtMotifs", (DL_FUNC) &_scRepertoire_rcppGenerateUniqueNtMotifs, 1}, + {"_scRepertoire_rcppGetNtKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetNtKmerPercent, 2}, {NULL, NULL, 0} }; diff --git a/src/kmer.cpp b/src/kmer.cpp new file mode 100644 index 00000000..21bfe325 --- /dev/null +++ b/src/kmer.cpp @@ -0,0 +1,100 @@ +// nucleotide kmer counting + +#include +#include +#include + +inline const unsigned short int toIndex(const char x) { + switch(x) { + case 'A':case 'a': + return 0; + case 'C':case 'c': + return 1; + case 'G':case 'g': + return 2; + default: + return 3; + } +} + +inline const std::string toKmer(int index, int k) { + std::string kmer = ""; + for (int i = 0; i < k; i++) { + int baseIndex = index % 4; + char base; + + if (baseIndex == 0) { + base = 'A'; + } else if (baseIndex == 1) { + base = 'C'; + } else if (baseIndex == 2) { + base = 'G'; + } else { + base = 'T'; + } + + kmer = base + kmer; + index /= 4; + } + return kmer; +} + +// [[Rcpp::export]] +Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k) { + unsigned int numKmers = 1 << (k + k); + Rcpp::CharacterVector motifs (numKmers); + for (unsigned int i = 0; i < numKmers; i++) { + motifs[i] = toKmer(i, k); + } + return motifs; +} + +inline void kmerCount(std::vector& bins, const unsigned int& mask, const std::string& seq, const int k) { + unsigned int kmer = 0; + for (int i = 0; i < k - 1; i++) { + kmer = (kmer << 2) | toIndex(seq[i]); + } + for (int i = k - 1; i < seq.size(); i++) { + kmer = ((kmer << 2) & mask) | toIndex(seq[i]); + bins[kmer]++; + } +} + +const long double sum(std::vector& v) { + long double n = 0; + for (long double num : v) { + n += num; + } + return n; +} + +Rcpp::NumericVector convertToNumericVector(std::vector& v, int n) { + Rcpp::NumericVector converted (n); + for (int i = 0; i < n; i++) { + converted[i] = v[i]; + } + return converted; +} + +// [[Rcpp::export]] +const Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k) { + const unsigned int mask = (1 << (k + k)) - 1; + int numKmers = mask + 1; + std::vector bins (numKmers); + + for (std::string seq : seqs) { + kmerCount(bins, mask, seq, k); + } + + long double sumOfNonZeroTerms = sum(bins); + if (sumOfNonZeroTerms < 0.0001) { // == 0 + return Rcpp::NumericVector (numKmers, R_NaReal); + } + + const double scaleFactor = 1 / sumOfNonZeroTerms; + for (int i = 0; i < mask + 1; i++) { + bins[i] *= scaleFactor; + } + + return convertToNumericVector(bins, numKmers); +} diff --git a/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg b/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg deleted file mode 100644 index 94131b11..00000000 --- a/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg +++ /dev/null @@ -1,70 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0 -1000 -2000 - - - - - - - -1 -10 -100 -Abundance -Number of Clonotypes - -Samples - - - - - - -PY_P -PY_T -PX_P -PX_T -PZ_P -PZ_T -abundanceContig_scaled_plot - - diff --git a/tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg b/tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg new file 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -PX - - - - - - - - - - -PY - - - - - - - - - - -PZ - - - -TRBV20-1 -TRBV28 -TRBV2 -TRBV5-1 -TRBV6-5 -TRBV24-1 -TRBV19 -TRBV27 -TRBV7-9 -TRBV10-1 -TRBV12-4 -TRBV5-6 -TRBV4-1 -TRBV5-4 -TRBV3-1 -TRBV18 -TRBV11-1 -TRBV9 -TRBV7-2 -TRBV12-3 -TRBV7-3 -TRBV29-1 -TRBV11-2 -TRBV6-3 -TRBV7-8 -TRBV4-3 -TRBV30 -TRBV6-4 -TRBV6-1 -TRBV6-6 -TRBV10-3 -TRBV13 -TRBV14 -TRBV15 -TRBV5-5 -TRBV7-6 -TRBV11-3 -TRBV4-2 -TRBV25-1 -TRBV5-8 -TRBV21-1 -TRBV12-5 -TRBV7-7 -TRBV10-2 -TRBV16 -TRBV7-4 -TRBV5-3 - -0.0 -0.1 -0.2 - - - - -0.0 -0.1 -0.2 - - - - -0.0 -0.1 -0.2 - - - -vizGenes_bar_vignette_plot - - diff --git a/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg b/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg deleted file mode 100644 index c385183f..00000000 --- a/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg +++ /dev/null @@ -1,1186 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -TRBJ1-1 -TRBJ1-2 -TRBJ1-3 -TRBJ1-4 -TRBJ1-5 -TRBJ1-6 -TRBJ2-1 -TRBJ2-2 -TRBJ2-3 -TRBJ2-4 -TRBJ2-5 -TRBJ2-6 -TRBJ2-7 - - - - - - - - - - - - - - -TRBV10-1 -TRBV10-2 -TRBV10-3 -TRBV11-1 -TRBV11-2 -TRBV11-3 -TRBV12-3 -TRBV12-4 -TRBV12-5 -TRBV13 -TRBV14 -TRBV15 -TRBV16 -TRBV18 -TRBV19 -TRBV2 -TRBV20-1 -TRBV21-1 -TRBV24-1 -TRBV25-1 -TRBV27 -TRBV28 -TRBV29-1 -TRBV3-1 -TRBV30 -TRBV4-1 -TRBV4-2 -TRBV4-3 -TRBV5-1 -TRBV5-3 -TRBV5-4 -TRBV5-5 -TRBV5-6 -TRBV5-8 -TRBV6-1 -TRBV6-3 -TRBV6-4 -TRBV6-5 -TRBV6-6 -TRBV7-2 -TRBV7-3 -TRBV7-6 -TRBV7-7 -TRBV7-8 -TRBV7-9 -TRBV9 - - -0.02 -0.04 -0.06 -Scaled Values - - - - - - -vizGenes_heatmap_vignette_plot - - diff --git a/tests/testthat/test-percentKmer.R b/tests/testthat/test-percentKmer.R new file mode 100644 index 00000000..e5fa539b --- /dev/null +++ b/tests/testthat/test-percentKmer.R @@ -0,0 +1,27 @@ +combined <- combineTCR( + contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L") +) + +test_that("percentKmer works for AAs", { + top_30_aa_3mer_composition_matrix <- getdata( + "percentKmer", "top_30_aa_3mer_composition_matrix" + ) + + expect_equal( + percentKmer(combined, exportTable = TRUE), + top_30_aa_3mer_composition_matrix + ) +}) + +test_that("percentKmer works for NTs", { + top_30_nt_3mer_composition_matrix <- getdata( + "percentKmer", "top_30_nt_3mer_composition_matrix" + ) + expect_equal( + percentKmer(combined, cloneCall = "nt", exportTable = TRUE), + top_30_nt_3mer_composition_matrix + ) +}) + +# TODO test for cases where no kmers were counted (NA columns present) \ No newline at end of file diff --git a/tests/testthat/testdata/percentKmer/top_30_aa_3mer_composition_matrix.rds b/tests/testthat/testdata/percentKmer/top_30_aa_3mer_composition_matrix.rds new file mode 100644 index 0000000000000000000000000000000000000000..38e7a725249bdb912bfe35d9dacc2bc44902b9a0 GIT 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v, int n) { - Rcpp::NumericVector converted (n); - for (int i = 0; i < n; i++) { - converted[i] = v[i]; - } - return converted; -} - // [[Rcpp::export]] -const Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k) { +std::vector rcppGetNtKmerPercent(const std::vector& seqs, const int k) { const unsigned int mask = (1 << (k + k)) - 1; int numKmers = mask + 1; std::vector bins (numKmers); @@ -88,7 +80,7 @@ const Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& s long double sumOfNonZeroTerms = sum(bins); if (sumOfNonZeroTerms < 0.0001) { // == 0 - return Rcpp::NumericVector (numKmers, R_NaReal); + return bins; } const double scaleFactor = 1 / sumOfNonZeroTerms; @@ -96,5 +88,5 @@ const Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& s bins[i] *= scaleFactor; } - return convertToNumericVector(bins, numKmers); + return bins; } From 0fd39d372cf9401dfe39263ea946495a503b4e45 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 31 Oct 2023 08:46:35 -0500 Subject: [PATCH 106/142] update spelling --- R/addVariable.R | 2 +- R/clonalHomeostasis.R | 2 +- R/exportClones.R | 2 +- README.md | 2 +- inst/WORDLIST | 225 +++++++++++++++++++----- man/addVariable.Rd | 2 +- man/clonalHomeostasis.Rd | 2 +- man/exportClones.Rd | 2 +- tests/testthat/test-seurat_command.R | 7 +- tests/testthat/test-utils.R | 18 +- vignettes/articles/Clonal_Diversity.Rmd | 2 +- vignettes/articles/FAQ.Rmd | 2 +- vignettes/articles/Ibex.Rmd | 4 +- vignettes/articles/Processing.Rmd | 2 +- vignettes/articles/Trex.Rmd | 17 +- vignettes/articles/Trex_Trajectory.Rmd | 147 ++++++++++++++++ vignettes/vignette.Rmd | 4 +- 17 files changed, 365 insertions(+), 77 deletions(-) create mode 100644 vignettes/articles/Trex_Trajectory.Rmd diff --git a/R/addVariable.R b/R/addVariable.R index 6e5a849c..836f40cb 100644 --- a/R/addVariable.R +++ b/R/addVariable.R @@ -3,7 +3,7 @@ #' This function adds variables to the product of \code{\link{combineTCR}}, #' or \code{\link{combineBCR}} to be used in later visualizations. #' For each element, the function will add a column (labeled by name) -#' with the variable. The length of the variable paramater needs to match +#' with the variable. The length of the variable parameter needs to match #' the length of the combined object. #' #' @examples diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 4dc7d51d..37cb4f4e 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -4,7 +4,7 @@ #' The grouping of these clonotypes is based on the parameter cloneSize, #' at default, cloneSize will group the clonotypes into bins of Rare = 0 #' to 0.0001, Small = 0.0001 to 0.001, etc. To adjust the proportions, -#' change the number or labeling of the cloneSize paramter. If a matrix +#' change the number or labeling of the cloneSize parameter. If a matrix #' output for the data is preferred, set \strong{exportTable} = TRUE. #' #' @examples diff --git a/R/exportClones.R b/R/exportClones.R index 48825669..6160117e 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -2,7 +2,7 @@ #' #' This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) #' by barcodes. \strong{format} determines the structure of the csv file - \emph{paired} will -#' export sequences by barcodes and include multiple chians, \emph{airr} will export a data +#' export sequences by barcodes and include multiple chains, \emph{airr} will export a data #' frame that is consistent with the AIRR format, and \emph{TCRMatch} will #' export a data frame that has the TRB chain with count information. #' diff --git a/README.md b/README.md index de04960a..15170f51 100644 --- a/README.md +++ b/README.md @@ -18,7 +18,7 @@ scRepertoire is compatible and integrated with the R packages [Trex](https://git ## Working with scRepertoire -scRepertoire has a comprehensive [website](https://www.borch.dev/uploads/screpertoire/) for detialed tutorials and function information. +scRepertoire has a comprehensive [website](https://www.borch.dev/uploads/screpertoire/) for detailed tutorials and function information. ## Installation diff --git a/inst/WORDLIST b/inst/WORDLIST index e33e95f3..3aea16a1 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -1,151 +1,294 @@ -AAACGGGAGATGGCGT +ADT +AE AIRR Andreatta +Atchley +Autoencoder BCR +BCRs BD +Barcode BioC -CATSATLRVVAEKLFF -CAVNGGSQGNLIF CDR -CSAEREDTDTQYF +CMD +COVID CTaa CTgene CTnt CTstrict +CVVSDNTGGFKTIF Carmona -CellRanger +ChangeLog Chao -Clonotype -Clonotypes +ClonalDiversity +CoNGA +Codecov CombineBCR -CombineTCR +CombineExpression Contig Contigs Corvino Drs +Embeddings +FilteringMulti Francesco +GSE +Gini Github Hyperexpanded IGH +IGKJ IGL Ig +Inv +JC +JSON Jaccard -LD +KF +Kidera Levenshtein +Liangtao Massimo Mazziotta +McPAS +MiXCR Morisita +Multiomic +Noonan +OHE +Omniscope +PIRD PMID -PY Pielou -Pielou's +Pre ProjectTIL +Readded +Rebasing +Rebumping +RenameCells +Repo SCE +Satija's SingleCellExperiment Startrac StartracDiversity -StartracOUt -StartracOut TCR TCRA TCRB +TCRMatch TCRs TRA +TRAV TRB +TRBD +TRBV TRD TRG +TUST Trex +UMAP +VAE VDJ VDJC +VDJdb Vgene +VizGene WAT -Weizhou +WNN Zhang +Zheng aa +abundanceContig +addVariable airr al +alluvialClones +alluvialClonotype alluvialClonotypes +autoencoder +autoencoders +autoencoding barcode barcodes -changeNames +bioc +bioconductor +bronchoalveolar +cav +cdr +chao +checkBlanks +checkContigs circlize +clonaRarefaction +clonalAbundance +clonalBias +clonalCluster +clonalCompare +clonalDiversity +clonalHomeostasis +clonalLength clonalNetwork +clonalOccupy +clonalOverlap clonalOverlay +clonalProportion +clonalQuant +clonalScatter +clonalSizeDistribution +clonalSplit clonally cloneCall +cloneSize +cloneSizes cloneType cloneTypes +clonesizeDistribution clonotype +clonotypeBias +clonotypeSizeDistribution clonotypes clonotypic +clusterTCR +colData +coldata colorectal combineBCR +combineContig combineExpression +combineSeurat combineTCR +combineTRUST combinedContig comparators +compareClonotype +compareClonotypes +complementarity contig contigs +convolutional createHTOContigList csv -defualt +customizable +dev df -dispaly -doParallel +dir +doubletons downsample downsampling +epitope +epitopes et expa +explainability exportClones +exportGraph exportTable filterMulti +filteringMulti +frac +gd getCirclize +getCoord ggalluvial +ggdendrogram +ggfittext ggplot +gini github +grDevices +grabMeta +grey +greyed +groupBy +hammingCompare +hcl +highlightClones highlightClonotypes +https +hypermutation +iNEXT +iedb igraph -individualy +incongruent +inv +io jaccard -labled -loc +json +kmer +kmers +lapply +lengthContigs +lengthDF +ln +loadContigs +lvCompare +lvcompare +mB mRNA -majorCluster +maTrex +makeGenes +matric migr migra morisita -multiplets +multisystem +mumosa +na nt -num -objecter +nucleotides +occupiedClonotype +occupiedRepertoire occupiedscRepertoire -outputed -pIndex paramater -paramter -permutated +parseAIRR +parseBCR +parseTCR +percentAA +percentGenes +percentKmer +percentVJ +phateR powerTCR -preprocess +pseudotime +quantContig quantifications rda -registerDoParallel +readRDS +regressClonotype +relevel removeMulti removeNA +repo +rescaling +runIbex +runTrex +sc +scRep +scRepetoire scater +scatterClonotype +scatterClonotypes +screp seurat +shannon +simpson startrac -stripBarcode +str +stripBarcodes +subsetClones subtype -sys -tcr -throughly +subtypes +summarise +theCall +trackable tran transcriptomic +trex tsv -unqiue unreturned -CMD -Codecov -coords -autoencoding -screp +variational +viridis +visVgene +vizGene +vizGenes +vizVgene diff --git a/man/addVariable.Rd b/man/addVariable.Rd index 69a89aca..52a59481 100644 --- a/man/addVariable.Rd +++ b/man/addVariable.Rd @@ -20,7 +20,7 @@ input.data list with the variable column added to each element. This function adds variables to the product of \code{\link{combineTCR}}, or \code{\link{combineBCR}} to be used in later visualizations. For each element, the function will add a column (labeled by name) -with the variable. The length of the variable paramater needs to match +with the variable. The length of the variable parameter needs to match the length of the combined object. } \examples{ diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index 1c5ca0bc..1c60a737 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -43,7 +43,7 @@ This function calculates the space occupied by clonotype proportions. The grouping of these clonotypes is based on the parameter cloneSize, at default, cloneSize will group the clonotypes into bins of Rare = 0 to 0.0001, Small = 0.0001 to 0.001, etc. To adjust the proportions, -change the number or labeling of the cloneSize paramter. If a matrix +change the number or labeling of the cloneSize parameter. If a matrix output for the data is preferred, set \strong{exportTable} = TRUE. } \examples{ diff --git a/man/exportClones.Rd b/man/exportClones.Rd index 602e9a69..f8aafbe6 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -33,7 +33,7 @@ CSV file of the paired sequences. \description{ This function saves a csv file of clonotypes (genes, amino acid, and nucleotide sequences) by barcodes. \strong{format} determines the structure of the csv file - \emph{paired} will -export sequences by barcodes and include multiple chians, \emph{airr} will export a data +export sequences by barcodes and include multiple chains, \emph{airr} will export a data frame that is consistent with the AIRR format, and \emph{TCRMatch} will export a data frame that has the TRB chain with count information. } diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R index 5b1526f7..c3cafd51 100644 --- a/tests/testthat/test-seurat_command.R +++ b/tests/testthat/test-seurat_command.R @@ -18,8 +18,11 @@ test_that("make_screp_seurat_cmd works", { expect_equal( test_obj@params, list( - cloneCall = "CTstrict", chain = "both", group.by = "none", - proportion = TRUE, filterNA = FALSE, + cloneCall = "CTstrict", + chain = "both", + group.by = NULL, + proportion = TRUE, + filterNA = FALSE, cloneSize = c( `None ( < X <= 0)` = 0, `Rare (0 < X <= 1e-04)` = 1e-04, `Small (1e-04 < X <= 0.001)` = 0.001, diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index ecb7313d..e8ee395f 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -23,21 +23,6 @@ test_that("checkList works", { # no idea what to put to make the stop message happen. perhaps with data.table(contig_list) ? but then it shouldnt fail :P }) -test_that("checkContigs works", { - input <- list( - df1 = data.frame(a = c("x", "", "z"), b = c("1", "2", "3")), - df2 = data.frame(c = c("foo", "bar", ""), d = c("", "spam", "eggs")) - ) - expected <- .checkContigs(input) - expect_true(is.list(expected)) - expect_true(is.data.frame(expected[[1]])) - expect_equal(expected[[1]]$a, c("x", NA, "z")) - expect_equal(expected[[1]]$b, c("1", "2", "3")) - expect_true(is.data.frame(expected[[2]])) - expect_equal(expected[[2]]$c, c("foo", "bar", NA)) - expect_equal(expected[[2]]$d, c(NA, "spam", "eggs")) -}) - # TODO bound.input.return # TODO get.coord # TODO checkSingleObject @@ -64,6 +49,9 @@ test_that("Modifying barcodes without ID works correctly", { expect_identical(modified_data, expected_modified_data) }) +############## +##TODO figure out where getdata is from? +##TODO annotate testthat functions??? test_that("Modifying barcodes with ID works correctly", { samples <- c("sample3", "sample4") ID <- c("id1", "id2") diff --git a/vignettes/articles/Clonal_Diversity.Rmd b/vignettes/articles/Clonal_Diversity.Rmd index 84595966..4090c629 100644 --- a/vignettes/articles/Clonal_Diversity.Rmd +++ b/vignettes/articles/Clonal_Diversity.Rmd @@ -49,7 +49,7 @@ combined.TCR <- combineTCR(contig_list, Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: **"shannon"**, **"inv.simpson"**, **"norm.entropy"**, **"gini.simpson"**, **"chao1"**, and **"ACE"**. Please see the manual for more information on each metric and the underlying calculations. -Inherent in diversity calculations is a bias increasing diversity with increasing repertoire size. ```clonalDiversity()``` will automatically downsample to the smallest repertoire size and perform boostapping to return the mean diversity estimates. If the output of diversity values are strange or minimally variable, it is likely due to a sample with small repertoire size. +Inherent in diversity calculations is a bias increasing diversity with increasing repertoire size. ```clonalDiversity()``` will automatically downsample to the smallest repertoire size and perform bootstrapping to return the mean diversity estimates. If the output of diversity values are strange or minimally variable, it is likely due to a sample with small repertoire size. **n.boots** The number of calculations to perform **(default = 100)**. diff --git a/vignettes/articles/FAQ.Rmd b/vignettes/articles/FAQ.Rmd index 4a7f9c9a..ec0191cd 100644 --- a/vignettes/articles/FAQ.Rmd +++ b/vignettes/articles/FAQ.Rmd @@ -54,7 +54,7 @@ original: ACGTACGTACGTACGT-1 seurat-modified: ACGTACGTACGTACGT-1_1 ``` -scReprtoire uses the samples in combineTCR() to add a prefix to the barcodes: +scRepertoire uses the samples in combineTCR() to add a prefix to the barcodes: ``` original: ACGTACGTACGTACGT-1 diff --git a/vignettes/articles/Ibex.Rmd b/vignettes/articles/Ibex.Rmd index e3116551..cbbb5798 100644 --- a/vignettes/articles/Ibex.Rmd +++ b/vignettes/articles/Ibex.Rmd @@ -49,7 +49,7 @@ suppressMessages(library(dplyr)) The idea behind Ibex is to combine BCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using BCR and RNA. Ibex was designed to make a customizable approach to this combined approach using R. -More infromation is available at the [Ibex GitHub Repo](https://github.com/ncborcherding/Ibex). +More information is available at the [Ibex GitHub Repo](https://github.com/ncborcherding/Ibex). ## Installation @@ -128,6 +128,7 @@ SeuratObj <- RunUMAP(SeuratObj, plot1 <- DimPlot(SeuratObj, reduction = "ibex.umap") + NoLegend() plot2 <- DimPlot(SeuratObj, group.by = "CTaa", reduction = "ibex.umap") + scale_color_viridis(discrete = TRUE, option = "B") + + theme(plot.title = element_blank()) + NoLegend() plot1 + plot2 @@ -155,6 +156,7 @@ SeuratObj <- FindClusters(SeuratObj, plot3 <- DimPlot(SeuratObj, reduction = "wnn.umap") plot4 <- DimPlot(SeuratObj, reduction = "wnn.umap", group.by = "CTaa") + scale_color_viridis(discrete = TRUE, option = "B") + + theme(plot.title = element_blank()) + NoLegend() plot3 + plot4 diff --git a/vignettes/articles/Processing.Rmd b/vignettes/articles/Processing.Rmd index e8b7329a..07d8c625 100644 --- a/vignettes/articles/Processing.Rmd +++ b/vignettes/articles/Processing.Rmd @@ -78,7 +78,7 @@ head(subset2[[1]]) ## exportClones -After assigning the cloneby barcode, we can export the paired clonotypes using `exportClones()` to save for later use or to use in other pipelines. +After assigning the clone by barcode, we can export the paired clonotypes using `exportClones()` to save for later use or to use in other pipelines. **write.file** * TRUE, save the file. diff --git a/vignettes/articles/Trex.Rmd b/vignettes/articles/Trex.Rmd index 161efcff..a7330363 100644 --- a/vignettes/articles/Trex.Rmd +++ b/vignettes/articles/Trex.Rmd @@ -50,7 +50,7 @@ use_condaenv(condaenv = "r-reticulate", required = TRUE) The idea behind Trex is to combine TCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using TCR and RNA with python. Trex was designed to make a customizable approach to this combined approach using R. -More infromation is available at the [Trex GitHub Repo](https://github.com/ncborcherding/Trex). +More information is available at the [Trex GitHub Repo](https://github.com/ncborcherding/Trex). ## Installation @@ -142,7 +142,8 @@ plot1 <- DimPlot(SeuratObj, plot2 <- DimPlot(SeuratObj, group.by = "CTaa", reduction = "Trex.umap") + - scale_color_viridis(discrete = TRUE, option = "B") + + scale_color_viridis(discrete = TRUE, option = "B") + + theme(plot.title = element_blank()) + NoLegend() plot1 + plot2 @@ -173,7 +174,8 @@ plot3 <- DimPlot(SeuratObj, plot4 <- DimPlot(SeuratObj, reduction = "wnn.umap", group.by = "CTaa") + - scale_color_viridis(discrete = TRUE, option = "B") + + scale_color_viridis(discrete = TRUE, option = "B") + + theme(plot.title = element_blank()) + NoLegend() plot3 + plot4 @@ -254,11 +256,13 @@ CoNGA.seurat <- annotateDB(CoNGA.seurat, DimPlot(CoNGA.seurat, reduction = "Trex.umap", - group.by = "TRB_Epitope.species") + group.by = "TRB_Epitope.species") + + theme(plot.title = element_blank()) DimPlot(CoNGA.seurat, reduction = "Trex.umap", - group.by = "TRB_Epitope.sequence") + group.by = "TRB_Epitope.sequence") + + theme(plot.title = element_blank()) ``` Or ```annotateDB()``` can be used on the full single-cell object and examine the sequence information along the RNA-based UMAP. An added feature to the function allows the annotations to be extended to CDR3 sequences that are within defined edit distances from the reference using **edit.distance**. @@ -270,7 +274,8 @@ SeuratObj <- annotateDB(SeuratObj, DimPlot(SeuratObj, reduction = "rna.umap", - group.by = "TRB_Epitope.species") + group.by = "TRB_Epitope.species") + + theme(plot.title = element_blank()) ``` diff --git a/vignettes/articles/Trex_Trajectory.Rmd b/vignettes/articles/Trex_Trajectory.Rmd new file mode 100644 index 00000000..bf29181e --- /dev/null +++ b/vignettes/articles/Trex_Trajectory.Rmd @@ -0,0 +1,147 @@ +--- +title: "Combining Deep Learning and TCRs with Trex" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Combining Deep Learning and TCRs with Trex} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +suppressMessages(library(Trex)) +suppressMessages(library(Seurat)) +suppressMessages(library(ggplot2)) +suppressMessages(library(viridis)) +suppressMessages(library(dplyr)) +suppressMessages(library(reticulate)) +use_condaenv(condaenv = "r-reticulate", required = TRUE) + +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L"), + filterMulti = TRUE) + +googledrive::drive_download("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link", overwrite = TRUE) + +scRep_example <- readRDS("scRep_example_full.rds") + +scRep_example <- combineExpression(combined.TCR, + scRep_example, + cloneCall="gene", + group.by = "sample") + +#Adding patient information +scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) + +#Adding type information +scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) +``` + + +Moving beyond the dual-embedding of TCR/RNA using Trex, the approach can also be used for performing trajectory analysis. Unlike using RNA alone, we can leverage using the clonal identity as part of the calculation. For this vignette, we will there are several packages that will be used: + +* [mumosa(https://bioconductor.org/packages/release/bioc/html/mumosa.html) for neighbor rescaling. +* [phateR](https://github.com/KrishnaswamyLab/phateR) for the diffusion-based affinity embedding. +* [slingshot](https://www.bioconductor.org/packages/release/bioc/html/slingshot.html) for lineage estimation and pseudotime calculation. + + +## Loading Libraries + +```{r} +library(mumosa) +library(phateR) +library(slingshot) +``` + +## Getting the Clonal Embeddings for TRA and TRB chains + +[Trex](https://github.com/ncborcherding/Trex) takes the CDR3 amino acid sequence and embeds them into latent dimensional space using convolutional neural networks. An introduction to Trex and the models available can be found in the [scRepertoire website](https://www.borch.dev/uploads/screpertoire/articles/trex). Below we will use variational autoencoders (VAE) using the the Kidera factors. + +```{r} +SeuratObj <- subset(scRep_example, Patient == "P18") + + + +SeuratObj <- runTrex(SeuratObj, + chains = "TRA", + encoder.model = "VAE", + encoder.input = "KF", + reduction.name = "Trex.TRA.KF") + +SeuratObj <- runTrex(SeuratObj, + chains = "TRB", + encoder.model = "VAE", + encoder.input = "KF", + reduction.name = "Trex.TRB.KF") + +plot1 <- DimPlot(SeuratObj, + reduction = "Trex.TRA.KF", + group.by = "orig.ident") + + scale_color_viridis(option = "B", discrete = TRUE)+ + theme(plot.title = element_blank()) + +plot2 <- DimPlot(SeuratObj, + reduction = "Trex.TRB.KF", + group.by = "orig.ident") + + scale_color_viridis(option = "B", discrete = TRUE) + + theme(plot.title = element_blank()) + +plot1 + plot2 +``` + + + + +```{r} +SeuratObj <- SeuratObj %>% + NormalizeData(verbose = FALSE) %>% + FindVariableFeatures(verbose = FALSE) %>% + quietTCRgenes() %>% + ScaleData(verbose = FALSE) %>% + RunPCA(verbose = FALSE) + + +rescaled.output <- rescaleByNeighbors( + list(RNA = SeuratObj@reductions$pca@cell.embeddings, + TRA = SeuratObj@reductions$Trex.TRA.KF@cell.embeddings, + TRB = SeuratObj@reductions$Trex.TRB.KF@cell.embeddings) + ) +``` + + + + + + diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index 11918c51..725bbd28 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -537,7 +537,7 @@ clonalSizeDistribution(combined, ## clonalDiversity -Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: "shannon" "inv.simpson", "norm.entropy", "gini.simpson", "chao1", and "ACE". Please see the manual for more information on each metric and the underlying calculations. ```clonalDiversity()``` will automatically downsample to the smallest repertoire size and perform boostapping to return the median diversity estimates. +Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: "shannon" "inv.simpson", "norm.entropy", "gini.simpson", "chao1", and "ACE". Please see the manual for more information on each metric and the underlying calculations. ```clonalDiversity()``` will automatically downsample to the smallest repertoire size and perform bootstrapping to return the median diversity estimates. **n.boots** The number of calculations to perform @@ -637,7 +637,7 @@ Next, we can attach the clonotypic information to our Seurat object using the `c We can call (cloneCall) the 4 variations of clonotypes: 1) VDJC genes, 2) CDR3 amino acid sequence, 3) CDR3 nucleotide sequence, or 4) VDJC genes and CDR3 nucleotide sequence. The attaching function will also calculate the clonotype frequency based on the **group.by** variable. If blank, **group.by** will calculate frequencies of clonotypes by individual run, but because we have 8 samples of paired peripheral blood and lung T cells, we will use the group.by variable to call "sample" to calculate frequencies across the same patient's blood and lung T cells. -In order to categorize the frequency, we have the variable **proportion**, which if TRUE, allows for the relative proportion or when FALSE, will use absolute frequency to define clonotype groups **cloneSize** acts as a bin to place labels. As a default, **cloneSize** is set to equal cloneTypes=c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1). However, below is an example of using total frequency as expansion assignments. +In order to categorize the frequency, we have the variable **proportion**, which if TRUE, allows for the relative proportion or when FALSE, will use absolute frequency to define clonotype groups **cloneSize** acts as a bin to place labels. As a default, **cloneSize** is set to equal: Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, and Hyperexpanded = 1. However, below is an example of using total frequency as expansion assignments. ```{r} sce <- combineExpression(combined, From 451f85226ecce437018e1bd484a3d8a80fb2f9f8 Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Tue, 31 Oct 2023 06:55:08 -0700 Subject: [PATCH 107/142] slight code refactoring and rcpp kmer counting --- R/RcppExports.R | 4 -- R/percentKmer.R | 8 +++- man/percentKmer.Rd | 1 + src/RcppExports.cpp | 12 +----- src/hello_world.cpp | 6 --- src/kmer.cpp | 94 ++++++++++++++++++++++++++++----------------- 6 files changed, 68 insertions(+), 57 deletions(-) delete mode 100644 src/hello_world.cpp diff --git a/R/RcppExports.R b/R/RcppExports.R index 71062fe8..3bfd8f2f 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -1,10 +1,6 @@ # Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 -rcpp_hello_world <- function() { - invisible(.Call(`_scRepertoire_rcpp_hello_world`)) -} - rcppGenerateUniqueNtMotifs <- function(k) { .Call(`_scRepertoire_rcppGenerateUniqueNtMotifs`, k) } diff --git a/R/percentKmer.R b/R/percentKmer.R index ddf13aac..214e06ac 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -11,7 +11,7 @@ #' percentKmer(combined, #' chain = "TRB", #' motif.length = 3) - +#' #' @param input.data The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. #' @param chain "TRA", "TRB", "TRG", "TRG", "IGH", "IGL". @@ -67,6 +67,12 @@ percentKmer <- function(input.data, for(i in seq_along(input.data)) { input.data[[i]][,cloneCall][which(input.data[[i]][,cloneCall] == "NA")] <- NA input.data[[i]] <- input.data[[i]][!is.na(input.data[[i]][,cloneCall]),] + + if (identical(cloneCall, "CTnt") && motif.length > 1) { + mat[i, ] <- rcppGetNtKmerPercent(input.data[[i]][,cloneCall], motif.length) + next + } + motifs <- .tokenize_multiple_sequences(input.data[[i]][,cloneCall], motif.length) motif.table <- table(unlist(motifs)) if(any(grepl("\\;", names(motif.table)))) { diff --git a/man/percentKmer.Rd b/man/percentKmer.Rd index fee9fcd7..3ed4eeaf 100644 --- a/man/percentKmer.Rd +++ b/man/percentKmer.Rd @@ -50,5 +50,6 @@ combined <- combineTCR(contig_list, percentKmer(combined, chain = "TRB", motif.length = 3) + } \concept{Summarize_Repertoire} diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 8405d326..9390d7bb 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -10,15 +10,6 @@ Rcpp::Rostream& Rcpp::Rcout = Rcpp::Rcpp_cout_get(); Rcpp::Rostream& Rcpp::Rcerr = Rcpp::Rcpp_cerr_get(); #endif -// rcpp_hello_world -void rcpp_hello_world(); -RcppExport SEXP _scRepertoire_rcpp_hello_world() { -BEGIN_RCPP - Rcpp::RNGScope rcpp_rngScope_gen; - rcpp_hello_world(); - return R_NilValue; -END_RCPP -} // rcppGenerateUniqueNtMotifs Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k); RcppExport SEXP _scRepertoire_rcppGenerateUniqueNtMotifs(SEXP kSEXP) { @@ -31,7 +22,7 @@ BEGIN_RCPP END_RCPP } // rcppGetNtKmerPercent -const Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k); +Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k); RcppExport SEXP _scRepertoire_rcppGetNtKmerPercent(SEXP seqsSEXP, SEXP kSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; @@ -44,7 +35,6 @@ END_RCPP } static const R_CallMethodDef CallEntries[] = { - {"_scRepertoire_rcpp_hello_world", (DL_FUNC) &_scRepertoire_rcpp_hello_world, 0}, {"_scRepertoire_rcppGenerateUniqueNtMotifs", (DL_FUNC) &_scRepertoire_rcppGenerateUniqueNtMotifs, 1}, {"_scRepertoire_rcppGetNtKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetNtKmerPercent, 2}, {NULL, NULL, 0} diff --git a/src/hello_world.cpp b/src/hello_world.cpp deleted file mode 100644 index 02307bba..00000000 --- a/src/hello_world.cpp +++ /dev/null @@ -1,6 +0,0 @@ -#include - -// [[Rcpp::export]] -void rcpp_hello_world() { - Rcpp::Rcout << "hello, world!" << "\n"; -} diff --git a/src/kmer.cpp b/src/kmer.cpp index 4377c1b9..6349afea 100644 --- a/src/kmer.cpp +++ b/src/kmer.cpp @@ -1,4 +1,4 @@ -// nucleotide kmer counting +// bit-based nucleotide kmer counting #include #include @@ -6,39 +6,38 @@ inline const unsigned short int toIndex(const char x) { switch(x) { - case 'A':case 'a': - return 0; - case 'C':case 'c': - return 1; - case 'G':case 'g': - return 2; - default: - return 3; + case 'A': return 0; + case 'C': return 1; + case 'G': return 2; + default: return 3; } } -inline const std::string toKmer(int index, int k) { +inline const char lastNt(unsigned int index) { + switch(index & 3) { + case 0: return 'A'; + case 1: return 'C'; + case 2: return 'G'; + default: return 'T'; + } +} + +inline const std::string toKmer(unsigned int index, int k) { std::string kmer = ""; for (int i = 0; i < k; i++) { - int baseIndex = index % 4; - char base; - - if (baseIndex == 0) { - base = 'A'; - } else if (baseIndex == 1) { - base = 'C'; - } else if (baseIndex == 2) { - base = 'G'; - } else { - base = 'T'; - } - - kmer = base + kmer; - index /= 4; + kmer = lastNt(index) + kmer; + index >>= 2; } return kmer; } +inline bool isNt(char c) { + switch(c) { + case 'A': case 'C': case 'G': case 'T': return true; + default: return false; + } +} + // [[Rcpp::export]] Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k) { unsigned int numKmers = 1 << (k + k); @@ -49,14 +48,29 @@ Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k) { return motifs; } +inline void updateSkip(int& skip, char c, int k) { + if (!isNt(c)) { + skip = k; + } else if (skip > 0) { + skip--; + } +} + +// doesnt handle _NA_ for k = 1 inline void kmerCount(std::vector& bins, const unsigned int mask, const std::string& seq, int k) { + int skip = 0; unsigned int kmer = 0; + for (int i = 0; i < k - 1; i++) { kmer = (kmer << 2) | toIndex(seq[i]); + updateSkip(skip, seq[i], k); } - for (int i = k - 1; i < seq.size(); i++) { + + for (int i = k - 1; i < (int) seq.size(); i++) { kmer = ((kmer << 2) & mask) | toIndex(seq[i]); - bins[kmer]++; + + updateSkip(skip, seq[i], k); + if (skip == 0) {bins[kmer]++;} } } @@ -68,25 +82,35 @@ const long double sum(std::vector& v) { return n; } +Rcpp::NumericVector convertZerosToNA(std::vector& v, int numKmers) { + Rcpp::NumericVector converted (numKmers, R_NaReal); + for (int i = 0; i < numKmers; i++) { + if (v[i] > 0) { + converted[i] = v[i]; + } + } + return converted; +} + // [[Rcpp::export]] -std::vector rcppGetNtKmerPercent(const std::vector& seqs, const int k) { +Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k) { const unsigned int mask = (1 << (k + k)) - 1; int numKmers = mask + 1; - std::vector bins (numKmers); + std::vector bins (numKmers, 0); for (std::string seq : seqs) { kmerCount(bins, mask, seq, k); } - long double sumOfNonZeroTerms = sum(bins); - if (sumOfNonZeroTerms < 0.0001) { // == 0 - return bins; + long double binSum = sum(bins); + if (binSum == 0.0) { // pretty sure this can only happen if there arent valid seqs? + return Rcpp::NumericVector (numKmers, R_NaReal); } - const double scaleFactor = 1 / sumOfNonZeroTerms; - for (int i = 0; i < mask + 1; i++) { + const double scaleFactor = 1 / binSum; + for (int i = 0; i < numKmers; i++) { bins[i] *= scaleFactor; } - return bins; + return convertZerosToNA(bins, numKmers); } From 4ffed9e0b2c0eac030cbc08cdf31bf9620af5c30 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 31 Oct 2023 09:50:37 -0500 Subject: [PATCH 108/142] working through testthat backlog --- inst/WORDLIST | 1 + .../viz/abundancecontig-scaled-plot.svg | 70 - .../viz/clonalabundance-scaled-plot.svg | 78 + .../viz/clonalcompare-alluvial-plot.svg | 145 + .../viz/clonallength-both-chain-plot.svg | 503 +++ .../_snaps/viz/clonalquant-scaled-plot.svg | 90 + .../viz/clonalscatter-vignette-plot.new.svg | 2515 +++++++++++ .../viz/clonalscatter-vignette-plot.svg | 2515 +++++++++++ .../clonalsizedistribution-vignette-plot.svg | 94 + .../clonesizedistribution-vignette-plot.svg | 82 - .../viz/compareclonotypes-alluvial-plot.svg | 141 - .../viz/compareclonotypes-area-plot.svg | 95 - .../viz/lengthcontig-both-chain-plot.svg | 393 -- .../_snaps/viz/quantcontig-scaled-plot.svg | 80 - .../viz/quantcontig-single-sample-plot.svg | 44 - .../viz/scatterclonotype-vignette-plot.svg | 3841 ----------------- .../_snaps/viz/vizgenes-bar-vignette-plot.svg | 1583 ++++--- .../viz/vizgenes-heatmap-vignette-plot.svg | 2328 +++++----- tests/testthat/test-combineContigs.R | 3 +- tests/testthat/test-seuratFunctions.R | 4 +- tests/testthat/test-seurat_command.R | 1 - tests/testthat/test-viz.R | 49 +- .../combineTCR_list_expected.rds | Bin 1344 -> 1360 bytes .../combineExpression_new_metadata.rds | Bin 7629 -> 30455 bytes .../utils/filteringMulti_expected.rds | Bin 771 -> 897 bytes 25 files changed, 8116 insertions(+), 6539 deletions(-) delete mode 100644 tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalcompare-alluvial-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonallength-both-chain-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalquant-scaled-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalscatter-vignette-plot.new.svg create mode 100644 tests/testthat/_snaps/viz/clonalscatter-vignette-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalsizedistribution-vignette-plot.svg delete mode 100644 tests/testthat/_snaps/viz/clonesizedistribution-vignette-plot.svg delete mode 100644 tests/testthat/_snaps/viz/compareclonotypes-alluvial-plot.svg delete mode 100644 tests/testthat/_snaps/viz/compareclonotypes-area-plot.svg delete mode 100644 tests/testthat/_snaps/viz/lengthcontig-both-chain-plot.svg delete mode 100644 tests/testthat/_snaps/viz/quantcontig-scaled-plot.svg delete mode 100644 tests/testthat/_snaps/viz/quantcontig-single-sample-plot.svg delete mode 100644 tests/testthat/_snaps/viz/scatterclonotype-vignette-plot.svg diff --git a/inst/WORDLIST b/inst/WORDLIST index 3aea16a1..75d3839d 100644 --- a/inst/WORDLIST +++ b/inst/WORDLIST @@ -9,6 +9,7 @@ BCRs BD Barcode BioC +CASSVRRERANTGELFF CDR CMD COVID diff --git a/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg b/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg deleted file mode 100644 index 94131b11..00000000 --- a/tests/testthat/_snaps/viz/abundancecontig-scaled-plot.svg +++ /dev/null @@ -1,70 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0 -1000 -2000 - - - - - - - -1 -10 -100 -Abundance -Number of Clonotypes - -Samples - - - - - - -PY_P -PY_T -PX_P -PX_T -PZ_P -PZ_T -abundanceContig_scaled_plot - - diff --git a/tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg b/tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg new file mode 100644 index 00000000..23042354 --- /dev/null +++ b/tests/testthat/_snaps/viz/clonalabundance-scaled-plot.svg @@ -0,0 +1,78 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0 +2000 +4000 + + + + + + + + +1 +10 +100 +1000 +Abundance +Number of Clonotypes + +Samples + + + + + + + + +P17B +P17L +P18B +P18L +P19B +P19L +P20B +P20L +clonalAbundance_scaled_plot + + diff --git a/tests/testthat/_snaps/viz/clonalcompare-alluvial-plot.svg b/tests/testthat/_snaps/viz/clonalcompare-alluvial-plot.svg new file mode 100644 index 00000000..b2fd5f38 --- /dev/null +++ b/tests/testthat/_snaps/viz/clonalcompare-alluvial-plot.svg @@ -0,0 +1,145 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.2 +0.4 +0.6 + + + + + + + +P17B +P17L +Proportion + +clones + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +CAASETSYDKVIF_CASSSGEKGYTF +CAVEDQVTGGFKTIF_CAAGQGVMNTEAFF +CAVGAQQGGKLIF_CASSLSLSDGRHGYTF +CAVQAGDSWGKLQF_CASSYGGTEAFF +CAVRKTTGTASKLTF_CASSLFGDKGETQYF +CAVRPLVASKIIF_CASSQDRAQNRQGPNTGELFF +CAVTFHYNTDKLIF_CASSQDRTGLDYEQYF +CVVSDFGNEKLTF_CASSLGSGGTGNEQFF +CVVSDNTGGFKTIF_CASSVRRERANTGELFF +NA_CAAGQGVMNTEAFF +NA_CASSQDRTGLDYEQYF +NA_CASSVRRERANTGELFF +CAASIAGGNKLTF_CASSLGQGSIGAFF +CACLYGGSQGNLIF_CASSLTRGLENIQYF +CALSDGPLVAGNMLTF_CASSLGQGIYNEQFF +CIVRVATGNQFYF_CASSRSDRVHTGELFF +CVVSDPRGYNKLIF_CASSPTTGGTGELFF +NA_CASSLGQGIYNEQFF +NA_CASSRSDRVHTGELFF +clonalCompare_alluvial_plot + + diff --git a/tests/testthat/_snaps/viz/clonallength-both-chain-plot.svg b/tests/testthat/_snaps/viz/clonallength-both-chain-plot.svg new file mode 100644 index 00000000..e2366e49 --- /dev/null +++ b/tests/testthat/_snaps/viz/clonallength-both-chain-plot.svg @@ -0,0 +1,503 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + @@ -1122,65 +1187,70 @@ - -TRBV10-1 -TRBV10-2 -TRBV10-3 -TRBV11-1 -TRBV11-2 -TRBV11-3 -TRBV12-3 -TRBV12-4 -TRBV12-5 -TRBV13 -TRBV14 -TRBV15 -TRBV16 -TRBV18 -TRBV19 -TRBV2 -TRBV20-1 -TRBV21-1 -TRBV24-1 -TRBV25-1 -TRBV27 -TRBV28 -TRBV29-1 -TRBV3-1 -TRBV30 -TRBV4-1 -TRBV4-2 -TRBV4-3 -TRBV5-1 -TRBV5-3 -TRBV5-4 -TRBV5-5 -TRBV5-6 -TRBV5-8 -TRBV6-1 -TRBV6-3 -TRBV6-4 -TRBV6-5 -TRBV6-6 -TRBV7-2 -TRBV7-3 -TRBV7-6 -TRBV7-7 -TRBV7-8 -TRBV7-9 -TRBV9 - - -0.02 -0.04 -0.06 -Scaled Values - - - - - - + +TRBV2 +TRBV3-1 +TRBV4-1 +TRBV4-2 +TRBV4-3 +TRBV5-1 +TRBV5-4 +TRBV5-5 +TRBV5-6 +TRBV5-7 +TRBV5-8 +TRBV6-1 +TRBV6-2 +TRBV6-4 +TRBV6-5 +TRBV6-6 +TRBV6-7 +TRBV6-9 +TRBV7-2 +TRBV7-3 +TRBV7-4 +TRBV7-6 +TRBV7-7 +TRBV7-8 +TRBV7-9 +TRBV9 +TRBV10-1 +TRBV10-2 +TRBV10-3 +TRBV11-1 +TRBV11-2 +TRBV11-3 +TRBV12-3 +TRBV12-4 +TRBV12-5 +TRBV13 +TRBV14 +TRBV15 +TRBV16 +TRBV18 +TRBV19 +TRBV20-1 +TRBV24-1 +TRBV25-1 +TRBV27 +TRBV28 +TRBV29-1 +TRBV30 + + +-8 +-6 +-4 +-2 +Log(Scaled Values) + + + + + + + + vizGenes_heatmap_vignette_plot diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R index 8aa8756b..b02a5b41 100644 --- a/tests/testthat/test-combineContigs.R +++ b/tests/testthat/test-combineContigs.R @@ -4,9 +4,10 @@ test_that("combineTCR works", { data("contig_list") trial1 <- combineTCR( - df = lapply(contig_list[1:3], head), + input.data = lapply(contig_list[1:3], head), samples = c("P17B", "P17L", "P18B") ) + expected1 <- readRDS("testdata/combineContigs/combineTCR_list_expected.rds") expect_identical(trial1, expected1) diff --git a/tests/testthat/test-seuratFunctions.R b/tests/testthat/test-seuratFunctions.R index 1947ecc4..8968bc97 100644 --- a/tests/testthat/test-seuratFunctions.R +++ b/tests/testthat/test-seuratFunctions.R @@ -7,10 +7,10 @@ test_that("combineExpression works with seurat objects", { "P19B","P19L", "P20B", "P20L")) combined_test <- combineExpression(combined, scRep_example) - expect_length(combined_test@meta.data, 12) + expect_length(combined_test@meta.data, 13) expect_equal(combined_test@meta.data[, 1:6], scRep_example@meta.data[, 1:6]) expect_equal( - combined_test@meta.data[, 7:12], + combined_test@meta.data[, 7:13], getdata("seuratFunctions", "combineExpression_new_metadata") ) }) diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R index c3cafd51..a48946f1 100644 --- a/tests/testthat/test-seurat_command.R +++ b/tests/testthat/test-seurat_command.R @@ -20,7 +20,6 @@ test_that("make_screp_seurat_cmd works", { list( cloneCall = "CTstrict", chain = "both", - group.by = NULL, proportion = TRUE, filterNA = FALSE, cloneSize = c( diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R index 0eae97da..395f2a4a 100644 --- a/tests/testthat/test-viz.R +++ b/tests/testthat/test-viz.R @@ -19,24 +19,18 @@ test_that("clonalQuant works", { "clonalQuant_scaled_plot", clonalQuant(combined, scale = TRUE) ) expect_equal( - clonalQuant(combined, scale = TRUE, exportTable = TRUE), + clonalQuant(combined, + scale = TRUE, + exportTable = TRUE), data.frame( "contigs" = c(745L, 2117L, 1254L, 1202L, 5544L, 1619L, 6087L, 192L), "values" = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"), "total" = c(2805L, 2893L, 1328L, 1278L, 6942L, 2747L, 8991L, 201L), "scaled" = c( - 26.55971, 73.17663, 94.42771, 94.05321, 79.86171, 58.93702, 67.70103, 95.52239 + 26.5597148, 73.1766333, 94.4277108, 94.0532081, 79.8617113, 58.9370222, 67.7010344, 95.5223881 ) ) ) - - expect_doppelganger( - "clonalQuant_single_sample_plot", clonalQuant(single_contig) - ) - expect_identical( - clonalQuant(single_contig, exportTable = TRUE), - data.frame("contigs" = 745L, "total" = 2805L) - ) }) test_that("clonalAbundance works", { @@ -56,7 +50,7 @@ test_that("clonalCompare works", { "clonalCompare_alluvial_plot", clonalCompare( combined, - numbers = 10, + top.clones = 10, samples = c("P17B", "P17L"), cloneCall="aa", graph = "alluvial" @@ -66,8 +60,8 @@ test_that("clonalCompare works", { expect_doppelganger( "clonalCompare_area_plot", clonalCompare( - combined, - numbers = 10, + input.data = combined, + top.clones = 10, samples = c("P17B", "P17L"), cloneCall="aa", graph = "area" @@ -75,22 +69,7 @@ test_that("clonalCompare works", { ) }) -test_that("clonalScatter works", { - expect_doppelganger( - "clonalScatter_vignette_plot", - clonalScatter( - combined, - cloneCall ="gene", - x.axis = "P17B", - y.axis = "P17L", - dot.size = "total", - graph = "proportion", - seed = 42 - ) - ) - - # TODO test the exportTable arg -}) +# TODO clonalScatter # something in `clonesizeDistribution` prints "NULL" to the terminal test_that("clonalSizeDistribution works", { @@ -102,14 +81,15 @@ test_that("clonalSizeDistribution works", { # TODO makingLodes + test_that("vizGenes works", { expect_doppelganger( "vizGenes_bar_vignette_plot", vizGenes( combined, - gene = "V", - chain = "TRB", - plot = "bar", + x.axis = "TRBV", + y.axis = NULL, + plot = "barplot", order = "variance", scale = TRUE ) @@ -119,9 +99,8 @@ test_that("vizGenes works", { "vizGenes_heatmap_vignette_plot", vizGenes( combined[c(1,3,5)], - gene = "V", - chain = "TRB", - y.axis = "J", + x.axis = "TRBV", + y.axis = "TRBJ", plot = "heatmap", scale = TRUE, order = "gene" diff 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R/combineContigs.R | 8 +- R/percentKmer.R | 4 +- R/utils.R | 6 +- src/{kmer.cpp => ntKmers.cpp} | 45 +- src/screpUtils.h | 21 + .../_snaps/viz/clonalcompare-area-plot.svg | 91 + .../viz/clonalscatter-vignette-plot.new.svg | 2515 ----------------- .../viz/clonalscatter-vignette-plot.svg | 2515 ----------------- tests/testthat/test-percentKmer.R | 3 +- tests/testthat/test-utils.R | 21 +- tests/testthat/testdata/utils/Con.df.rds | Bin 0 -> 523 bytes tests/testthat/testdata/utils/data2.rds | Bin 0 -> 4449 bytes .../testdata/utils/parseTCR_result.rds | Bin 0 -> 2214 bytes 15 files changed, 156 insertions(+), 5077 deletions(-) rename src/{kmer.cpp => ntKmers.cpp} (67%) create mode 100644 src/screpUtils.h create mode 100644 tests/testthat/_snaps/viz/clonalcompare-area-plot.svg delete mode 100644 tests/testthat/_snaps/viz/clonalscatter-vignette-plot.new.svg delete mode 100644 tests/testthat/_snaps/viz/clonalscatter-vignette-plot.svg create mode 100644 tests/testthat/testdata/utils/Con.df.rds create mode 100644 tests/testthat/testdata/utils/data2.rds create mode 100644 tests/testthat/testdata/utils/parseTCR_result.rds diff --git a/.Rbuildignore b/.Rbuildignore index 184dd927..d18a20b4 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -10,3 +10,4 @@ ^docs$ ^pkgdown$ ^index\.md$ +^dev\.R$ \ No newline at end of file diff --git a/.gitignore b/.gitignore index 89ff4ce0..e419020e 100644 --- a/.gitignore +++ b/.gitignore @@ -6,4 +6,5 @@ local_tests.R .RData docs vignettes/articles/scRep_example_full.rds -.vscode \ No newline at end of file +.vscode +dev.R \ No newline at end of file diff --git a/R/combineContigs.R b/R/combineContigs.R index d8136681..06f22e55 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -86,14 +86,14 @@ combineTCR <- function(input.data, } else { out <- input.data } - for (i in seq_along(out)) { # ideally the nested code could be in a function for a better development/testing experience + for (i in seq_along(out)) { data2 <- out[[i]] data2 <- .makeGenes(cellType = "T", data2) unique_df <- unique(data2$barcode) # could potentially display % here Con.df <- data.frame(matrix(NA, length(unique_df), 7)) colnames(Con.df) <- c("barcode",tcr1_lines, tcr2_lines) Con.df$barcode <- unique_df - Con.df <- .parseTCR(Con.df, unique_df, data2) + Con.df <- .parseTCR(Con.df, unique_df, data2) # from profvis, this takes the most runtime Con.df <- .assignCT(cellType = "T", Con.df) Con.df[Con.df == "NA_NA" | Con.df == "NA;NA_NA;NA"] <- NA data3 <- merge(data2[,-which(names(data2) %in% c("TCR1","TCR2"))], @@ -121,10 +121,10 @@ combineTCR <- function(input.data, final[[i]]<-final[[i]][!duplicated(final[[i]]$barcode),] final[[i]]<-final[[i]][rowSums(is.na(final[[i]])) < 10, ] } - if (removeNA) { + if (removeNA) { final <- .removingNA(final) } - if (removeMulti) { + if (removeMulti) { final <- .removingMulti(final) } final diff --git a/R/percentKmer.R b/R/percentKmer.R index 214e06ac..511e1401 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -68,7 +68,7 @@ percentKmer <- function(input.data, input.data[[i]][,cloneCall][which(input.data[[i]][,cloneCall] == "NA")] <- NA input.data[[i]] <- input.data[[i]][!is.na(input.data[[i]][,cloneCall]),] - if (identical(cloneCall, "CTnt") && motif.length > 1) { + if (identical(cloneCall, "CTnt") && motif.length > 1L && motif.length <= 32L) { mat[i, ] <- rcppGetNtKmerPercent(input.data[[i]][,cloneCall], motif.length) next } @@ -108,7 +108,7 @@ percentKmer <- function(input.data, #Plotting ggplot(mat_melt, aes(x=Var2, y = Var1, fill=value)) + - geom_tile(lwd= 0.1, color = "black") + + geom_tile(lwd = 0.1, color = "black") + scale_fill_gradientn(name = "Percentage", colors = .colorizer(palette,21)) + theme_classic() + coord_flip() + diff --git a/R/utils.R b/R/utils.R index 2e865900..b9d232f7 100644 --- a/R/utils.R +++ b/R/utils.R @@ -293,7 +293,7 @@ is_seurat_or_se_object <- function(obj) { where.chain <- data2[location.i[z],"chain"] if (where.chain == "TRA" | where.chain == "TRG") { - if (is.na(Con.df[y,"TCR1"])) { + if (is.na(Con.df[y,"TCR1"])) { Con.df[y,tcr1_lines] <- data2[location.i[z],data1_lines] } else { Con.df[y,tcr1_lines] <- paste( @@ -303,8 +303,8 @@ is_seurat_or_se_object <- function(obj) { ) } } else if (where.chain == "TRB" | where.chain == "TRD") { - if (is.na(Con.df[y,"TCR2"])) { - Con.df[y,tcr2_lines] <- data2[location.i[z],data2_lines] + if (is.na(Con.df[y,"TCR2"])) { # 306 and 307 here are speed bottlenecks + Con.df[y,tcr2_lines] <- data2[location.i[z],data2_lines] } else { Con.df[y,tcr2_lines] <- paste( Con.df[y, tcr2_lines], diff --git a/src/kmer.cpp b/src/ntKmers.cpp similarity index 67% rename from src/kmer.cpp rename to src/ntKmers.cpp index 6349afea..30863bc4 100644 --- a/src/kmer.cpp +++ b/src/ntKmers.cpp @@ -1,11 +1,13 @@ -// bit-based nucleotide kmer counting +// 2-bit-based nucleotide kmer counting +// by Qile Yang #include #include #include +#include "screpUtils.h" -inline const unsigned short int toIndex(const char x) { - switch(x) { +inline unsigned short int toNtIndex(const char nt) { + switch(nt) { case 'A': return 0; case 'C': return 1; case 'G': return 2; @@ -13,7 +15,7 @@ inline const unsigned short int toIndex(const char x) { } } -inline const char lastNt(unsigned int index) { +inline char lastNt(unsigned int index) { switch(index & 3) { case 0: return 'A'; case 1: return 'C'; @@ -22,7 +24,7 @@ inline const char lastNt(unsigned int index) { } } -inline const std::string toKmer(unsigned int index, int k) { +inline std::string toNtKmer(unsigned long int index, int k) { std::string kmer = ""; for (int i = 0; i < k; i++) { kmer = lastNt(index) + kmer; @@ -40,10 +42,10 @@ inline bool isNt(char c) { // [[Rcpp::export]] Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k) { - unsigned int numKmers = 1 << (k + k); + long int numKmers = 1 << (k + k); Rcpp::CharacterVector motifs (numKmers); - for (unsigned int i = 0; i < numKmers; i++) { - motifs[i] = toKmer(i, k); + for (unsigned long int i = 0; i < numKmers; i++) { + motifs[i] = toNtKmer(i, k); } return motifs; } @@ -56,42 +58,23 @@ inline void updateSkip(int& skip, char c, int k) { } } -// doesnt handle _NA_ for k = 1 +// actual kmer counter - doesnt handle _NA_ for k = 1 inline void kmerCount(std::vector& bins, const unsigned int mask, const std::string& seq, int k) { int skip = 0; - unsigned int kmer = 0; + unsigned long int kmer = 0; for (int i = 0; i < k - 1; i++) { - kmer = (kmer << 2) | toIndex(seq[i]); + kmer = (kmer << 2) | toNtIndex(seq[i]); updateSkip(skip, seq[i], k); } for (int i = k - 1; i < (int) seq.size(); i++) { - kmer = ((kmer << 2) & mask) | toIndex(seq[i]); - + kmer = ((kmer << 2) & mask) | toNtIndex(seq[i]); updateSkip(skip, seq[i], k); if (skip == 0) {bins[kmer]++;} } } -const long double sum(std::vector& v) { - long double n = 0; - for (long double num : v) { - n += num; - } - return n; -} - -Rcpp::NumericVector convertZerosToNA(std::vector& v, int numKmers) { - Rcpp::NumericVector converted (numKmers, R_NaReal); - for (int i = 0; i < numKmers; i++) { - if (v[i] > 0) { - converted[i] = v[i]; - } - } - return converted; -} - // [[Rcpp::export]] Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, const int k) { const unsigned int mask = (1 << (k + k)) - 1; diff --git a/src/screpUtils.h b/src/screpUtils.h new file mode 100644 index 00000000..892fae6e --- /dev/null +++ b/src/screpUtils.h @@ -0,0 +1,21 @@ +#include +#include +#include + +const long double sum(std::vector& v) { + long double n = 0; + for (long double num : v) { + n += num; + } + return n; +} + +Rcpp::NumericVector convertZerosToNA(std::vector& v, int len) { + Rcpp::NumericVector converted (len, R_NaReal); + for (int i = 0; i < len; i++) { + if (v[i] > 0) { + converted[i] = v[i]; + } + } + return converted; +} diff --git a/tests/testthat/_snaps/viz/clonalcompare-area-plot.svg b/tests/testthat/_snaps/viz/clonalcompare-area-plot.svg new file mode 100644 index 00000000..b905f239 --- /dev/null +++ b/tests/testthat/_snaps/viz/clonalcompare-area-plot.svg @@ -0,0 +1,91 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 + + + + + + + + + +P17B +P17L +Proportion + +clones + + + + + + + + + +CAASETSYDKVIF_CASSSGEKGYTF +CAVGAQQGGKLIF_CASSLSLSDGRHGYTF +CAVQAGDSWGKLQF_CASSYGGTEAFF +CAVRKTTGTASKLTF_CASSLFGDKGETQYF +CAVTFHYNTDKLIF_CASSQDRTGLDYEQYF +CVVSDNTGGFKTIF_CASSVRRERANTGELFF +NA_CAAGQGVMNTEAFF +NA_CASSQDRTGLDYEQYF +NA_CASSVRRERANTGELFF +clonalCompare_area_plot + + diff --git a/tests/testthat/_snaps/viz/clonalscatter-vignette-plot.new.svg b/tests/testthat/_snaps/viz/clonalscatter-vignette-plot.new.svg deleted file mode 100644 index 1e1d459f..00000000 --- a/tests/testthat/_snaps/viz/clonalscatter-vignette-plot.new.svg +++ /dev/null @@ -1,2515 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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--git a/tests/testthat/_snaps/viz/clonalscatter-vignette-plot.svg b/tests/testthat/_snaps/viz/clonalscatter-vignette-plot.svg deleted file mode 100644 index dd34888d..00000000 --- a/tests/testthat/_snaps/viz/clonalscatter-vignette-plot.svg +++ /dev/null @@ -1,2515 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.000 -0.005 -0.010 -0.015 -0.020 - - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -P17B -P17L - -Total n - - - -250 -500 -750 - -class - - - - - -P17B.expanded -P17B.singlet -P17L.expanded -P17L.singlet -dual.expanded -clonalScatter_vignette_plot - - diff --git a/tests/testthat/test-percentKmer.R b/tests/testthat/test-percentKmer.R index e5fa539b..aef10cb6 100644 --- a/tests/testthat/test-percentKmer.R +++ b/tests/testthat/test-percentKmer.R @@ -1,6 +1,6 @@ combined <- combineTCR( contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L") + samples = c("P17B", "P17L", "P18B", "P18L", "P19B", "P19L", "P20B", "P20L") ) test_that("percentKmer works for AAs", { @@ -18,6 +18,7 @@ test_that("percentKmer works for NTs", { top_30_nt_3mer_composition_matrix <- getdata( "percentKmer", "top_30_nt_3mer_composition_matrix" ) + expect_equal( percentKmer(combined, cloneCall = "nt", exportTable = TRUE), top_30_nt_3mer_composition_matrix diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index e8ee395f..60be0b5c 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -20,7 +20,7 @@ test_that("checkList works", { expect_identical(.checkList(contig_list), contig_list) expect_identical(.checkList(contig_list[[1]]), list(contig_list[[1]])) expect_identical(.checkList(contig_list[[1]])[[1]], contig_list[[1]]) - # no idea what to put to make the stop message happen. perhaps with data.table(contig_list) ? but then it shouldnt fail :P + # no idea what to put to make the stop message happen. }) # TODO bound.input.return @@ -50,7 +50,7 @@ test_that("Modifying barcodes without ID works correctly", { }) ############## -##TODO figure out where getdata is from? +## resolved TODO: getdata is from 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.../_snaps/viz/clonalcompare-area-plot.svg | 91 + .../viz/clonalscatter-vignette-plot.new.svg | 2515 ----------------- .../viz/clonalscatter-vignette-plot.svg | 2515 ----------------- tests/testthat/test-clonalDiversity.R | 25 + tests/testthat/test-clonalHomeostasis.R | 20 + tests/testthat/test-clonalOverlap.R | 25 + tests/testthat/test-clonalProportion.R | 20 + tests/testthat/test-clonalScatter.R | 18 + ...atFunctions.R => test-combineExpression.R} | 17 +- tests/testthat/test-viz.R | 5 +- .../clonalDiversity_exportTable.rds | Bin 0 -> 349 bytes .../clonalHomeostasis_exportTable.rds | Bin 0 -> 418 bytes .../clonalProportion_exportTable.rds | Bin 0 -> 281 bytes .../clonalScatter_exportTable.rds | Bin 0 -> 27985 bytes 19 files changed, 3483 insertions(+), 5041 deletions(-) create mode 100644 tests/testthat/_snaps/clonalDiversity/clonaldiversity-plot.svg create mode 100644 tests/testthat/_snaps/clonalHomeostasis/clonalhomeostasis-plot.svg create mode 100644 tests/testthat/_snaps/clonalOverlap/clonaloverlap-raw-plot.svg create mode 100644 tests/testthat/_snaps/clonalProportion/clonalproportion-plot.svg create mode 100644 tests/testthat/_snaps/clonalScatter/clonalscatter-plot.svg create mode 100644 tests/testthat/_snaps/viz/clonalcompare-area-plot.svg delete mode 100644 tests/testthat/_snaps/viz/clonalscatter-vignette-plot.new.svg delete mode 100644 tests/testthat/_snaps/viz/clonalscatter-vignette-plot.svg create mode 100644 tests/testthat/test-clonalDiversity.R create mode 100644 tests/testthat/test-clonalHomeostasis.R create mode 100644 tests/testthat/test-clonalOverlap.R create mode 100644 tests/testthat/test-clonalProportion.R create mode 100644 tests/testthat/test-clonalScatter.R rename tests/testthat/{test-seuratFunctions.R => test-combineExpression.R} (58%) create mode 100644 tests/testthat/testdata/visualizations/clonalDiversity_exportTable.rds create mode 100644 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- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.000 -0.005 -0.010 -0.015 -0.020 - - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -P17B -P17L - -Total n - - - -250 -500 -750 - -class - - - - - -P17B.expanded -P17B.singlet -P17L.expanded -P17L.singlet -dual.expanded -clonalScatter_vignette_plot - - diff --git a/tests/testthat/test-clonalDiversity.R b/tests/testthat/test-clonalDiversity.R new file mode 100644 index 00000000..cf91f864 --- /dev/null +++ b/tests/testthat/test-clonalDiversity.R @@ -0,0 +1,25 @@ +# test script for clonalDiversity.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalDiversity works", { + set.seed(42) + expect_doppelganger( + "clonalDiversity_plot", + clonalDiversity(combined, + cloneCall = "gene", + n.boots = 1) + ) + set.seed(42) + expect_equal( + clonalDiversity(combined, + cloneCall = "gene", + skip.boots = TRUE, + metrics = c("norm.entropy", "shannon"), + exportTable = TRUE), + getdata("visualizations", "clonalDiversity_exportTable") + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-clonalHomeostasis.R b/tests/testthat/test-clonalHomeostasis.R new file mode 100644 index 00000000..0e88c236 --- /dev/null +++ b/tests/testthat/test-clonalHomeostasis.R @@ -0,0 +1,20 @@ +# test script for clonalHomeostasis.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalDiversity works", { + expect_doppelganger( + "clonalHomeostasis_plot", + clonalHomeostasis(combined, + cloneCall = "gene") + ) + expect_equal( + clonalHomeostasis(combined, + cloneCall = "gene", + exportTable = TRUE), + getdata("visualizations", "clonalHomeostasis_exportTable") + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-clonalOverlap.R b/tests/testthat/test-clonalOverlap.R new file mode 100644 index 00000000..e8451af1 --- /dev/null +++ b/tests/testthat/test-clonalOverlap.R @@ -0,0 +1,25 @@ +# test script for clonalOverlap.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalOverlap works", { + expect_doppelganger( + "clonalOverlap_raw_plot", + clonalOverlap( + combined, + method = "raw") + ) + expect_equal( + clonalOverlap( + combined[c(1,2)], + method = "raw", + exportTable = TRUE), + data.frame(row.names = c("P17B", "P17L"), + "P17B" = rep(NA, 2), + "P17L" = c(85L, NA) + ) + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-clonalProportion.R b/tests/testthat/test-clonalProportion.R new file mode 100644 index 00000000..9012b7db --- /dev/null +++ b/tests/testthat/test-clonalProportion.R @@ -0,0 +1,20 @@ +# test script for clonalProportion.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalProportion works", { + expect_doppelganger( + "clonalProportion_plot", + clonalProportion(combined, + cloneCall = "gene") + ) + expect_equal( + clonalProportion(combined, + cloneCall = "gene", + exportTable = TRUE), + getdata("visualizations", "clonalProportion_exportTable") + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-clonalScatter.R b/tests/testthat/test-clonalScatter.R new file mode 100644 index 00000000..7ea043c2 --- /dev/null +++ b/tests/testthat/test-clonalScatter.R @@ -0,0 +1,18 @@ +# test script for clonalScatter.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalScatter works", { + set.seed(42) + expect_doppelganger( + "clonalScatter_plot", + clonalScatter(combined, + cloneCall = "gene", + y.axis = "P17B", + x.axis = "P17L") + ) +##TODO add exportTable +}) \ No newline at end of file diff --git a/tests/testthat/test-seuratFunctions.R b/tests/testthat/test-combineExpression.R similarity index 58% rename from tests/testthat/test-seuratFunctions.R rename to tests/testthat/test-combineExpression.R index 8968bc97..a11ca93f 100644 --- a/tests/testthat/test-seuratFunctions.R +++ b/tests/testthat/test-combineExpression.R @@ -1,4 +1,4 @@ -# test script for seuratFunctions.R - testcases are NOT comprehensive! +# test script for combineExpression.R - testcases are NOT comprehensive! test_that("combineExpression works with seurat objects", { data("mini_contig_list", "scRep_example") @@ -7,17 +7,18 @@ test_that("combineExpression works with seurat objects", { "P19B","P19L", "P20B", "P20L")) combined_test <- combineExpression(combined, scRep_example) + #Seurat object test expect_length(combined_test@meta.data, 13) expect_equal(combined_test@meta.data[, 1:6], scRep_example@meta.data[, 1:6]) expect_equal( combined_test@meta.data[, 7:13], getdata("seuratFunctions", "combineExpression_new_metadata") ) + + #Single-cell experiment test + #as.SingleCellExperiment will add an ident column 13 --> 14 + sce_test <- Seurat::as.SingleCellExperiment(scRep_example) + sce_test <- combineExpression(combined, sce_test) + expect_length(sce_test@colData, 14) + expect_equal(as.data.frame(sce_test@colData[, 1:6]), scRep_example@meta.data[, 1:6]) }) - -# TODO more testcases for combineEXpression, especially with SCE objects -# TODO highlightClonotypes -# 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.../clonalquant-scaled-plot.svg | 0 .../_snaps/percentAA/percentaa-plot.svg | 3727 +++++++++++ .../_snaps/percentGenes/percentgenes-plot.svg | 547 ++ .../_snaps/percentVJ/percentvj-plot.svg | 5589 +++++++++++++++++ .../clonalsizedistribution-vignette-plot.svg | 94 - .../vizgenes-bar-vignette-plot.svg | 0 .../vizgenes-heatmap-vignette-plot.svg | 0 tests/testthat/test-clonalAbundance.R | 15 + tests/testthat/test-clonalCompare.R | 30 + tests/testthat/test-clonalLength.R | 14 + tests/testthat/test-clonalQuant.R | 26 + tests/testthat/test-highlightClones.R | 18 + tests/testthat/test-percentAA.R | 24 + tests/testthat/test-percentGenes.R | 24 + tests/testthat/test-percentVJ.R | 22 + tests/testthat/test-viz.R | 108 - tests/testthat/test-vizGenes.R | 33 + .../seuratFunctions/highlightClones_meta.rds | Bin 0 -> 950 bytes .../visualizations/percentAA_exportTable.rds | Bin 0 -> 10439 bytes .../percentGenes_exportTable.rds | Bin 0 -> 2240 bytes .../visualizations/percentVJ_exportTable.rds | Bin 0 -> 8177 bytes 26 files changed, 10072 insertions(+), 203 deletions(-) rename tests/testthat/_snaps/{viz => clonalAbundance}/clonalabundance-scaled-plot.svg (100%) rename tests/testthat/_snaps/{viz => clonalCompare}/clonalcompare-alluvial-plot.svg (100%) rename tests/testthat/_snaps/{viz => clonalCompare}/clonalcompare-area-plot.svg (100%) rename tests/testthat/_snaps/{viz => clonalLength}/clonallength-both-chain-plot.svg (100%) rename tests/testthat/_snaps/{viz => clonalQuant}/clonalquant-scaled-plot.svg (100%) create mode 100644 tests/testthat/_snaps/percentAA/percentaa-plot.svg create mode 100644 tests/testthat/_snaps/percentGenes/percentgenes-plot.svg create mode 100644 tests/testthat/_snaps/percentVJ/percentvj-plot.svg delete mode 100644 tests/testthat/_snaps/viz/clonalsizedistribution-vignette-plot.svg rename tests/testthat/_snaps/{viz => vizGenes}/vizgenes-bar-vignette-plot.svg (100%) rename tests/testthat/_snaps/{viz => vizGenes}/vizgenes-heatmap-vignette-plot.svg (100%) create 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+TRBV3-1 +TRBV5-4 +TRBV6-2 +TRBV12-4 +TRBV27 +TRBV28 +TRBV11-2 +TRBV7-2 +TRBV4-3 +TRBV6-5 +TRBV6-1 +TRBV24-1 +TRBV19 +TRBV12-3 +TRBV16 +TRBV18 +TRBV2 +TRBV30 +TRBV6-6 +TRBV29-1 +TRBV4-2 +TRBV5-5 +TRBV10-2 +TRBV5-6 +TRBV5-8 +TRBV15 +TRBV13 +TRBV12-5 +TRBV14 +TRBV7-6 +TRBV7-7 +TRBV25-1 +TRBV6-4 +TRBV7-3 +TRBV10-1 +TRBV11-1 +TRBV7-4 +TRBV6-9 +TRBV6-7 +TRBV5-7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +TRBV5-1 +TRBV10-3 +TRBV9 +TRBV7-8 +TRBV20-1 +TRBV7-9 +TRBV4-1 +TRBV11-3 +TRBV3-1 +TRBV5-4 +TRBV6-2 +TRBV12-4 +TRBV27 +TRBV28 +TRBV11-2 +TRBV7-2 +TRBV4-3 +TRBV6-5 +TRBV6-1 +TRBV24-1 +TRBV19 +TRBV12-3 +TRBV16 +TRBV18 +TRBV2 +TRBV30 +TRBV6-6 +TRBV29-1 +TRBV4-2 +TRBV5-5 +TRBV10-2 +TRBV5-6 +TRBV5-8 +TRBV15 +TRBV13 +TRBV12-5 +TRBV14 +TRBV7-6 +TRBV7-7 +TRBV25-1 +TRBV6-4 +TRBV7-3 +TRBV10-1 +TRBV11-1 +TRBV7-4 +TRBV6-9 +TRBV6-7 +TRBV5-7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +TRBV5-1 +TRBV10-3 +TRBV9 +TRBV7-8 +TRBV20-1 +TRBV7-9 +TRBV4-1 +TRBV11-3 +TRBV3-1 +TRBV5-4 +TRBV6-2 +TRBV12-4 +TRBV27 +TRBV28 +TRBV11-2 +TRBV7-2 +TRBV4-3 +TRBV6-5 +TRBV6-1 +TRBV24-1 +TRBV19 +TRBV12-3 +TRBV16 +TRBV18 +TRBV2 +TRBV30 +TRBV6-6 +TRBV29-1 +TRBV4-2 +TRBV5-5 +TRBV10-2 +TRBV5-6 +TRBV5-8 +TRBV15 +TRBV13 +TRBV12-5 +TRBV14 +TRBV7-6 +TRBV7-7 +TRBV25-1 +TRBV6-4 +TRBV7-3 +TRBV10-1 +TRBV11-1 +TRBV7-4 +TRBV6-9 +TRBV6-7 +TRBV5-7 + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0 +10 +20 +30 +Percentage + + + + + + + + +percentVJ_plot + + diff --git a/tests/testthat/_snaps/viz/clonalsizedistribution-vignette-plot.svg b/tests/testthat/_snaps/viz/clonalsizedistribution-vignette-plot.svg deleted file mode 100644 index fdedf2e0..00000000 --- a/tests/testthat/_snaps/viz/clonalsizedistribution-vignette-plot.svg +++ /dev/null @@ -1,94 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -P19B -P20B -P20L -P17L -P19L -P17B -P18B -P18L - - - - - - - - - - - - - - - - - - - --0.1 -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 -clonalSizeDistribution_vignette_plot - - diff --git a/tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg b/tests/testthat/_snaps/vizGenes/vizgenes-bar-vignette-plot.svg similarity index 100% rename from tests/testthat/_snaps/viz/vizgenes-bar-vignette-plot.svg rename to tests/testthat/_snaps/vizGenes/vizgenes-bar-vignette-plot.svg diff --git a/tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg b/tests/testthat/_snaps/vizGenes/vizgenes-heatmap-vignette-plot.svg similarity index 100% rename from tests/testthat/_snaps/viz/vizgenes-heatmap-vignette-plot.svg rename to tests/testthat/_snaps/vizGenes/vizgenes-heatmap-vignette-plot.svg diff --git a/tests/testthat/test-clonalAbundance.R b/tests/testthat/test-clonalAbundance.R new file mode 100644 index 00000000..20f7cfdf --- /dev/null +++ b/tests/testthat/test-clonalAbundance.R @@ -0,0 +1,15 @@ +# test script for clonalAbundance.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalAbundance works", { + expect_doppelganger( + "clonalAbundance_scaled_plot", clonalAbundance(combined, scale = FALSE) + ) +}) + + diff --git a/tests/testthat/test-clonalCompare.R b/tests/testthat/test-clonalCompare.R new file mode 100644 index 00000000..d2a57b37 --- /dev/null +++ b/tests/testthat/test-clonalCompare.R @@ -0,0 +1,30 @@ +# test script for clonalCompare.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalCompare works", { + expect_doppelganger( + "clonalCompare_alluvial_plot", + clonalCompare( + combined, + top.clones = 10, + samples = c("P17B", "P17L"), + cloneCall="aa", + graph = "alluvial" + ) + ) + + expect_doppelganger( + "clonalCompare_area_plot", + clonalCompare( + combined, + top.clones = 10, + samples = c("P17B", "P17L"), + cloneCall="aa", + graph = "area") + ) +}) diff --git a/tests/testthat/test-clonalLength.R b/tests/testthat/test-clonalLength.R new file mode 100644 index 00000000..3cd1bbc3 --- /dev/null +++ b/tests/testthat/test-clonalLength.R @@ -0,0 +1,14 @@ +# test script for clonalLength.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalLength works", { + expect_doppelganger( + "clonalLength_both_chain_plot", clonalLength(combined, chain = "both") + ) +}) + diff --git a/tests/testthat/test-clonalQuant.R b/tests/testthat/test-clonalQuant.R new file mode 100644 index 00000000..b7c2c61e --- /dev/null +++ b/tests/testthat/test-clonalQuant.R @@ -0,0 +1,26 @@ +# test script for clonalQuant.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalQuant works", { + expect_doppelganger( + "clonalQuant_scaled_plot", clonalQuant(combined, scale = TRUE) + ) + expect_equal( + clonalQuant(combined, + scale = TRUE, + exportTable = TRUE), + data.frame( + "contigs" = c(745L, 2117L, 1254L, 1202L, 5544L, 1619L, 6087L, 192L), + "values" = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"), + "total" = c(2805L, 2893L, 1328L, 1278L, 6942L, 2747L, 8991L, 201L), + "scaled" = c( + 26.5597148, 73.1766333, 94.4277108, 94.0532081, 79.8617113, 58.9370222, 67.7010344, 95.5223881 + ) + ) + ) +}) diff --git a/tests/testthat/test-highlightClones.R b/tests/testthat/test-highlightClones.R new file mode 100644 index 00000000..4f2f9135 --- /dev/null +++ b/tests/testthat/test-highlightClones.R @@ -0,0 +1,18 @@ +# test script for vizGenes.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) + + +test_that("highlightClones works", { + test_obj <- highlightClones(test_obj, + cloneCall = "aa", + sequence = "CVVSDNTGGFKTIF_CASSVRRERANTGELFF") + expect_equal( + test_obj@meta.data[1:10, 7:14], + getdata("seuratFunctions", "highlightClones_meta") + ) +}) diff --git a/tests/testthat/test-percentAA.R b/tests/testthat/test-percentAA.R new file mode 100644 index 00000000..407cfccd --- /dev/null +++ b/tests/testthat/test-percentAA.R @@ -0,0 +1,24 @@ +# test script for percentAA.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("percentAA works", { + set.seed(42) + expect_doppelganger( + "percentAA_plot", + percentAA(combined, + chain = "TRB", + aa.length = 20) + ) + set.seed(42) + expect_equal( + percentAA(combined, + chain = "TRB", + aa.length = 20, + exportTable = TRUE), + getdata("visualizations", "percentAA_exportTable") + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-percentGenes.R b/tests/testthat/test-percentGenes.R new file mode 100644 index 00000000..069f6ef3 --- /dev/null +++ b/tests/testthat/test-percentGenes.R @@ -0,0 +1,24 @@ +# test script for percentGenes.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("percentGenes works", { + set.seed(42) + expect_doppelganger( + "percentGenes_plot", + percentGenes(combined, + chain = "TRB", + gene = "V") + ) + set.seed(42) + expect_equal( + percentGenes(combined, + chain = "TRB", + gene = "V", + exportTable = TRUE), + getdata("visualizations", "percentGenes_exportTable") + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-percentVJ.R b/tests/testthat/test-percentVJ.R new file mode 100644 index 00000000..a44ff2fe --- /dev/null +++ b/tests/testthat/test-percentVJ.R @@ -0,0 +1,22 @@ +# test script for percentVJ.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("percentVJ works", { + set.seed(42) + expect_doppelganger( + "percentVJ_plot", + percentVJ(combined, + chain = "TRB") + ) + set.seed(42) + expect_equal( + percentVJ(combined, + chain = "TRB", + exportTable = TRUE), + getdata("visualizations", "percentVJ_exportTable") + ) +}) \ No newline at end of file diff --git a/tests/testthat/test-viz.R b/tests/testthat/test-viz.R deleted file mode 100644 index 7963ddac..00000000 --- a/tests/testthat/test-viz.R +++ /dev/null @@ -1,108 +0,0 @@ -# test script for viz.R - testcases are NOT comprehensive! -# TODO all functions need to be tested for a single sample - however, many don't -# really work properly for single samples - -# testdata: (assumes combineTCR works) -combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) - -single_contig <- combineTCR(contig_list[[1]]) - -single_contig_with_sample <- combineTCR( - contig_list[[1]], samples = "P17B", -) -# TODO test more cases with single_contig - -test_that("clonalQuant works", { - expect_doppelganger( - "clonalQuant_scaled_plot", clonalQuant(combined, scale = TRUE) - ) - expect_equal( - clonalQuant(combined, - scale = TRUE, - exportTable = TRUE), - data.frame( - "contigs" = c(745L, 2117L, 1254L, 1202L, 5544L, 1619L, 6087L, 192L), - "values" = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"), - "total" = c(2805L, 2893L, 1328L, 1278L, 6942L, 2747L, 8991L, 201L), - "scaled" = c( - 26.5597148, 73.1766333, 94.4277108, 94.0532081, 79.8617113, 58.9370222, 67.7010344, 95.5223881 - ) - ) - ) -}) - -test_that("clonalAbundance works", { - expect_doppelganger( - "clonalAbundance_scaled_plot", clonalAbundance(combined, scale = FALSE) - ) -}) - -test_that("clonalLength works", { - expect_doppelganger( - "clonalLength_both_chain_plot", clonalLength(combined, chain = "both") - ) -}) - -test_that("clonalCompare works", { - expect_doppelganger( - "clonalCompare_alluvial_plot", - clonalCompare( - combined, - top.clones = 10, - samples = c("P17B", "P17L"), - cloneCall="aa", - graph = "alluvial" - ) - ) - - expect_doppelganger( - "clonalCompare_area_plot", - clonalCompare( - combined, - top.clones = 10, - samples = c("P17B", "P17L"), - cloneCall="aa", - graph = "area") - ) -}) - -# TODO clonalScatter - -# something in `clonesizeDistribution` prints "NULL" to the terminal -test_that("clonalSizeDistribution works", { - expect_doppelganger( - "clonalSizeDistribution_vignette_plot", - clonalSizeDistribution(combined, cloneCall = "gene+nt", method="ward.D2") - ) -}) - -# TODO makingLodes - - -test_that("vizGenes works", { - expect_doppelganger( - "vizGenes_bar_vignette_plot", - vizGenes( - combined, - x.axis = "TRBV", - y.axis = NULL, - plot = "barplot", - order = "variance", - scale = TRUE - ) - ) - - expect_doppelganger( - "vizGenes_heatmap_vignette_plot", - vizGenes( - combined[c(1,3,5)], - x.axis = "TRBV", - y.axis = "TRBJ", - plot = "heatmap", - scale = TRUE, - order = "gene" - ) - ) -}) diff --git a/tests/testthat/test-vizGenes.R b/tests/testthat/test-vizGenes.R new file mode 100644 index 00000000..364081a4 --- /dev/null +++ b/tests/testthat/test-vizGenes.R @@ -0,0 +1,33 @@ +# test script for vizGenes.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("vizGenes works", { + expect_doppelganger( + "vizGenes_bar_vignette_plot", + vizGenes( + combined, + x.axis = "TRBV", + y.axis = NULL, + plot = "barplot", + 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zi?w(mSsc1U1ZJe@MS&GadBt+dfO$j*MGzI!c-ddk5w{>V2Iy`mpQI{KI|A3?56 z)%sjsyGomE6Ilb!n48!D-BxFo)M{_;Y1?v~dc4cAat1cW)mJivv5G|tjGH<{UI2!g zI3@nM3{(4EnVbf!H;QU!fbyDYr?$iS`Znxdz@K5x` zUV!uKz7dcQ({;5nbcjOGw~@+`a-{6edwZ~;HWL7)!(rL vOz2xh|9hXR*Jkp!L+~R2>+a#Q00E5H0k!fDK*71GJ}?c70ssI200CKAn?b0D From 08d8238f1cd45b8f7bbf4c36f520cd1445acabad Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Wed, 1 Nov 2023 17:39:23 -0700 Subject: [PATCH 113/142] fixed name of scRep_example & minor refactoring --- tests/testthat/test-combineContigs.R | 2 +- tests/testthat/test-seurat_command.R | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R index b02a5b41..b41dfa77 100644 --- a/tests/testthat/test-combineContigs.R +++ b/tests/testthat/test-combineContigs.R @@ -8,7 +8,7 @@ test_that("combineTCR works", { samples = c("P17B", "P17L", "P18B") ) - expected1 <- readRDS("testdata/combineContigs/combineTCR_list_expected.rds") + expected1 <- getdata("combineContigs", "combineTCR_list_expected") expect_identical(trial1, expected1) diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R index a48946f1..5e23e815 100644 --- a/tests/testthat/test-seurat_command.R +++ b/tests/testthat/test-seurat_command.R @@ -1,7 +1,7 @@ test_that("make_screp_seurat_cmd works", { data("scRep_example", "mini_contig_list") - combined <- combineTCR(mini_contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", + combined <- combineTCR(mini_contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) test_obj <- combineExpression(combined, scRep_example) expect_true(!is.null(test_obj@commands[["combineExpression"]])) From 7c82612c6584113bc2ec0409295ef8f7c5b5ecd6 Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Wed, 1 Nov 2023 17:43:43 -0700 Subject: [PATCH 114/142] renamed screp_example + minor refactoring --- R/utils.R | 14 ++++++++------ data/{screp_example.rda => scRep_example.rda} | Bin 2 files changed, 8 insertions(+), 6 deletions(-) rename data/{screp_example.rda => scRep_example.rda} (100%) diff --git a/R/utils.R b/R/utils.R index b9d232f7..adf561d2 100644 --- a/R/utils.R +++ b/R/utils.R @@ -285,6 +285,8 @@ is_seurat_or_se_object <- function(obj) { #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding #' @keywords internal .parseTCR <- function(Con.df, unique_df, data2) { + tcr1_index <- 2 + tcr2_index <- 5 data2 <- data2 %>% arrange(., chain, cdr3_nt) for (y in seq_along(unique_df)){ barcode.i <- Con.df$barcode[y] @@ -292,9 +294,9 @@ is_seurat_or_se_object <- function(obj) { for (z in seq_along(location.i)) { where.chain <- data2[location.i[z],"chain"] - if (where.chain == "TRA" | where.chain == "TRG") { - if (is.na(Con.df[y,"TCR1"])) { - Con.df[y,tcr1_lines] <- data2[location.i[z],data1_lines] + if (where.chain == "TRA" || where.chain == "TRG") { + if (is.na(Con.df[y, tcr1_index])) { + Con.df[y,tcr1_lines] <- data2[location.i[z], data1_lines] } else { Con.df[y,tcr1_lines] <- paste( Con.df[y, tcr1_lines], @@ -302,9 +304,9 @@ is_seurat_or_se_object <- function(obj) { sep=";" ) } - } else if (where.chain == "TRB" | where.chain == "TRD") { - if (is.na(Con.df[y,"TCR2"])) { # 306 and 307 here are speed bottlenecks - Con.df[y,tcr2_lines] <- data2[location.i[z],data2_lines] + } else if (where.chain == "TRB" || where.chain == "TRD") { + if (is.na(Con.df[y, tcr2_index])) { + Con.df[y,tcr2_lines] <- data2[location.i[z], data2_lines] } else { Con.df[y,tcr2_lines] <- paste( Con.df[y, tcr2_lines], diff --git a/data/screp_example.rda b/data/scRep_example.rda similarity index 100% rename from data/screp_example.rda rename to data/scRep_example.rda From 9c365a448335145cb040b0e1e3c23406ca6dc4dc Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 2 Nov 2023 05:41:17 -0500 Subject: [PATCH 115/142] testthat push 2 --- R/alluvialClones.R | 2 +- R/clonalBias.R | 4 +- .../alluvialClones/alluvialclones-plot.svg | 821 ++++++++++++++++++ .../_snaps/clonalBias/clonalbias-plot.svg | 83 ++ .../clonalNetwork/clonalnetwork-plot.svg | 579 ++++++++++++ .../clonaloccupy-default-plot.svg | 119 +++ .../clonaloccupy-proportion-plot.svg | 92 ++ .../clonalOverlay/clonaloverlay-plot.svg | 717 +++++++++++++++ tests/testthat/test-alluvialClones.R | 29 + tests/testthat/test-clonalBias.R | 35 + tests/testthat/test-clonalNetwork.R | 42 + tests/testthat/test-clonalOccupy.R | 30 + tests/testthat/test-clonalOverlay.R | 23 + tests/testthat/test-highlightClones.R | 2 +- .../alluvialClones_exportTable.rds | Bin 0 -> 11282 bytes .../clonalBias_exportTable.rds | Bin 0 -> 921 bytes .../clonalNetwork_exportClones.rds | Bin 0 -> 333 bytes .../clonalNetwork_exportTable.rds | Bin 0 -> 391 bytes .../clonalOccupy_exportTable.rds | Bin 0 -> 396 bytes .../seuratFunctions/highlightClones_meta.rds | Bin 950 -> 960 bytes vignettes/articles/Installation.Rmd | 2 +- vignettes/articles/Processing.Rmd | 6 +- 22 files changed, 2578 insertions(+), 8 deletions(-) create mode 100644 tests/testthat/_snaps/alluvialClones/alluvialclones-plot.svg create mode 100644 tests/testthat/_snaps/clonalBias/clonalbias-plot.svg create mode 100644 tests/testthat/_snaps/clonalNetwork/clonalnetwork-plot.svg create mode 100644 tests/testthat/_snaps/clonalOccupy/clonaloccupy-default-plot.svg create mode 100644 tests/testthat/_snaps/clonalOccupy/clonaloccupy-proportion-plot.svg create mode 100644 tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg create mode 100644 tests/testthat/test-alluvialClones.R create mode 100644 tests/testthat/test-clonalBias.R create mode 100644 tests/testthat/test-clonalNetwork.R create mode 100644 tests/testthat/test-clonalOccupy.R create mode 100644 tests/testthat/test-clonalOverlay.R create mode 100644 tests/testthat/testdata/seuratFunctions/alluvialClones_exportTable.rds create mode 100644 tests/testthat/testdata/seuratFunctions/clonalBias_exportTable.rds create mode 100644 tests/testthat/testdata/seuratFunctions/clonalNetwork_exportClones.rds create mode 100644 tests/testthat/testdata/seuratFunctions/clonalNetwork_exportTable.rds create mode 100644 tests/testthat/testdata/seuratFunctions/clonalOccupy_exportTable.rds diff --git a/R/alluvialClones.R b/R/alluvialClones.R index 375d6079..d8edf218 100644 --- a/R/alluvialClones.R +++ b/R/alluvialClones.R @@ -148,7 +148,7 @@ alluvialClones <- function(sc.data, plot <- plot + geom_text(stat = ggalluvial::StatStratum, infer.label = FALSE, reverse = TRUE, size = 2) + - scale_fill_manual(values = .colorizer(palette, length(levels(lodes[,color])))) + + scale_fill_manual(values = .colorizer(palette, length(unique(lodes[,color])))) + scale_x_discrete(expand = c(0.025,0.025)) + theme_classic() + theme(axis.title.x = element_blank(), diff --git a/R/clonalBias.R b/R/clonalBias.R index 3cde5317..065ba79a 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -108,12 +108,12 @@ clonalBias <- function(sc.data, #Plotting plot <- ggplot(bias, aes(x=ncells,y=bias)) + geom_point(aes(fill=Top_state, size = cloneSize), shape = 21, stroke = 0.25) + - .quiet(stat_quantile(data=df_shuffle, + stat_quantile(data=df_shuffle, quantiles = c(corrected_p), method = "rqss", lambda=3, color = "black", - lty = 2)) + + lty = 2) + scale_fill_manual(values = .colorizer(palette, length(unique(bias[,"Top_state"])))) + theme_classic() + xlab("Clone Size") + diff --git a/tests/testthat/_snaps/alluvialClones/alluvialclones-plot.svg b/tests/testthat/_snaps/alluvialClones/alluvialclones-plot.svg new file mode 100644 index 00000000..ff3a4863 --- /dev/null +++ b/tests/testthat/_snaps/alluvialClones/alluvialclones-plot.svg @@ -0,0 +1,821 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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a/tests/testthat/_snaps/clonalNetwork/clonalnetwork-plot.svg b/tests/testthat/_snaps/clonalNetwork/clonalnetwork-plot.svg new file mode 100644 index 00000000..1961c41f --- /dev/null +++ b/tests/testthat/_snaps/clonalNetwork/clonalnetwork-plot.svg @@ -0,0 +1,579 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ +0 +20 +40 +60 + + + + + + + + + +0 +5 +10 +15 +Single Cells + +cloneSize + + + +Hyperexpanded (0.1 < X <= 1) +Large (0.01 < X <= 0.1) +Medium (0.001 < X <= 0.01) +clonalOccupy_default_plot + + diff --git a/tests/testthat/_snaps/clonalOccupy/clonaloccupy-proportion-plot.svg b/tests/testthat/_snaps/clonalOccupy/clonaloccupy-proportion-plot.svg new file mode 100644 index 00000000..22f75144 --- /dev/null +++ b/tests/testthat/_snaps/clonalOccupy/clonaloccupy-proportion-plot.svg @@ -0,0 +1,92 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.00 +0.25 +0.50 +0.75 +1.00 + + + + + + + + + + +0 +5 +10 +15 +Proportion of Cells + +cloneSize + + + +Hyperexpanded (0.1 < X <= 1) +Large (0.01 < X <= 0.1) +Medium (0.001 < X <= 0.01) +clonalOccupy_proportion_plot + + diff --git a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg new file mode 100644 index 00000000..f7cb4e13 --- /dev/null +++ b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg @@ -0,0 +1,717 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) +test_obj$Patient <- substr(test_obj$orig.ident,1,3) +test_obj$Type <- substr(test_obj$orig.ident,4,4) + + +test_that("alluvialClones works", { + + expect_doppelganger( + "alluvialClones_plot", + alluvialClones(test_obj, + cloneCall = "aa", + y.axes = c("Patient", "ident"), + color = "Type") + ) + + expect_equal( + alluvialClones(test_obj, + cloneCall = "aa", + y.axes = c("Patient", "ident", "Type"), + exportTable = TRUE), + getdata("seuratFunctions", "alluvialClones_exportTable") + ) +}) diff --git a/tests/testthat/test-clonalBias.R b/tests/testthat/test-clonalBias.R new file mode 100644 index 00000000..0834dfae --- /dev/null +++ b/tests/testthat/test-clonalBias.R @@ -0,0 +1,35 @@ +# test script for clonalBias.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) +test_obj$Patient <- substr(test_obj$orig.ident,1,3) +test_obj$Type <- substr(test_obj$orig.ident,4,4) + + +test_that("clonalBias works", { + + set.seed(42) + expect_doppelganger( + "clonalBias_plot", + clonalBias(test_obj, + cloneCall = "aa", + split.by = "Patient", + group.by = "seurat_clusters", + n.boots = 1, + min.expand = 2) + ) + set.seed(42) + expect_equal( + clonalBias(test_obj, + cloneCall = "aa", + split.by = "Patient", + group.by = "seurat_clusters", + n.boots = 1, + min.expand = 2, + exportTable = TRUE), + getdata("seuratFunctions", "clonalBias_exportTable") + ) +}) diff --git a/tests/testthat/test-clonalNetwork.R b/tests/testthat/test-clonalNetwork.R new file mode 100644 index 00000000..dc041ae5 --- /dev/null +++ b/tests/testthat/test-clonalNetwork.R @@ -0,0 +1,42 @@ +# test script for clonalNetwork.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +library(ggraph) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) + + + +test_that("clonalNetwork works", { + + set.seed(42) + expect_doppelganger( + "clonalNetwork_plot", + clonalNetwork(test_obj, + reduction = "umap", + group.by = "seurat_clusters", + filter.identity = 3, + cloneCall = "aa") + ) + expect_equal( + clonalNetwork(test_obj, + reduction = "umap", + group.by = "seurat_clusters", + cloneCall = "aa", + exportTable = TRUE), + getdata("seuratFunctions", "clonalNetwork_exportTable") + ) + + expect_equal( + clonalNetwork(test_obj, + reduction = "umap", + group.by = "seurat_clusters", + cloneCall = "aa", + exportClones = TRUE), + getdata("seuratFunctions", "clonalNetwork_exportClones") + ) + + +}) diff --git a/tests/testthat/test-clonalOccupy.R b/tests/testthat/test-clonalOccupy.R new file mode 100644 index 00000000..77117457 --- /dev/null +++ b/tests/testthat/test-clonalOccupy.R @@ -0,0 +1,30 @@ +# test script for clonalOccupy.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) + + +test_that("clonalOccupy works", { + + expect_doppelganger( + "clonalOccupy_default_plot", + clonalOccupy(test_obj, + x.axis = "seurat_clusters") + ) + expect_doppelganger( + "clonalOccupy_proportion_plot", + clonalOccupy(test_obj, + x.axis = "ident", + proportion = TRUE, + label = FALSE) + ) + expect_equal( + clonalOccupy(test_obj, + x.axis = "seurat_clusters", + exportTable = TRUE), + getdata("seuratFunctions", "clonalOccupy_exportTable") + ) +}) diff --git a/tests/testthat/test-clonalOverlay.R b/tests/testthat/test-clonalOverlay.R new file mode 100644 index 00000000..f60295c3 --- /dev/null +++ b/tests/testthat/test-clonalOverlay.R @@ -0,0 +1,23 @@ +# test script for clonalOverlay.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) +test_obj$Patient <- substr(test_obj$orig.ident, 1,3) + + +test_that("clonalOverlay works", { + + set.seed(42) + expect_doppelganger( + "clonalOverlay_plot", + clonalOverlay(test_obj, + reduction = "umap", + freq.cutpoint = 1, + bins = 10, + facet.by = "Patient") + ) + +}) diff --git a/tests/testthat/test-highlightClones.R b/tests/testthat/test-highlightClones.R index 4f2f9135..253b56a7 100644 --- a/tests/testthat/test-highlightClones.R +++ b/tests/testthat/test-highlightClones.R @@ -1,4 +1,4 @@ -# test script for vizGenes.R - testcases are NOT comprehensive! +# test script for highlightClones.R - testcases are NOT comprehensive! # testdata: (assumes 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a/vignettes/articles/Installation.Rmd +++ b/vignettes/articles/Installation.Rmd @@ -58,7 +58,7 @@ A full copy of the changes in each version can be found in the [NEWS/ChangeLog]( scRepertoire is an active project, that receives a number of changes based on user feedback, the most up-to-date versions are available via the GitHub repositories. These versions can be installed using ```devtools::install_github()``` or ```remotes::install_github()```. The **dev** version is the active development branch and might be less stable than the main branch or bioconductor versions. ``` -devtools::install_github(repo = "ncborcherding/scRepertoire") +devtools::install_github(repo = "ncborcherding/scRepertoire", ref = "v2) remotes::install_github(repo = "ncborcherding/scRepertoire", ref = "dev") ``` \ No newline at end of file diff --git a/vignettes/articles/Processing.Rmd b/vignettes/articles/Processing.Rmd index 07d8c625..57a81297 100644 --- a/vignettes/articles/Processing.Rmd +++ b/vignettes/articles/Processing.Rmd @@ -80,9 +80,9 @@ head(subset2[[1]]) After assigning the clone by barcode, we can export the paired clonotypes using `exportClones()` to save for later use or to use in other pipelines. -**write.file** -* TRUE, save the file. -* FALSE, return a data.frame. +**write.file** +* TRUE, save the file. +* FALSE, return a data.frame. **dir** directory location to save the csv From 8f9c6c9573a87fe5dda916b610c12e85f1bba246 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 4 Nov 2023 06:13:19 -0500 Subject: [PATCH 116/142] harmonizing testthat, qile, and my progess --- NEWS | 1 + NEWS.md | 14 +- R/clonalDiversity.R | 1 - R/clonalOverlap.R | 2 +- R/startracDiversity.R | 3 +- R/utils.R | 8 - inst/pkgdown.yml | 2 +- .../startracdiversity-plot.svg | 344 ++++++++++++++++++ .../clonalclonalrarefaction-h0-p1-plot.svg | 72 ++++ .../clonalclonalrarefaction-h1-p2-plot.svg | 72 ++++ .../clonalclonalrarefaction-h2-p3-plot.svg | 72 ++++ .../clonalsizedistribution-plot.svg | 94 +++++ .../_snaps/viz/clonalcompare-area-plot.svg | 91 ----- tests/testthat/test-StartractDiversity.R | 29 ++ tests/testthat/test-clonalRarefaction.R | 56 +++ tests/testthat/test-clonalSizeDistribution.R | 23 ++ tests/testthat/test-combineContigs.R | 22 +- tests/testthat/test-exportClones.R | 33 ++ tests/testthat/test-loadContigs.R | 26 ++ tests/testthat/test-utils.R | 36 +- .../clustering/clonalCluster_1sample.rds | Bin 0 -> 236866 bytes .../clustering/clonalCluster_Seurat_meta.rds | Bin 0 -> 39491 bytes .../combineTCR_list_filterMulti.rds | Bin 0 -> 1360 bytes .../combineTCR_list_removeNA.rds | Bin 0 -> 1360 bytes .../testdata/export/exportClones_TCRMatch.rds | Bin 0 -> 264348 bytes .../testdata/export/exportClones_airr.rds | Bin 0 -> 1038099 bytes .../testdata/export/exportClones_paired.rds | Bin 0 -> 962948 bytes .../testthat/testdata/load/loadContigs_BD.rds | Bin 0 -> 66282 bytes .../testdata/load/loadContigs_TRUST4.rds | Bin 0 -> 43526 bytes .../testdata/load/loadContigs_WAT3R.rds | Bin 0 -> 201552 bytes .../StartracDiversity_exportTable.rds | Bin 0 -> 738 bytes .../utils/filteringMulti_expected.rds | Bin 897 -> 0 bytes .../clonalRarefaction_h0_p1_exportTable.rds | Bin 0 -> 1293 bytes .../clonalRarefaction_h1_p2_exportTable.rds | Bin 0 -> 1316 bytes .../clonalRarefaction_h2_p3_exportTable.rds | Bin 0 -> 1697 bytes .../clonalSizeDistribution_exportTable.rds | Bin 0 -> 428 bytes vignettes/articles/Attaching_SC.Rmd | 8 +- vignettes/articles/Clonal_Bias.Rmd | 4 +- vignettes/articles/Clonal_Cluster.Rmd | 6 +- vignettes/articles/Clonal_Diversity.Rmd | 10 +- vignettes/articles/Clonal_Dynamics.Rmd | 4 +- vignettes/articles/Clonal_Visualizations.Rmd | 12 +- vignettes/articles/Combining_Contigs.Rmd | 4 +- vignettes/articles/FAQ.Rmd | 4 +- vignettes/articles/Ibex.Rmd | 16 +- vignettes/articles/Loading.Rmd | 6 +- vignettes/articles/Processing.Rmd | 6 +- vignettes/articles/Repertoire_Summary.Rmd | 16 +- vignettes/articles/SC_Visualizations.Rmd | 16 +- vignettes/articles/Trex.Rmd | 18 +- 50 files changed, 937 insertions(+), 194 deletions(-) create mode 100644 tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg create mode 100644 tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg create mode 100644 tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg create mode 100644 tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg create mode 100644 tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg delete mode 100644 tests/testthat/_snaps/viz/clonalcompare-area-plot.svg create mode 100644 tests/testthat/test-StartractDiversity.R create mode 100644 tests/testthat/test-clonalRarefaction.R create mode 100644 tests/testthat/test-clonalSizeDistribution.R create mode 100644 tests/testthat/test-exportClones.R create mode 100644 tests/testthat/test-loadContigs.R create mode 100644 tests/testthat/testdata/clustering/clonalCluster_1sample.rds create mode 100644 tests/testthat/testdata/clustering/clonalCluster_Seurat_meta.rds create mode 100644 tests/testthat/testdata/combineContigs/combineTCR_list_filterMulti.rds create mode 100644 tests/testthat/testdata/combineContigs/combineTCR_list_removeNA.rds create mode 100644 tests/testthat/testdata/export/exportClones_TCRMatch.rds create mode 100644 tests/testthat/testdata/export/exportClones_airr.rds create mode 100644 tests/testthat/testdata/export/exportClones_paired.rds create mode 100644 tests/testthat/testdata/load/loadContigs_BD.rds create mode 100644 tests/testthat/testdata/load/loadContigs_TRUST4.rds create mode 100644 tests/testthat/testdata/load/loadContigs_WAT3R.rds create mode 100644 tests/testthat/testdata/seuratFunctions/StartracDiversity_exportTable.rds delete mode 100644 tests/testthat/testdata/utils/filteringMulti_expected.rds create mode 100644 tests/testthat/testdata/visualizations/clonalRarefaction_h0_p1_exportTable.rds create mode 100644 tests/testthat/testdata/visualizations/clonalRarefaction_h1_p2_exportTable.rds create mode 100644 tests/testthat/testdata/visualizations/clonalRarefaction_h2_p3_exportTable.rds create mode 100644 tests/testthat/testdata/visualizations/clonalSizeDistribution_exportTable.rds diff --git a/NEWS b/NEWS index 15f40387..389c4aca 100644 --- a/NEWS +++ b/NEWS @@ -31,6 +31,7 @@ UNDERLYING CHANGES * group.by and split.by have been consolidated into single group.by parameter * Added support for .json, Omniscope, and MiXCR formats * Made GitHub.io website for support/vignettes/FAQ +* Added testhat for all exported and internal functions DEPRECATED AND DEFUNCT diff --git a/NEWS.md b/NEWS.md index 53509ae1..206ddf96 100644 --- a/NEWS.md +++ b/NEWS.md @@ -1,5 +1,5 @@ -# scRepertoire VERSION 2.0.0 (2023-10-23) +# scRepertoire VERSION 2.0.0 (2023-11-4) ## NEW FEATURES @@ -21,17 +21,19 @@ ## UNDERLYING CHANGES -* .theCall now allows for a custom header/variable and checks the colnames. +* Removed internal **.quiet()** function. +* **.theCall()** now allows for a custom header/variable and checks the colnames. * Replaced data arguments to be more descriptive: *df* is now *input.data*, *dir* is now *input*, and *sc* is now *sc.data* * Deep clean on the documentation for each function for increased consistency and explainability -* startrac diversity metric re-implemented to remove startrac-class object intermediary -* implemented powerTCR locally to reduce dependencies and continue support +* ```Startracdiversity()``` metric re-implemented to remove startrac-class object intermediary +* Implemented powerTCR locally to reduce dependencies and continue support * Universalized underlying function language and intermediate variables * License change to MIT -* group.by and split.by have been consolidated into single group.by parameter +* **group.by** and **split.by** have been consolidated into single **group.by** parameter * Added support for .json, Omniscope, and MiXCR formats for ```loadContigs()``` * Made GitHub.io website for support/vignettes/FAQ * Restructured NEWS Tracking +* Added testhat for all exported and internal functions ## DEPRECATED AND DEFUNCT @@ -225,7 +227,7 @@ # scRepertoire VERSION 0.99.17 * Updated NEWS formatting -* Edited DESCRIPTION to SingleCellExperiment R package +* Edited DESCRIPTION to Single Cell Experiment R package * Updated information in the vignette diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 39c1d80b..1ac05f21 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -149,7 +149,6 @@ clonalDiversity <- function(input.data, mat_melt <- suppressMessages(melt(mat, id.vars = c(group.by, x.axis))) values <- str_sort(as.character(unique(mat_melt[,group.by])), numeric = TRUE) - values <- .quiet(dput(values)) mat_melt[,group.by] <- factor(mat_melt[,group.by], levels = values) if (x.axis == "x.axis") { plot <- ggplot(mat_melt, aes(x=1, y=as.numeric(value))) diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 5f723e8d..e3b218e5 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -74,7 +74,7 @@ clonalOverlap <- function(input.data, input.data <- input.data[order(names(input.data))] values <- str_sort(as.character(unique(names(input.data))), numeric = TRUE) - input.data <- input.data[.quiet(dput(values))] + input.data <- input.data[values] num_samples <- length(input.data[]) names_samples <- names(input.data) length <- seq_len(num_samples) diff --git a/R/startracDiversity.R b/R/startracDiversity.R index 9dbcfea5..c1beb74c 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -100,8 +100,7 @@ StartracDiversity <- function(sc.data, } mat_melt <- melt(mat, id = c("group", "majorCluster")) - values <- as.character(unique(mat_melt$majorCluster)) - values <- .quiet(dput(values)) + values <- str_sort(unique(mat_melt$majorCluster), numeric = TRUE) mat_melt$majorCluster <- factor(mat_melt$majorCluster, levels = values) mat_melt$value <- as.numeric(mat_melt$value) col <- length(unique(mat_melt[,"majorCluster"])) diff --git a/R/utils.R b/R/utils.R index adf561d2..3c274bab 100644 --- a/R/utils.R +++ b/R/utils.R @@ -246,14 +246,6 @@ is_seurat_or_se_object <- function(obj) { return(data1) } -# This suppressing outputs for using dput() -#' @keywords internal -.quiet <- function(x) { - sink(tempfile()) - on.exit(sink()) - invisible(force(x)) -} - # This is to help sort the type of clonotype data to use #' @keywords internal diff --git a/inst/pkgdown.yml b/inst/pkgdown.yml index e0a40fe1..c44519b9 100644 --- a/inst/pkgdown.yml +++ b/inst/pkgdown.yml @@ -19,7 +19,7 @@ articles: Trex: Trex.html Trex_Trajectory: Trex_Trajectory.html vignette: vignette.html -last_built: 2023-11-01T16:44Z +last_built: 2023-11-04T11:12Z urls: reference: https://www.borch.dev/uploads/scRepertoire/reference article: https://www.borch.dev/uploads/scRepertoire/articles diff --git a/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg b/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg new file mode 100644 index 00000000..af181bdd --- /dev/null +++ b/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg @@ -0,0 +1,344 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + 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+ + +0 +1000 +2000 +3000 + + + + + + + + + +0 +2000 +4000 +6000 +Number of individuals +Species diversity + + + +Rarefaction +Extrapolation + + + + + +P17B +P17L +clonalclonalRarefaction_h0_p1_plot + + diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg new file mode 100644 index 00000000..8fc4bd97 --- /dev/null +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg @@ -0,0 +1,72 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.00 +0.05 +0.10 +0.15 +0.20 + + + + + + + + + +0 +1000 +2000 +Number of individuals +Sample coverage + + + +Rarefaction +Extrapolation + + + + + +P18B +P18L +clonalclonalRarefaction_h1_p2_plot + + diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg new file mode 100644 index 00000000..2e6f838b --- /dev/null +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg @@ -0,0 +1,72 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0 +500 +1000 +1500 + + + + + + + + + +0.0 +0.2 +0.4 +0.6 +Sample coverage +Species diversity + + + +Rarefaction +Extrapolation + + + + + +P19B +P19L +clonalclonalRarefaction_h2_p3_plot + + diff --git a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg new file mode 100644 index 00000000..cb2025f6 --- /dev/null +++ b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg @@ -0,0 +1,94 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +P19B +P20B +P20L +P17L +P19L +P17B +P18B +P18L + + + + + + + + + + + + + + + + + + + +-0.1 +0.0 +0.1 +0.2 +0.3 +0.4 +0.5 +clonalSizeDistribution_plot + + diff --git a/tests/testthat/_snaps/viz/clonalcompare-area-plot.svg b/tests/testthat/_snaps/viz/clonalcompare-area-plot.svg deleted file mode 100644 index b905f239..00000000 --- a/tests/testthat/_snaps/viz/clonalcompare-area-plot.svg +++ /dev/null @@ -1,91 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.0 -0.1 -0.2 -0.3 -0.4 -0.5 - - - - - - - - - -P17B -P17L -Proportion - -clones - - - - - - - - - -CAASETSYDKVIF_CASSSGEKGYTF -CAVGAQQGGKLIF_CASSLSLSDGRHGYTF -CAVQAGDSWGKLQF_CASSYGGTEAFF -CAVRKTTGTASKLTF_CASSLFGDKGETQYF -CAVTFHYNTDKLIF_CASSQDRTGLDYEQYF -CVVSDNTGGFKTIF_CASSVRRERANTGELFF -NA_CAAGQGVMNTEAFF -NA_CASSQDRTGLDYEQYF -NA_CASSVRRERANTGELFF -clonalCompare_area_plot - - diff --git a/tests/testthat/test-StartractDiversity.R b/tests/testthat/test-StartractDiversity.R new file mode 100644 index 00000000..2c753ae2 --- /dev/null +++ b/tests/testthat/test-StartractDiversity.R @@ -0,0 +1,29 @@ +# test script for StartracDiversity.R - testcases are NOT comprehensive! + +# testdata: (assumes combineTCR works) +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +test_obj <- combineExpression(combined, scRep_example) +test_obj$Patient <- substr(test_obj$orig.ident,1,3) +test_obj$Type <- substr(test_obj$orig.ident,4,4) + + +test_that("StartracDiversity works", { + + #Ridiculous ggplot warning system can't suppress any internal warnings about NAs + expect_doppelganger( + "StartracDiversity_plot", + suppressWarnings(print(StartracDiversity(test_obj, + type = "Type", + group.by = "Patient"))) + ) + + expect_equal( + StartracDiversity(test_obj, + type = "Type", + group.by = "Patient", + exportTable = TRUE), + getdata("seuratFunctions", "StartracDiversity_exportTable") + ) +}) diff --git a/tests/testthat/test-clonalRarefaction.R b/tests/testthat/test-clonalRarefaction.R new file mode 100644 index 00000000..5e6a8e85 --- /dev/null +++ b/tests/testthat/test-clonalRarefaction.R @@ -0,0 +1,56 @@ +# test script for clonalRarefaction.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalRarefaction works", { + expect_doppelganger( + "clonalclonalRarefaction_h0_p1_plot", + clonalRarefaction(combined[1:2], + plot.type = 1, + hill.numbers = 0, + n.boots = 1) + ) + + trial1 <- clonalRarefaction(combined[1:2], + plot.type = 1, + hill.numbers = 0, + n.boots = 1, + exportTable = TRUE) + expect_identical(trial1, getdata("visualizations", "clonalRarefaction_h0_p1_exportTable")) + + expect_doppelganger( + "clonalclonalRarefaction_h1_p2_plot", + clonalRarefaction(combined[3:4], + plot.type = 2, + hill.numbers = 1, + n.boots = 1) + ) + + trial2 <- clonalRarefaction(combined[3:4], + plot.type = 2, + hill.numbers = 1, + n.boots = 1, + exportTable = TRUE) + expect_identical(trial2, getdata("visualizations", "clonalRarefaction_h1_p2_exportTable")) + + expect_doppelganger( + "clonalclonalRarefaction_h2_p3_plot", + clonalRarefaction(combined[5:6], + plot.type = 3, + hill.numbers = 2, + n.boots = 1) + ) + + + trial3 <- clonalRarefaction(combined[5:6], + plot.type = 3, + hill.numbers = 2, + n.boots = 1, + exportTable = TRUE) + expect_identical(trial3, getdata("visualizations", "clonalRarefaction_h2_p3_exportTable")) + + +}) \ No newline at end of file diff --git a/tests/testthat/test-clonalSizeDistribution.R b/tests/testthat/test-clonalSizeDistribution.R new file mode 100644 index 00000000..b0ae42df --- /dev/null +++ b/tests/testthat/test-clonalSizeDistribution.R @@ -0,0 +1,23 @@ +# test script for clonalSizeDistribution.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("clonalSizeDistribution works", { + expect_doppelganger( + "clonalSizeDistribution_plot", + clonalSizeDistribution(combined, + cloneCall = "aa", + method = "ward.D2") + ) + expect_equal( + clonalSizeDistribution(combined, + cloneCall = "aa", + method = "ward.D2", + exportTable = TRUE), + getdata("visualizations", "clonalSizeDistribution_exportTable") + ) + +}) \ No newline at end of file diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R index b41dfa77..6a7b7b11 100644 --- a/tests/testthat/test-combineContigs.R +++ b/tests/testthat/test-combineContigs.R @@ -8,7 +8,7 @@ test_that("combineTCR works", { samples = c("P17B", "P17L", "P18B") ) - expected1 <- getdata("combineContigs", "combineTCR_list_expected") + expected1 <- readRDS("testdata/combineContigs/combineTCR_list_expected.rds") expect_identical(trial1, expected1) @@ -18,8 +18,26 @@ test_that("combineTCR works", { expected2 <- expected1[[1]] expect_identical(trial2, expected2) + + trial3 <- combineTCR( + input.data = lapply(contig_list[1:3], head), + samples = c("P17B", "P17L", "P18B"), + filterMulti = TRUE + ) + expected3 <- readRDS("testdata/combineContigs/combineTCR_list_filterMulti.rds") + + expect_identical(trial3, expected3) + + trial4 <- combineTCR( + input.data = lapply(contig_list[1:3], head), + samples = c("P17B", "P17L", "P18B"), + removeNA = TRUE + ) + expected4 <- readRDS("testdata/combineContigs/combineTCR_list_removeNA.rds") + + expect_identical(trial4, expected4) + }) # TODO combineTCR (need more edge cases, different args, errors, etc.) # TODO combineBCR -# TODO lvCompare diff --git a/tests/testthat/test-exportClones.R b/tests/testthat/test-exportClones.R new file mode 100644 index 00000000..3a1d8df0 --- /dev/null +++ b/tests/testthat/test-exportClones.R @@ -0,0 +1,33 @@ +# test script for eportClones.R - testcases are NOT comprehensive! + +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + + +test_that("exportClones works", { + trial1 <- exportClones(combined, + write.file = FALSE, + format = "paired") + expect_equal( + trial1, + getdata("export", "exportClones_paired") + ) + + trial2 <- exportClones(combined, + write.file = FALSE, + format = "airr") + expect_equal( + trial2, + getdata("export", "exportClones_airr") + ) + + trial3 <- exportClones(combined, + write.file = FALSE, + format = "TCRMatch") + expect_equal( + trial3, + getdata("export", "exportClones_TCRMatch") + ) + +}) \ No newline at end of file diff --git a/tests/testthat/test-loadContigs.R b/tests/testthat/test-loadContigs.R new file mode 100644 index 00000000..c9120c20 --- /dev/null +++ b/tests/testthat/test-loadContigs.R @@ -0,0 +1,26 @@ +# test script for combineContigs.R - testcases are NOT comprehensive! + +test_that("combineTCR works", { + TRUST4 <- read.csv("https://www.borch.dev/uploads/contigs/TRUST4_contigs.csv") + trial1 <- loadContigs(TRUST4, format = "TRUST4") + expect_identical(trial1, + getdata("load", "loadContigs_TRUST4") + ) + + + BD <- read.csv("https://www.borch.dev/uploads/contigs/BD_contigs.csv") + trial2 <- loadContigs(BD, format = "BD") + expect_identical(trial2, + getdata("load", "loadContigs_BD") + ) + + WAT3R <- read.csv("https://www.borch.dev/uploads/contigs/WAT3R_contigs.csv") + trial3 <- loadContigs(WAT3R, format = "WAT3R") + expect_identical(trial3, + getdata("load", "loadContigs_WAT3R") + ) + +}) + +# TODO Test more formats +# TODO Would be nice to have a dir option diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index 60be0b5c..5c162338 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -73,25 +73,26 @@ test_that("Modifying barcodes with ID works correctly", { expect_identical(modified_data, expected_modified_data) }) -# TODO removingNA -# TODO removingMulti -test_that("filteringMulti works", { - expect_identical( - .filteringMulti(head(contig_list[[1]])), - getdata("utils", "filteringMulti_expected") - ) -}) - -# TODO filteringNA -# TODO diversityCall # TODO parseContigs -# TODO morisitaIndex -# TODO jaccardIndex -# TODO rawIndex -# TODO overlapIndex # TODO quiet -# TODO .theCall + +.quiet <- function(x) { + sink(tempfile()) + on.exit(sink()) + invisible(force(x)) +} + + + +test_that(".theCall works", { + expect_equal(.theCall(NULL, "aa", check.df = FALSE), "CTaa") + expect_equal(.theCall(NULL, "nt", check.df = FALSE), "CTnt") + expect_equal(.theCall(NULL, "genes", check.df = FALSE), "CTgene") + expect_equal(.theCall(NULL, "strict", check.df = FALSE), "CTstrict") +}) +#TODO .theCall Add custom header + test_that(".parseTCR works", { Con.df <- getdata("utils", "Con.df") @@ -114,9 +115,10 @@ test_that("makeGenes works for cellType T", { getdata("utils", "makeGenes_T_expected") ) }) - # TODO makesGenes (cellType B) + # TODO short.check + # TODO select.gene test_that("Check if object is a dataframe or list of dataframes", { diff --git a/tests/testthat/testdata/clustering/clonalCluster_1sample.rds b/tests/testthat/testdata/clustering/clonalCluster_1sample.rds new file mode 100644 index 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index 7e5608ce..a7b5f192 100644 --- a/vignettes/articles/Attaching_SC.Rmd +++ b/vignettes/articles/Attaching_SC.Rmd @@ -54,7 +54,7 @@ The data in the scRepertoire package is derived from a [study](https://pubmed.nc ## Quick Pre-processing of the Single-Cell Object -```{r} +```{r tidy = FALSE} scRep_example <- readRDS("scRep_example_full.rds") #Making a Single-Cell Experiment object @@ -74,7 +74,7 @@ Part of `combineExpression()` is calculating the clonal frequency and proportion We can check the default cloneSize groupings using the Single-Cell Experiment object we just created above with using **group.by** set to the *sample* variable used in ```combineTCR()``` : -```{r} +```{r tidy = FALSE} sce <- combineExpression(combined.TCR, sce, cloneCall="gene", @@ -90,7 +90,7 @@ plotUMAP(sce, colour_by = "cloneSize") + Alternatively, if we want **cloneSize** to be based on the frequency of the clone, we can set **proportion** = FALSE and we will need to change the **cloneSize** bins to integers. If we have not inspected our clone data, setting the upper limit of the clonal frequency might be difficult - ```combineExpression()``` will automatically adjust the upper limit to fit the distribution of the frequencies. To demonstrate this, check out the Seurat object below: -```{r} +```{r tidy = FALSE} scRep_example <- combineExpression(combined.TCR, scRep_example, cloneCall="gene", @@ -108,7 +108,7 @@ If we have TCR/BCR enrichment or want to add info for gamma-delta and alpha-beta **Major note** if there are duplicate barcodes (if a cell has both Ig and TCR), the immune receptor information will not be added. It might be worth checking cluster identities and removing incongruent barcodes in the products of ```combineTCR()``` and ```combineBCR()```. As an anecdote, the [testing data](https://support.10xgenomics.com/single-cell-vdj/datasets/6.0.1/SC5v2_Melanoma_5Kcells_Connect_single_channel_SC5v2_Melanoma_5Kcells_Connect_single_channel) we used to improve this function had 5-6% of barcode overlap. -```{r, eval=FALSE} +```{r, eval=FALSE, tidy = FALSE} #This is an example of the process, which will not be evaluated during knit TCR <- combineTCR(...) BCR <- combineBCR(...) diff --git a/vignettes/articles/Clonal_Bias.Rmd b/vignettes/articles/Clonal_Bias.Rmd index 85f908c8..44973000 100644 --- a/vignettes/articles/Clonal_Bias.Rmd +++ b/vignettes/articles/Clonal_Bias.Rmd @@ -74,7 +74,7 @@ In order to use the ```StartracDiversity()``` function, you will need to include + migr - Cross-tissue Migration + tran - State Transition -```{r} +```{r tidy = FALSE} StartracDiversity(scRep_example, type = "Type", group.by = "Patient") @@ -90,7 +90,7 @@ A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/3582969 **group.by** * The compartment/variable used for the purpose of the comparison -```{r, message = FALSE} +```{r, message = FALSE, tidy = FALSE} clonalBias(scRep_example, cloneCall = "aa", split.by = "Patient", diff --git a/vignettes/articles/Clonal_Cluster.Rmd b/vignettes/articles/Clonal_Cluster.Rmd index d65b7eb4..302e22da 100644 --- a/vignettes/articles/Clonal_Cluster.Rmd +++ b/vignettes/articles/Clonal_Cluster.Rmd @@ -66,7 +66,7 @@ Edit-distance based clusters will have the following format: ### Basic use: -```{r} +```{r tidy = FALSE} sub_combined <- clonalCluster(combined.TCR[[2]], chain = "TRA", sequence = "aa", @@ -80,7 +80,7 @@ If performed over the number of samples, such as the list elements, **group.by** ### Clustering with attached data: -```{r} +```{r tidy = FALSE} #Adding patient information scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) @@ -105,7 +105,7 @@ Using ```clonalCluster()```, we can also return an igraph object of all the rela ### Returning an igraph object: -```{r} +```{r tidy = FALSE} #Clustering Patient 19 samples igraph.object <- clonalCluster(combined.TCR[c(5,6)], chain = "TRB", diff --git a/vignettes/articles/Clonal_Diversity.Rmd b/vignettes/articles/Clonal_Diversity.Rmd index 4090c629..4c142428 100644 --- a/vignettes/articles/Clonal_Diversity.Rmd +++ b/vignettes/articles/Clonal_Diversity.Rmd @@ -62,7 +62,7 @@ The number of calculations to perform **(default = 100)**. Skip the bootstrapping calculations. -```{r} +```{r tidy = FALSE} clonalDiversity(combined.TCR, cloneCall = "gene", n.boots = 20) @@ -91,7 +91,7 @@ This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.h ### Rarefaction using Shannon Diversity (q = 0) -```{r, message=FALSE} +```{r, message=FALSE, tidy = FALSE} clonalRarefaction(combined.TCR, plot.type = 1, hill.numbers = 0, @@ -110,7 +110,7 @@ clonalRarefaction(combined.TCR, ### Rarefaction using Shannon Diversity (q = 1) -```{r} +```{r tidy = FALSE} clonalRarefaction(combined.TCR, plot.type = 1, hill.numbers = 1, @@ -133,7 +133,7 @@ Another method for modeling the repertoire distribution is a discrete gamma-GPD If using this function, please read/cite [Koch et al.](https://pubmed.ncbi.nlm.nih.gov/30485278/) and check out the [powerTCR](https://bioconductor.org/packages/release/bioc/html/powerTCR.html) R package. -```{r} +```{r tidy = FALSE} clonalSizeDistribution(combined.TCR, cloneCall = "aa", method= "ward.D2") @@ -143,7 +143,7 @@ clonalSizeDistribution(combined.TCR, If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. -```{r} +```{r tidy = FALSE} clonalOverlap(combined.TCR, cloneCall = "strict", method = "morisita") diff --git a/vignettes/articles/Clonal_Dynamics.Rmd b/vignettes/articles/Clonal_Dynamics.Rmd index af5a2e6f..2dc6140c 100644 --- a/vignettes/articles/Clonal_Dynamics.Rmd +++ b/vignettes/articles/Clonal_Dynamics.Rmd @@ -59,7 +59,7 @@ By examining the clonal space, we effectively look at the relative space occupie * Large = 0.1 * Hyperexpanded = 1 -```{r} +```{r tidy = FALSE} clonalHomeostasis(combined.TCR, cloneCall = "gene") @@ -83,7 +83,7 @@ The **clonalSplit** represents the ranking of clonotypes by copy or frequency of * 30000 * 100000 -```{r} +```{r tidy = FALSE} clonalProportion(combined.TCR, cloneCall = "gene") diff --git a/vignettes/articles/Clonal_Visualizations.Rmd b/vignettes/articles/Clonal_Visualizations.Rmd index b91a9e3c..44495ce5 100644 --- a/vignettes/articles/Clonal_Visualizations.Rmd +++ b/vignettes/articles/Clonal_Visualizations.Rmd @@ -60,7 +60,7 @@ The first function to explore the clonotypes is `clonalQuant()` to return the to + "both" for combined chain visualization + "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" to select single chain -```{r} +```{r tidy = FALSE} clonalQuant(combined.TCR, cloneCall="strict", chain = "both", @@ -80,7 +80,7 @@ clonalQuant(combined.TCR, We can also examine the relative distribution of clonotypes by abundance. Here `clonalAbundance()` will produce a line graph with a total number of clonotypes by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the contig object using the **group.by** variable in the function. -```{r} +```{r tidy = FALSE} clonalAbundance(combined.TCR, cloneCall = "gene", scale = FALSE) @@ -98,7 +98,7 @@ We can look at the length distribution of the CDR3 sequences by calling the `len + "both" for combined chain visualization + "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" to select single chain -```{r} +```{r tidy = FALSE} clonalLength(combined.TCR, cloneCall="aa", chain = "both") @@ -132,7 +132,7 @@ We can also look at clonotypes between samples and changes in dynamics by using **relabel.clones** * Simplify the isolated clonotypes to numerical designations to allow for a tidier visualization -```{r} +```{r tidy = FALSE} clonalCompare(combined.TCR, top.clones = 10, samples = c("P17B", "P17L"), @@ -142,7 +142,7 @@ clonalCompare(combined.TCR, We can also choose to highlight specific clonotypes, such as in the case of *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*: -```{r} +```{r tidy = FALSE} clonalCompare(combined.TCR, top.clones = 10, highlight.clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"), @@ -167,7 +167,7 @@ clonalCompare(combined.TCR, * "proportion" for the relative proportion the clonotype represents across all clonotypes * "count" for the total count of clonotypes by sample -```{r} +```{r tidy = FALSE} clonalScatter(combined.TCR, cloneCall ="gene", x.axis = "P18B", diff --git a/vignettes/articles/Combining_Contigs.Rmd b/vignettes/articles/Combining_Contigs.Rmd index 34b2138f..ee728f19 100644 --- a/vignettes/articles/Combining_Contigs.Rmd +++ b/vignettes/articles/Combining_Contigs.Rmd @@ -70,7 +70,7 @@ prevent issues with duplicate barcodes **(optional)**. The output of `combineTCR()` will be a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It will also combine the multiple reads into clonotype calls by either the nucleotide sequence (**CTnt**), amino acid sequence (**CTaa**), the VDJC gene sequence (**CTgene**), or the combination of the nucleotide and gene sequence (**CTstrict**). -```{r} +```{r tidy = FALSE} combined.TCR <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L"), @@ -96,7 +96,7 @@ The level of similarity in sequences to group together. **Default** is 0.85. Calculate the normalized edit distance (**TRUE**) or skip the calculation (**FALSE**). Skipping the edit distance calculation may save time, especially in the context of large data sets, but is not recommended. -```{r} +```{r tidy = FALSE} BCR.contigs <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv") combined.BCR <- combineBCR(BCR.contigs, samples = "P1", diff --git a/vignettes/articles/FAQ.Rmd b/vignettes/articles/FAQ.Rmd index ec0191cd..6598741f 100644 --- a/vignettes/articles/FAQ.Rmd +++ b/vignettes/articles/FAQ.Rmd @@ -76,7 +76,7 @@ For all visualizations in scRepertoire, there are 2 ways to adjust the color sch + Change the **palette** parameter to the desired color scheme. This approach uses the built-in palettes of grDevices and we can access the list of color schemes using ``hcl.pals()``. + Add a ggplot layer with a new color scheme. -```{r} +```{r tidy = FALSE} clonalQuant(combined.TCR, cloneCall="strict", chain = "both", @@ -94,7 +94,7 @@ clonalQuant(combined.TCR, Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph instead of the visual output. -```{r} +```{r tidy = FALSE} clonalQuant_output <- clonalQuant(combined.TCR, cloneCall="strict", scale = TRUE, diff --git a/vignettes/articles/Ibex.Rmd b/vignettes/articles/Ibex.Rmd index cbbb5798..dcbac33b 100644 --- a/vignettes/articles/Ibex.Rmd +++ b/vignettes/articles/Ibex.Rmd @@ -53,7 +53,7 @@ More information is available at the [Ibex GitHub Repo](https://github.com/ncbor ## Installation -```{r, eval = FALSE} +```{r, eval = FALSE, tidy = FALSE} devtools::install_github("ncborcherding/Ibex") ``` @@ -61,7 +61,7 @@ devtools::install_github("ncborcherding/Ibex") To show the multiple options of Ibex, the example data is derived from [this manuscript](https://pubmed.ncbi.nlm.nih.gov/33891889/) - multimodal single-cell characterization of COVID19-associated multisystem inflammatory syndrome in children. The data example built into the package (ibex_example) is derived from randomly sampling cells from Patient 1. -```{r} +```{r tidy = FALSE} SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Ibex_FullExample.rds")) ``` @@ -92,7 +92,7 @@ Ibex has 2 major functions - the first being ```Ibex.matrix()```, which is the b If choosing the geometric transformation, what value of theta to use (default is pi) -```{r} +```{r tidy = FALSE} ibex_vectors <- Ibex.matrix(SeuratObj, chains = "Light", encoder.input = "OHE") @@ -104,7 +104,7 @@ qplot(data = as.data.frame(ibex_vectors), Ibex_2, Ibex_3) + theme_classic() Additionally, ```runIbex()``` can be used to append the Seurat or Single-cell Experiment object with the Ibex vectors and allow for further analysis. Importantly, ```runIbex()``` will remove single cells that do not have recovered BCR data in the metadata of the object. -```{r} +```{r tidy = FALSE} SeuratObj <- runIbex(SeuratObj, chains = "Heavy", encoder.input = "KF", @@ -116,7 +116,7 @@ SeuratObj <- runIbex(SeuratObj, After ```runIbex()```, we have the encoded values stored under **"Ibex..."**. Using the Ibex reduction stored in Seurat, we can calculate the nearest neighbor and shared nearest neighbor indexes and generate a UMAP. -```{r} +```{r tidy = FALSE} #Generating UMAP from ibex Neighbors SeuratObj <- RunUMAP(SeuratObj, reduction = "ibex.KF", @@ -136,7 +136,7 @@ plot1 + plot2 We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija's group [paper](https://pubmed.ncbi.nlm.nih.gov/34062119/). We will use the RNA, ADT protein levels, and ibex vectors for the WNN calculations. -```{r} +```{r tidy = FALSE} SeuratObj <- FindMultiModalNeighbors( SeuratObj, reduction.list = list("pca", "apca", "ibex.KF"), @@ -166,7 +166,7 @@ plot3 + plot4 We can also look at the differences in the UMAP generated from RNA, ADT, or Ibex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above. -```{r} +```{r tidy = FALSE} SeuratObj <- RunUMAP(SeuratObj, reduction = 'pca', dims = 1:30, @@ -194,7 +194,7 @@ Recent [work](https://pubmed.ncbi.nlm.nih.gov/34426704/) has proposed using repr In order to generate a single-cell object based on the CoNGA approach, Ibex offers the function ```CoNGAfy()```. For **method**, select either "mean" or "dist" as described above. After performing ```CoNGAfy()```, the user can use any of the above reduction strategies. -```{r} +```{r tidy = FALSE} CoNGA.seurat <- CoNGAfy(SeuratObj, method = "dist") diff --git a/vignettes/articles/Loading.Rmd b/vignettes/articles/Loading.Rmd index ba058b36..f3af6ad6 100644 --- a/vignettes/articles/Loading.Rmd +++ b/vignettes/articles/Loading.Rmd @@ -71,7 +71,7 @@ Beyond the default 10x Genomic Cell Ranger pipeline outputs, scRepertoire suppor ```loadContigs()``` can be given a directory where the sequencing experiments are located and it will recursively load and process the contig data based on the file names. Alternatively, ```loadContigs()``` can be given a list of data frames and process the contig data -```{r, eval=FALSE} +```{r, eval=FALSE, tidy = FALSE} #Directory example contig.output <- c("~/Documents/MyExperiment") contig.list <- loadContigs(input = contig.output, @@ -94,7 +94,7 @@ It is now easy to create the contig list from a multiplexed experiment by first This function depends on the match of barcodes between the single-cell object and contigs. If there is a prefix or different suffix added to the barcode, this will result in no contigs recovered. Currently, it is recommended you do this step before the integration, as integration workflows commonly alter the barcodes. There is a **multi.run** variable that can be used on the integrated object. However, it assumes you have modified the barcodes with the Seurat pipeline (automatic addition of _# to end), and your contig list is in the same order. -```{r, eval = F} +```{r, eval = F, tidy = FALSE} contigs <- read.csv(".../outs/filtered_contig_annotations.csv") contig.list <- createHTOContigList(contigs, @@ -108,7 +108,7 @@ scRepertoire comes with a data set from T cells derived from four patients with The built-in example data is derived from the 10x Cell Ranger pipeline, so it is ready to go for downstream processing and analysis. -```{r} +```{r tidy = FALSE} data("contig_list") #the data built into scRepertoire head(contig_list[[1]]) diff --git a/vignettes/articles/Processing.Rmd b/vignettes/articles/Processing.Rmd index 57a81297..26c29889 100644 --- a/vignettes/articles/Processing.Rmd +++ b/vignettes/articles/Processing.Rmd @@ -49,7 +49,7 @@ combined.TCR <- combineTCR(contig_list, What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **variable.name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. -```{r} +```{r tidy = FALSE} combined.TCR <- addVariable(combined.TCR, variable.name = "Type", variables = rep(c("B", "L"), 4)) @@ -63,7 +63,7 @@ Likewise, we can remove specific list elements after `combineTCR()` using the `s Alternatively, we can also just select the list elements after `combineTCR()` or `combineBCR()`. -```{r} +```{r, tidy = FALSE} subset1 <- subsetClones(combined.TCR, name = "sample", variables = c("P18L", "P18B")) @@ -90,7 +90,7 @@ directory location to save the csv **file.name** the csv file name -```{r, eval = FALSE} +```{r, eval = FALSE, tidy = FALSE} exportClones(combined, write.file = TRUE, dir = "~/Documents/MyExperiment/Sample1/" diff --git a/vignettes/articles/Repertoire_Summary.Rmd b/vignettes/articles/Repertoire_Summary.Rmd index 28ed19c0..a591a3a3 100644 --- a/vignettes/articles/Repertoire_Summary.Rmd +++ b/vignettes/articles/Repertoire_Summary.Rmd @@ -67,7 +67,7 @@ A visualization of the relative usage of genes of the TCR or BCR, using `vizGene + "gene" to order by gene name + "variance" to order by variance between the separate variable categories -```{r} +```{r tidy = FALSE} vizGenes(combined.TCR, x.axis = "TRBV", y.axis = NULL, @@ -77,7 +77,7 @@ vizGenes(combined.TCR, `vizGenes()` can also be used to look at the usage of genes in a single chain. For example, say we are interested in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: -```{r} +```{r tidy = FALSE} #Peripheral Blood vizGenes(combined.TCR[c(1,3,5,7)], x.axis = "TRBV", @@ -95,7 +95,7 @@ vizGenes(combined.TCR[c(2,4,6,8)], For the P17 patient samples, what if we are interested in chain pairings, we can look at TRBV and TRAV at the same time using them as inputs to **x.axis** and **y.axis**. -```{r} +```{r tidy = FALSE} vizGenes(combined.TCR[c(1,2)], x.axis = "TRBV", y.axis = "TRAV", @@ -108,7 +108,7 @@ vizGenes(combined.TCR[c(1,2)], Quantify the proportion of amino acids along the cdr3 sequence with `percentAA()`. By default, the function will pad the sequences with NAs up to the maximum of **aa.length**. Sequences longer than **aa.length** will be removed before visualization. -```{r, message = FALSE} +```{r, message = FALSE, tidy = FALSE} percentAA(combined.TCR, chain = "TRB", aa.length = 20) @@ -118,7 +118,7 @@ percentAA(combined.TCR, Quantify the proportion of V or J gene usage with `percentGenes()`. Like `percentAA()`, we select the chain of interest and then indicate the gene of interest with the **gene** parameter. Two major limitations of `percentGenes()` are, 1) the function quantifies only V or J genes, and 2) the quantification of the genes are limited to all the V or J genes seen across the samples, not all possible V or J genes. -```{r} +```{r tidy = FALSE} percentGenes(combined.TCR, chain = "TRB", gene = "Vgene") @@ -126,7 +126,7 @@ percentGenes(combined.TCR, We can also use the output `percentGenes()` for dimensional reduction to summarise the gene usage by sample. This can be done with a simple principal component analysis (below) or even more complex reductions. -```{r} +```{r tidy = FALSE} df.genes <- percentGenes(combined.TCR, chain = "TRB", gene = "Vgene", @@ -152,7 +152,7 @@ ggplot(df, aes(x = PC1, y = PC2)) + Quantify the proportion of V and J gene usage with `percentVJ()`. Like `percentGenes()`, this function will quantify the percentage of V and J paired together across individual repertoires. The output can be visualized using a heatmap or as input for further dimensional reduction. -```{r} +```{r tidy = FALSE} percentVJ(combined.TCR, chain = "TRB") @@ -186,7 +186,7 @@ Numerical value for the length of the kmer. **top.motifs** Display the most variable genes determined by mean absolute deviation. -```{r} +```{r tidy = FALSE} percentKmer(combined.TCR, cloneCall = "aa", chain = "TRB", diff --git a/vignettes/articles/SC_Visualizations.Rmd b/vignettes/articles/SC_Visualizations.Rmd index ee0999c1..a5df7c8c 100644 --- a/vignettes/articles/SC_Visualizations.Rmd +++ b/vignettes/articles/SC_Visualizations.Rmd @@ -71,7 +71,7 @@ We can modify the contours by selecting the number of **bins** or the number of This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs), which is a great pipeline to annotated T cell subtypes. -```{r} +```{r tidy = FALSE} clonalOverlay(scRep_example, reduction = "umap", freq.cutpoint = 1, @@ -97,7 +97,7 @@ Filtering for clones can be accomplished using 3 methods: * Remove clones that comprise less than a certain proportion of clones in groups. -```{r} +```{r tidy = FALSE} #ggraph needs to be loaded due to issues with ggplot library(ggraph) @@ -119,7 +119,7 @@ clonalNetwork(scRep_example, We can also use **exportClones** to quickly get clones that are shared across multiple identity groups, along with the total number of clones in the data set. -```{r} +```{r tidy = FALSE} shared.clones <- clonalNetwork(scRep_example, reduction = "umap", group.by = "seurat_clusters", @@ -132,7 +132,7 @@ head(shared.clones) We can also look at the clonotypes by calling specific sequences in the `highlightClonotypes()` below. In order to highlight the clonotypes, we first need to use the **cloneCall**, the type of sequence we will be using, and then the specific sequences themselves using **sequence**. Below, we can see the steps to highlight the two most prominent sequences *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*. -```{r} +```{r tidy = FALSE} scRep_example <- highlightClones(scRep_example, cloneCall= "aa", sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", @@ -146,9 +146,9 @@ Seurat::DimPlot(scRep_example, group.by = "highlight") We can also look at the count of cells by cluster assigned into specific frequency ranges by using the `clonalOccupy()` function and selecting the **x.axis** to display cluster or other variables in the meta data of the single cell object. + **proportion** can be used to look at relative level groupings -+ **label** will still return the absolute number of clonotypes ++ **label** will still return the absolute number of clonotypes -```{r} +```{r tidy = FALSE} clonalOccupy(scRep_example, x.axis = "seurat_clusters") @@ -164,7 +164,7 @@ After the metadata has been modified, we can look at clones across multiple cate To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html). -```{r} +```{r tidy = FALSE} alluvialClones(scRep_example, cloneCall = "aa", y.axes = c("Patient", "ident", "Type"), @@ -186,7 +186,7 @@ The first step is getting the data frame output to feed into the `chordDiagram() This will calculate the relative number of clonotypes shared based on the **group.by** variable using the product of `combineExpression()`. It is important to note `getCirclize()` will create a matrix the size of the **group.by** variable and then simplify into instructions to be read by the circlize R package. The output is the total number of unique and shared clones by the **group.by** variable. -```{r} +```{r tidy = FALSE} library(circlize) library(scales) diff --git a/vignettes/articles/Trex.Rmd b/vignettes/articles/Trex.Rmd index a7330363..7a54d256 100644 --- a/vignettes/articles/Trex.Rmd +++ b/vignettes/articles/Trex.Rmd @@ -62,7 +62,7 @@ devtools::install_github("ncborcherding/Trex") To show the multiple options of Trex, the example data is derived from [GSE167118](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE167118) - a cohort of CITE-seq data derived from severe COVID-19 patients. The [corresponding manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/) is excellent. The data example built into the package (trex_example) is derived from randomly sampling the single-cells from patient 17. -```{r} +```{r tidy = FALSE} SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Trex_FullExample.rds")) ``` @@ -91,7 +91,7 @@ Trex has 2 major functions - the first being ```maTrex()```, which is the backbo **theta** If choosing the geometric transformation, what value of theta to use (default is pi) -```{r} +```{r tidy = FALSE} Trex_vectors <- maTrex(SeuratObj, chains = "TRA", encoder.model = "VAE", @@ -115,7 +115,7 @@ ggplot(as.data.frame(Trex_vectors2), aes(x = Trex_1, y = Trex_2)) + Additionally, ```runTrex()``` can be used to append the Seurat or Single-cell Experiment object with the Trex vectors and allow for further analysis. Importantly, ```runTrex()``` will remove single cells that do not have recovered TCR data in the metadata of the object. -```{r} +```{r tidy = FALSE} SeuratObj <- runTrex(SeuratObj, chains = "TRB", encoder.model = "VAE", @@ -127,7 +127,7 @@ SeuratObj <- runTrex(SeuratObj, After ```runTrex()``` we have the encoded values stored under **"Trex..."**. Using the Trex reduction stored in Seurat, we can calculate the nearest neighbor and shared nearest neighbor indexes and generate a UMAP. -```{r} +```{r tidy = FALSE} #Generating UMAP from Trex Neighbors SeuratObj <- RunUMAP(SeuratObj, reduction = "Trex.KF", @@ -151,7 +151,7 @@ plot1 + plot2 We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija's group [paper](https://pubmed.ncbi.nlm.nih.gov/34062119/). We will use the RNA, ADT protein levels, and Trex vectors for the WNN calculations. -```{r} +```{r tidy = FALSE} SeuratObj <- FindMultiModalNeighbors( SeuratObj, reduction.list = list("pca", "apca", "Trex.KF"), @@ -185,7 +185,7 @@ plot3 + plot4 We can also look at the differences in the UMAP generated from RNA, ADT, or Trex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above. -```{r} +```{r tidy = FALSE} SeuratObj <- RunUMAP(SeuratObj, reduction = 'pca', dims = 1:30, @@ -213,7 +213,7 @@ Recent [work](https://pubmed.ncbi.nlm.nih.gov/34426704/) has proposed using repr In order to generate a single-cell object based on the CoNGA approach, Trex offers the function ```CoNGAfy()```. For **method**, select either "mean" or "dist" as described above. After performing ```CoNGAfy()```, the user can use any of the above reduction strategies. -```{r} +```{r tidy = FALSE} library(dplyr, include.only = c("%>%")) CoNGA.seurat <- CoNGAfy(SeuratObj, @@ -250,7 +250,7 @@ The database is collated from multiple sources and represents a great deal of ef 4. [TBA](https://db.cngb.org/pird/home/) (now called PIRD) - [citation](https://pubmed.ncbi.nlm.nih.gov/31373607/) -```{r} +```{r tidy = FALSE} CoNGA.seurat <- annotateDB(CoNGA.seurat, chains = "TRB") @@ -267,7 +267,7 @@ DimPlot(CoNGA.seurat, Or ```annotateDB()``` can be used on the full single-cell object and examine the sequence information along the RNA-based UMAP. An added feature to the function allows the annotations to be extended to CDR3 sequences that are within defined edit distances from the reference using **edit.distance**. -```{r} +```{r tidy = FALSE} SeuratObj <- annotateDB(SeuratObj, chains = "TRB", edit.distance = 2) From b56a2fc9c60b0bf88c6ae576f092dadbc7f9fd47 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 4 Nov 2023 06:56:06 -0500 Subject: [PATCH 117/142] removing extra line in vignettes --- inst/pkgdown.yml | 2 +- vignettes/articles/Attaching_SC.Rmd | 1 - vignettes/articles/Clonal_Bias.Rmd | 1 - vignettes/articles/Clonal_Cluster.Rmd | 1 - vignettes/articles/Clonal_Diversity.Rmd | 1 - vignettes/articles/Clonal_Dynamics.Rmd | 1 - vignettes/articles/Clonal_Visualizations.Rmd | 1 - vignettes/articles/Combining_Contigs.Rmd | 1 - vignettes/articles/FAQ.Rmd | 1 - vignettes/articles/Ibex.Rmd | 1 - vignettes/articles/Installation.Rmd | 1 - vignettes/articles/Loading.Rmd | 1 - vignettes/articles/Processing.Rmd | 1 - vignettes/articles/Repertoire_Summary.Rmd | 1 - vignettes/articles/SC_Visualizations.Rmd | 1 - vignettes/articles/Trex.Rmd | 1 - 16 files changed, 1 insertion(+), 16 deletions(-) diff --git a/inst/pkgdown.yml b/inst/pkgdown.yml index c44519b9..8aceb45b 100644 --- a/inst/pkgdown.yml +++ b/inst/pkgdown.yml @@ -19,7 +19,7 @@ articles: Trex: Trex.html Trex_Trajectory: Trex_Trajectory.html vignette: vignette.html -last_built: 2023-11-04T11:12Z +last_built: 2023-11-04T11:34Z urls: reference: https://www.borch.dev/uploads/scRepertoire/reference article: https://www.borch.dev/uploads/scRepertoire/articles diff --git a/vignettes/articles/Attaching_SC.Rmd b/vignettes/articles/Attaching_SC.Rmd index a7b5f192..1debaca4 100644 --- a/vignettes/articles/Attaching_SC.Rmd +++ b/vignettes/articles/Attaching_SC.Rmd @@ -9,7 +9,6 @@ vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8} --- -*** + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + + + + +3 +6 +9 +Clone Size +Clonotype Bias + +cloneSize + + +Large (0.01 < X <= 0.1) +Hyperexpanded (0.1 < X <= 1) + +Top_state + + + + + +3 +4 +5 +9 +13 +clonalBias_plot + + diff --git a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg index f7cb4e13..618876f2 100644 --- a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg +++ b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg @@ -145,7 +145,7 @@ - + @@ -314,13 +314,13 @@ - + - + @@ -402,7 +402,7 @@ - + @@ -588,9 +588,9 @@ - + - + diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg index f9433f2f..ae17a8f1 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg @@ -55,18 +55,18 @@ 6000 Number of individuals Species diversity - - - -Rarefaction -Extrapolation - - - - - -P17B -P17L + + + + + +P17B +P17L + + + +Rarefaction +Extrapolation clonalclonalRarefaction_h0_p1_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg index 8fc4bd97..45c6e0c0 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg @@ -55,18 +55,18 @@ 2000 Number of individuals Sample coverage - - - -Rarefaction -Extrapolation - - - - - -P18B -P18L + + + + + +P18B +P18L + + + +Rarefaction +Extrapolation clonalclonalRarefaction_h1_p2_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg index 2e6f838b..a0bd2297 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg @@ -29,7 +29,7 @@ - + @@ -55,18 +55,18 @@ 0.6 Sample coverage Species diversity - - - -Rarefaction -Extrapolation - - - - - -P19B -P19L + + + + + +P19B +P19L + + + +Rarefaction +Extrapolation clonalclonalRarefaction_h2_p3_plot diff --git a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg index cb2025f6..00fedb5c 100644 --- a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg +++ b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg @@ -38,19 +38,19 @@ - - - + + + - - - - - - + + + + + + @@ -80,14 +80,14 @@ - + -0.1 0.0 0.1 0.2 0.3 -0.4 +0.4 0.5 clonalSizeDistribution_plot diff --git a/tests/testthat/test-StartractDiversity.R b/tests/testthat/test-StartractDiversity.R index 3499088d..b42673a0 100644 --- a/tests/testthat/test-StartractDiversity.R +++ b/tests/testthat/test-StartractDiversity.R @@ -20,6 +20,7 @@ test_that("StartracDiversity works", { type = "Type", group.by = "Patient", exportTable = TRUE), - getdata("seuratFunctions", "StartracDiversity_exportTable") + getdata("seuratFunctions", "StartracDiversity_exportTable"), + tolerance = 1e-6 ) }) diff --git a/tests/testthat/test-seurat_command.R b/tests/testthat/test-seurat_command.R index 797b1706..e9345cd4 100644 --- a/tests/testthat/test-seurat_command.R +++ b/tests/testthat/test-seurat_command.R @@ -1,7 +1,8 @@ test_that("make_screp_seurat_cmd works", { data("scRep_example") - test_obj <- combineExpression(getCombined(), scRep_example) + combined <- getCombined() + test_obj <- combineExpression(combined, scRep_example) expect_true(!is.null(test_obj@commands[["combineExpression"]])) From e878a4103672157f60f598f4aa4176559d391208 Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Tue, 7 Nov 2023 07:55:09 -0800 Subject: [PATCH 128/142] sped up .parseTCR by using barcodeIndex --- R/RcppExports.R | 4 + R/utils.R | 14 +++- src/RcppExports.cpp | 13 +++ src/ntKmers.cpp | 2 +- src/parseTCR.cpp | 65 ++++++++++++++ src/scRepHelper.h | 2 +- .../_snaps/clonalBias/clonalbias-plot.new.svg | 84 ------------------- 7 files changed, 95 insertions(+), 89 deletions(-) create mode 100644 src/parseTCR.cpp delete mode 100644 tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg diff --git a/R/RcppExports.R b/R/RcppExports.R index 4ad0fa25..6d14107c 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -13,3 +13,7 @@ rcppGetNtKmerPercent <- function(seqs, k) { .Call(`_scRepertoire_rcppGetNtKmerPercent`, seqs, k) } +constructBarcodeIndex <- function(conDfBarcodes, data2Barcodes) { + .Call(`_scRepertoire_constructBarcodeIndex`, conDfBarcodes, data2Barcodes) +} + diff --git a/R/utils.R b/R/utils.R index 3c274bab..7cc7280b 100644 --- a/R/utils.R +++ b/R/utils.R @@ -151,6 +151,10 @@ is_seurat_or_se_object <- function(obj) { rownames(meta) <- sc@colData@rownames clu <- which(colnames(meta) == "ident") colnames(meta)[clu] <- "ident" + } else { + stop("Object indicated is not of class 'Seurat' or + 'SummarizedExperiment', make sure you are using + the correct data.") } return(meta) } @@ -277,14 +281,18 @@ is_seurat_or_se_object <- function(obj) { #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding #' @keywords internal .parseTCR <- function(Con.df, unique_df, data2) { + tcr1_index <- 2 tcr2_index <- 5 + data2_chain_index <- 6 + data2 <- data2 %>% arrange(., chain, cdr3_nt) + barcodeIndex <- constructBarcodeIndex(Con.df[, 1], data2[[1]]) + for (y in seq_along(unique_df)){ - barcode.i <- Con.df$barcode[y] - location.i <- which(barcode.i == data2$barcode) + location.i <- barcodeIndex[[y]] for (z in seq_along(location.i)) { - where.chain <- data2[location.i[z],"chain"] + where.chain <- data2[location.i[z], data2_chain_index] if (where.chain == "TRA" || where.chain == "TRG") { if (is.na(Con.df[y, tcr1_index])) { diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 6745a088..fac5d987 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -46,11 +46,24 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// constructBarcodeIndex +std::vector> constructBarcodeIndex(std::vector& conDfBarcodes, std::vector& data2Barcodes); +RcppExport SEXP _scRepertoire_constructBarcodeIndex(SEXP conDfBarcodesSEXP, SEXP data2BarcodesSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< std::vector& >::type conDfBarcodes(conDfBarcodesSEXP); + Rcpp::traits::input_parameter< std::vector& >::type data2Barcodes(data2BarcodesSEXP); + rcpp_result_gen = Rcpp::wrap(constructBarcodeIndex(conDfBarcodes, data2Barcodes)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_scRepertoire_rcppGetAaKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetAaKmerPercent, 3}, {"_scRepertoire_rcppGenerateUniqueNtMotifs", (DL_FUNC) &_scRepertoire_rcppGenerateUniqueNtMotifs, 1}, {"_scRepertoire_rcppGetNtKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetNtKmerPercent, 2}, + {"_scRepertoire_constructBarcodeIndex", (DL_FUNC) &_scRepertoire_constructBarcodeIndex, 2}, {NULL, NULL, 0} }; diff --git a/src/ntKmers.cpp b/src/ntKmers.cpp index 7b6e3e6c..7c1cbe9e 100644 --- a/src/ntKmers.cpp +++ b/src/ntKmers.cpp @@ -84,7 +84,7 @@ Rcpp::NumericVector rcppGetNtKmerPercent(const std::vector& seqs, c std::vector bins (numKmers, 0); for (std::string seq : seqs) { - if (seq.size() < k) { + if ((int) seq.size() < k) { continue; } kmerCount(bins, mask, seq, k); diff --git a/src/parseTCR.cpp b/src/parseTCR.cpp new file mode 100644 index 00000000..b989a902 --- /dev/null +++ b/src/parseTCR.cpp @@ -0,0 +1,65 @@ +// Helpers for .parseTCR in utils.R +// By Qile Yang + +#include +#include +#include +#include + +#define BarcodeIndciesMap std::unordered_map> + +BarcodeIndciesMap stringToIndiciesMap(std::vector& v) { + BarcodeIndciesMap map; + for (int i = 0; i < (int) v.size(); i++) { + if (map.find(v[i]) == map.end()) { + map[v[i]] = {}; + } + map[v[i]].push_back(i + 1); // + 1 for R indexing, otherwise i for C++ indexing + } + return map; +} + +// [[Rcpp::export]] +std::vector> constructBarcodeIndex( + std::vector& conDfBarcodes, std::vector& data2Barcodes +) { + std::vector> outputBarcodeIndex (conDfBarcodes.size()); + BarcodeIndciesMap data2BarcodeIndiciesMap = stringToIndiciesMap(data2Barcodes); + + for (int i = 0; i < (int) conDfBarcodes.size(); i++) { + std::string& barcode = conDfBarcodes[i]; + if (data2BarcodeIndiciesMap.find(barcode) != data2BarcodeIndiciesMap.end()) { + outputBarcodeIndex[i] = data2BarcodeIndiciesMap[barcode]; + } + } + + return outputBarcodeIndex; +} + +// // [[Rcpp::export]] +// Rcpp::Dataframe rcppParseTcr(Rcpp::Dataframe& conDf, Rcpp::Dataframe& data2) { +// Rcpp::CharacterVector conDfBarcodes = conDf[0]; // this is a reference and is equivalent to unique_df +// Rcpp::CharacterVector data2Barcodes = data2[0]; + +// if (conDfBarcodes.size() == 0) { +// return conDf; +// } + +// std::vector> barcodeIndex = constructBarcodeIndex(conDfBarcodes, data2Barcodes) + +// for (int y = 0; y < (int) conDfBarcodes.size(); y++) { +// std::vector indicies = barcodeIndex[y]; +// if (indicies.size() == 0) { +// continue; +// } + +// // for (int x = 0; x < (int) conDf.size(); x++) { +// // conDf[x][y] = data2[x][indicies[0]]; +// // for (int i = 1; i < (int) index.size(); i++) { +// // conDf[x][y] += "," + data2[x][index[i]]; +// // } +// // } +// } + +// return conDf; +// } \ No newline at end of file diff --git a/src/scRepHelper.h b/src/scRepHelper.h index d1e5dd20..be166002 100644 --- a/src/scRepHelper.h +++ b/src/scRepHelper.h @@ -3,7 +3,7 @@ class scRepHelper { public: - static const long double sum(std::vector& v) { + static long double sum(std::vector& v) { long double n = 0; for (long double num : v) { n += num; diff --git a/tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg b/tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg deleted file mode 100644 index 862f9457..00000000 --- a/tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg +++ /dev/null @@ -1,84 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - -3 -6 -9 -Clone Size -Clonotype Bias - -cloneSize - - -Large (0.01 < X <= 0.1) -Hyperexpanded (0.1 < X <= 1) - -Top_state - - - - - -3 -4 -5 -9 -13 -clonalBias_plot - - From d0c3a4a5d22211c5b0185c96cc81a3ad9963ff0f Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Tue, 7 Nov 2023 08:07:29 -0800 Subject: [PATCH 129/142] slightly refactored parseTCR --- R/RcppExports.R | 4 ++-- R/utils.R | 4 ++-- src/RcppExports.cpp | 10 +++++----- src/parseTCR.cpp | 10 ++++------ 4 files changed, 13 insertions(+), 15 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 6d14107c..90f25dca 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -13,7 +13,7 @@ rcppGetNtKmerPercent <- function(seqs, k) { .Call(`_scRepertoire_rcppGetNtKmerPercent`, seqs, k) } -constructBarcodeIndex <- function(conDfBarcodes, data2Barcodes) { - .Call(`_scRepertoire_constructBarcodeIndex`, conDfBarcodes, data2Barcodes) +rcppConstructBarcodeIndex <- function(conDfBarcodes, data2Barcodes) { + .Call(`_scRepertoire_rcppConstructBarcodeIndex`, conDfBarcodes, data2Barcodes) } diff --git a/R/utils.R b/R/utils.R index 7cc7280b..3034e6c8 100644 --- a/R/utils.R +++ b/R/utils.R @@ -278,7 +278,7 @@ is_seurat_or_se_object <- function(obj) { } # Assigning positions for TCR contig data -#' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding +#' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding, Qile Yang #' @keywords internal .parseTCR <- function(Con.df, unique_df, data2) { @@ -287,7 +287,7 @@ is_seurat_or_se_object <- function(obj) { data2_chain_index <- 6 data2 <- data2 %>% arrange(., chain, cdr3_nt) - barcodeIndex <- constructBarcodeIndex(Con.df[, 1], data2[[1]]) + barcodeIndex <- rcppConstructBarcodeIndex(Con.df[, 1], data2[[1]]) for (y in seq_along(unique_df)){ location.i <- barcodeIndex[[y]] diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index fac5d987..6158ed0d 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -46,15 +46,15 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } -// constructBarcodeIndex -std::vector> constructBarcodeIndex(std::vector& conDfBarcodes, std::vector& data2Barcodes); -RcppExport SEXP _scRepertoire_constructBarcodeIndex(SEXP conDfBarcodesSEXP, SEXP data2BarcodesSEXP) { +// rcppConstructBarcodeIndex +std::vector> rcppConstructBarcodeIndex(std::vector& conDfBarcodes, std::vector& data2Barcodes); +RcppExport SEXP _scRepertoire_rcppConstructBarcodeIndex(SEXP conDfBarcodesSEXP, SEXP data2BarcodesSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; Rcpp::traits::input_parameter< std::vector& >::type conDfBarcodes(conDfBarcodesSEXP); Rcpp::traits::input_parameter< std::vector& >::type data2Barcodes(data2BarcodesSEXP); - rcpp_result_gen = Rcpp::wrap(constructBarcodeIndex(conDfBarcodes, data2Barcodes)); + rcpp_result_gen = Rcpp::wrap(rcppConstructBarcodeIndex(conDfBarcodes, data2Barcodes)); return rcpp_result_gen; END_RCPP } @@ -63,7 +63,7 @@ static const R_CallMethodDef CallEntries[] = { {"_scRepertoire_rcppGetAaKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetAaKmerPercent, 3}, {"_scRepertoire_rcppGenerateUniqueNtMotifs", (DL_FUNC) &_scRepertoire_rcppGenerateUniqueNtMotifs, 1}, {"_scRepertoire_rcppGetNtKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetNtKmerPercent, 2}, - {"_scRepertoire_constructBarcodeIndex", (DL_FUNC) &_scRepertoire_constructBarcodeIndex, 2}, + {"_scRepertoire_rcppConstructBarcodeIndex", (DL_FUNC) &_scRepertoire_rcppConstructBarcodeIndex, 2}, {NULL, NULL, 0} }; diff --git a/src/parseTCR.cpp b/src/parseTCR.cpp index b989a902..d274cd86 100644 --- a/src/parseTCR.cpp +++ b/src/parseTCR.cpp @@ -14,13 +14,13 @@ BarcodeIndciesMap stringToIndiciesMap(std::vector& v) { if (map.find(v[i]) == map.end()) { map[v[i]] = {}; } - map[v[i]].push_back(i + 1); // + 1 for R indexing, otherwise i for C++ indexing + map[v[i]].push_back(i + 1); // i + 1 for R indexing } return map; } // [[Rcpp::export]] -std::vector> constructBarcodeIndex( +std::vector> rcppConstructBarcodeIndex( std::vector& conDfBarcodes, std::vector& data2Barcodes ) { std::vector> outputBarcodeIndex (conDfBarcodes.size()); @@ -36,6 +36,7 @@ std::vector> constructBarcodeIndex( return outputBarcodeIndex; } +// this is an upcoming function to *maybe* replace the R .parseTCR function. // // [[Rcpp::export]] // Rcpp::Dataframe rcppParseTcr(Rcpp::Dataframe& conDf, Rcpp::Dataframe& data2) { // Rcpp::CharacterVector conDfBarcodes = conDf[0]; // this is a reference and is equivalent to unique_df @@ -54,10 +55,7 @@ std::vector> constructBarcodeIndex( // } // // for (int x = 0; x < (int) conDf.size(); x++) { -// // conDf[x][y] = data2[x][indicies[0]]; -// // for (int i = 1; i < (int) index.size(); i++) { -// // conDf[x][y] += "," + data2[x][index[i]]; -// // } +// // // unfinished // // } // } From 8c5bbc675f652ae113b5d4579d3a9df684133984 Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Tue, 7 Nov 2023 08:13:04 -0800 Subject: [PATCH 130/142] slight modification to src/parseTCR.cpp --- src/parseTCR.cpp | 3 --- 1 file changed, 3 deletions(-) diff --git a/src/parseTCR.cpp b/src/parseTCR.cpp index d274cd86..4f93915a 100644 --- a/src/parseTCR.cpp +++ b/src/parseTCR.cpp @@ -11,9 +11,6 @@ BarcodeIndciesMap stringToIndiciesMap(std::vector& v) { BarcodeIndciesMap map; for (int i = 0; i < (int) v.size(); i++) { - if (map.find(v[i]) == map.end()) { - map[v[i]] = {}; - } map[v[i]].push_back(i + 1); // i + 1 for R indexing } return map; From 8a5a2a2ea50d5268733364827333c6d1d81d1503 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 8 Nov 2023 08:31:04 -0600 Subject: [PATCH 131/142] working through final testthat Still have issues with clonalNetwork --- R/clonalBias.R | 16 ++-- R/clonalNetwork.R | 5 +- R/clonalOverlap.R | 3 +- R/startracDiversity.R | 2 +- .../startracdiversity-plot.svg | 3 +- .../_snaps/clonalBias/clonalbias-plot.new.svg | 84 ------------------- .../_snaps/clonalBias/clonalbias-plot.svg | 62 +++++++------- .../clonalOverlay/clonaloverlay-plot.svg | 12 +-- .../clonalclonalrarefaction-h0-p1-plot.svg | 24 +++--- .../clonalclonalrarefaction-h1-p2-plot.svg | 24 +++--- .../clonalclonalrarefaction-h2-p3-plot.svg | 26 +++--- .../clonalsizedistribution-plot.svg | 22 ++--- vignettes/articles/Trex_Trajectory.Rmd | 83 +++++++++++++++--- 13 files changed, 172 insertions(+), 194 deletions(-) delete mode 100644 tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg diff --git a/R/clonalBias.R b/R/clonalBias.R index 9a12419c..00aca5cd 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -104,25 +104,29 @@ clonalBias <- function(sc.data, bias$cloneSize[i] <- as.vector(meta[which(meta[,cloneCall] == clone & meta[,split.by] == split),"cloneSize"])[1] } - bias$cloneSize <- factor(bias$cloneSize , rev(levels(meta[, "cloneSize"]))) + bias$cloneSize <- factor(bias$cloneSize, rev(levels(meta[, "cloneSize"]))) if (exportTable) { return(bias) } - + bias$dotSize <- as.numeric(bias$cloneSize) + #else, return the plot ggplot(bias, aes(x=ncells,y=bias)) + - geom_point(aes(fill=Top_state, size = cloneSize), shape = 21, stroke = 0.25) + + geom_point(aes(fill=Top_state, size = dotSize), shape = 21, stroke = 0.25) + stat_quantile(data=df_shuffle, quantiles = c(corrected_p), method = "rqss", - lambda=3, + lambda = 3, color = "black", lty = 2) + + #This is ridiculous way to get around the internal ggplot "style"-based warnings + scale_size_continuous(breaks = as.vector(unique(bias$dotSize)), labels = as.vector(unique(bias$cloneSize))) + scale_fill_manual(values = .colorizer(palette, length(unique(bias[,"Top_state"])))) + theme_classic() + - xlab("Clone Size") + - ylab("Clonotype Bias") + labs(size = "cloneSize", fill = "Group") + + xlab("Clonal Size") + + ylab("Clonal Bias") } #Background summary of clones diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index 0f476a30..991f77d1 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -176,7 +176,7 @@ clonalNetwork <- function(sc.data, } } if(is.null(edge.list)) { - stop("No shared clonotypes between the indicated identity") + stop("No shared clones between the indicated identity") } edge.list <- data.frame(edge.list) @@ -207,7 +207,8 @@ clonalNetwork <- function(sc.data, if (exportTable) { return(edge.list1) } - + #Warning from this is produced by geom_edge_bend and there is nothng that can be done + #until the authors of the package update it. plot <- ggraph(graph, layout = centers[match(names(V(graph)), rownames(centers)),]) + geom_point(data = coord, aes(x = coord[,1], y = coord[,2], color = group.by)) + geom_edge_bend(aes(edge_color = as.numeric(weight)), diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index e3b218e5..fc096f9b 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -115,7 +115,8 @@ clonalOverlap <- function(input.data, labs(fill = str_to_title(method)) + geom_text(aes(label = round(value, digits = 3), color = ifelse(value <= mean_value, - "white", "black"))) + + "white", "black")), + na.rm = TRUE) + scale_fill_gradientn(colors = .colorizer(palette, 7), na.value = "white") + scale_color_identity() + theme_classic() + diff --git a/R/startracDiversity.R b/R/startracDiversity.R index c1beb74c..8bfccf22 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -106,7 +106,7 @@ StartracDiversity <- function(sc.data, col <- length(unique(mat_melt[,"majorCluster"])) plot <- ggplot(mat_melt, aes(x=majorCluster, y=value)) + - geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0) + + geom_boxplot(aes(fill = majorCluster), outlier.alpha = 0, na.rm = TRUE) + facet_grid(variable ~.) + theme_classic() + ylab("Index Score") + diff --git a/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg b/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg index 15010d3b..af181bdd 100644 --- a/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg +++ b/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg @@ -176,7 +176,6 @@ - @@ -200,6 +199,7 @@ + @@ -217,7 +217,6 @@ - diff --git a/tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg b/tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg deleted file mode 100644 index 862f9457..00000000 --- a/tests/testthat/_snaps/clonalBias/clonalbias-plot.new.svg +++ /dev/null @@ -1,84 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - -3 -6 -9 -Clone Size -Clonotype Bias - -cloneSize - - -Large (0.01 < X <= 0.1) -Hyperexpanded (0.1 < X <= 1) - -Top_state - - - - - -3 -4 -5 -9 -13 -clonalBias_plot - - diff --git a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg index 0e7c5d57..5bea828a 100644 --- a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg +++ b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg @@ -28,17 +28,17 @@ - - - - - - - - - - - + + + + + + + + + + + @@ -58,26 +58,26 @@ 3 6 9 -Clone Size -Clonotype Bias - -cloneSize - - -Large (0.01 < X <= 0.1) -Hyperexpanded (0.1 < X <= 1) - -Top_state - - - - - -3 -4 -5 -9 -13 +Clonal Size +Clonal Bias + +Group + + + + + +3 +4 +5 +9 +13 + +cloneSize + + +Hyperexpanded (0.1 < X <= 1) +Large (0.01 < X <= 0.1) clonalBias_plot diff --git a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg index 618876f2..f7cb4e13 100644 --- a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg +++ b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg @@ -145,7 +145,7 @@ - + @@ -314,13 +314,13 @@ - + - + @@ -402,7 +402,7 @@ - + @@ -588,9 +588,9 @@ - + - + diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg index ae17a8f1..f9433f2f 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg @@ -55,18 +55,18 @@ 6000 Number of individuals Species diversity - - - - - -P17B -P17L - - - -Rarefaction -Extrapolation + + + +Rarefaction +Extrapolation + + + + + +P17B +P17L clonalclonalRarefaction_h0_p1_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg index 45c6e0c0..8fc4bd97 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg @@ -55,18 +55,18 @@ 2000 Number of individuals Sample coverage - - - - - -P18B -P18L - - - -Rarefaction -Extrapolation + + + +Rarefaction +Extrapolation + + + + + +P18B +P18L clonalclonalRarefaction_h1_p2_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg index a0bd2297..2e6f838b 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg @@ -29,7 +29,7 @@ - + @@ -55,18 +55,18 @@ 0.6 Sample coverage Species diversity - - - - - -P19B -P19L - - - -Rarefaction -Extrapolation + + + +Rarefaction +Extrapolation + + + + + +P19B +P19L clonalclonalRarefaction_h2_p3_plot diff --git a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg index 00fedb5c..cb2025f6 100644 --- a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg +++ b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg @@ -38,19 +38,19 @@ - - - + + + - - - - - - + + + + + + @@ -80,14 +80,14 @@ - + -0.1 0.0 0.1 0.2 0.3 -0.4 +0.4 0.5 clonalSizeDistribution_plot diff --git a/vignettes/articles/Trex_Trajectory.Rmd b/vignettes/articles/Trex_Trajectory.Rmd index bf29181e..979fd04d 100644 --- a/vignettes/articles/Trex_Trajectory.Rmd +++ b/vignettes/articles/Trex_Trajectory.Rmd @@ -89,10 +89,34 @@ library(slingshot) [Trex](https://github.com/ncborcherding/Trex) takes the CDR3 amino acid sequence and embeds them into latent dimensional space using convolutional neural networks. An introduction to Trex and the models available can be found in the [scRepertoire website](https://www.borch.dev/uploads/screpertoire/articles/trex). Below we will use variational autoencoders (VAE) using the the Kidera factors. -```{r} -SeuratObj <- subset(scRep_example, Patient == "P18") +For the purpose of this vignette, we will use Patent 17 samples and look specifically at CD4+ cells. Low expression of CD4 mRNA is commonly seen in single-cell RNA sequencing, so we will use the absence of *CD8A* and *CD8B* as a proxy for CD8 cells. + +```{r tidy=FALSE} +#Subsetting Patient Samples +SeuratObj <- subset(scRep_example, Patient == "P17") +#Subsetting CD4 Cells +"%!in%" <- Negate("%in%") +CD8A.pos <- rownames(SeuratObj[[]])[which(SeuratObj@assays$RNA@counts["CD8A",] > 1)] +CD8B.pos <- rownames(SeuratObj[[]])[which(SeuratObj@assays$RNA@counts["CD8B",] > 1)] +CD8.cells <- unique(CD8A.pos, CD8B.pos) +CD4.cells <- rownames(SeuratObj[[]])[rownames(SeuratObj[[]]) %!in% CD8.cells] +SeuratObj <- subset(SeuratObj, cells = CD4.cells) +###################### +#Getting RNA Reductions +####################### + +SeuratObj <- SeuratObj %>% + NormalizeData(verbose = FALSE) %>% + FindVariableFeatures(verbose = FALSE) %>% + quietTCRgenes() %>% + ScaleData(verbose = FALSE) %>% + RunPCA(verbose = FALSE) + +###################### +#Getting TCR Reductions +####################### SeuratObj <- runTrex(SeuratObj, chains = "TRA", @@ -122,22 +146,55 @@ plot1 + plot2 ``` +## Creating RNA/TCR Clusters and UMAPs -```{r} -SeuratObj <- SeuratObj %>% - NormalizeData(verbose = FALSE) %>% - FindVariableFeatures(verbose = FALSE) %>% - quietTCRgenes() %>% - ScaleData(verbose = FALSE) %>% - RunPCA(verbose = FALSE) +```{r tidy=FALSE} +rescaled.values <- rescaleByNeighbors( + list(RNA = SeuratObj@reductions$pca@cell.embeddings, + TRA = SeuratObj@reductions$Trex.TRA.KF@cell.embeddings[,1:3], + TRB = SeuratObj@reductions$Trex.TRB.KF@cell.embeddings[,1:3])) + +y <- DiffusionMap(rescaled.values) +plot(y) +phate.coord <- phate(rescaled.values) -rescaled.output <- rescaleByNeighbors( +ggplot(phate.coord) + + geom_point(aes(PHATE1, PHATE2, color = SeuratObj$CTaa)) + + guides(color = "none") + + +mds <- as.data.frame(calculateMDS(t(rescaled.values))) +ggplot(mds) + + geom_point(aes(mds[,1], mds[,2], color = SeuratObj$CTaa)) + + guides(color = "none") + +umap <- as.data.frame(calculateUMAP(t(rescaled.values))) +ggplot(umap) + + geom_point(aes(umap[,1], umap[,2], color = SeuratObj$CTaa)) + + guides(color = "none") + +umap.trimodal <- calculateMultiUMAP( list(RNA = SeuratObj@reductions$pca@cell.embeddings, - TRA = SeuratObj@reductions$Trex.TRA.KF@cell.embeddings, - TRB = SeuratObj@reductions$Trex.TRB.KF@cell.embeddings) - ) + TRA = SeuratObj@reductions$Trex.TRA.KF@cell.embeddings[,1:3], + TRB = SeuratObj@reductions$Trex.TRB.KF@cell.embeddings[,1:3]), + # metric = "cosine", + n_component = 25) +SeuratObj[["triUMAP"]] <- CreateDimReducObject(embeddings = umap.trimodal, + key = "triUMAP") + +SeuratObj <- SeuratObj %>% + FindNeighbors(reduction = "triUMAP", + dims = 1:20) %>% + FindClusters(algorithm = 4, + resolution = 0.1) + +DimPlot(SeuratObj, reduction = "triUMAP") +DimPlot(SeuratObj, group.by = "CTaa", reduction = "triUMAP") + + theme(plot.title = element_blank()) + + scale_color_viridis(option = "B", discrete = TRUE) + + NoLegend() ``` From b762f0117f508dfbbf51a66ddd38f6ec033188c7 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 8 Nov 2023 09:54:21 -0600 Subject: [PATCH 132/142] Removing legends on testthat svg --- DESCRIPTION | 1 + R/clonalBias.R | 1 + .../_snaps/clonalBias/clonalbias-plot.svg | 68 +- .../clonalOverlay/clonaloverlay-plot.svg | 1224 ++++++++--------- .../clonalclonalrarefaction-h0-p1-plot.svg | 52 +- .../clonalclonalrarefaction-h1-p2-plot.svg | 48 +- .../clonalclonalrarefaction-h2-p3-plot.svg | 52 +- tests/testthat/test-StartractDiversity.R | 9 +- tests/testthat/test-clonalBias.R | 3 +- tests/testthat/test-clonalOverlay.R | 3 +- tests/testthat/test-clonalRarefaction.R | 9 +- tests/testthat/test-clonalSizeDistribution.R | 2 +- 12 files changed, 694 insertions(+), 778 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 309dae38..504c0cbf 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -29,6 +29,7 @@ Imports: reshape2, rjson, rlang, + Russ, S4Vectors, SeuratObject, SingleCellExperiment, diff --git a/R/clonalBias.R b/R/clonalBias.R index 00aca5cd..af33142c 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -42,6 +42,7 @@ #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 +#' @importClassesFrom quantreg rqss #' @importFrom stringr str_sort #' @export #' @concept SC_Functions diff --git a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg index 5bea828a..9d7c6221 100644 --- a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg +++ b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg @@ -21,25 +21,25 @@ - - + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + @@ -51,33 +51,15 @@ - - - - -3 -6 -9 -Clonal Size + + + + +3 +6 +9 +Clonal Size Clonal Bias - -Group - - - - - -3 -4 -5 -9 -13 - -cloneSize - - -Hyperexpanded (0.1 < X <= 1) -Large (0.01 < X <= 0.1) clonalBias_plot diff --git a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg index f7cb4e13..003cf42d 100644 --- a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg +++ b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg @@ -21,645 +21,645 @@ - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - + + - - -P19 + + +P19 - - + + - - -P20 + + +P20 - - + + - - -P17 + + +P17 - - + + - - -P18 + + +P18 - - - - - --4 -0 -4 -8 - - - - - --4 -0 -4 -8 + + + + + +-4 +0 +4 +8 + + + + + +-4 +0 +4 +8 -5 0 @@ -674,44 +674,8 @@ -Dimension 1 +Dimension 1 Dimension 2 - -Active Identity - - - - - - - - - - - - - - - - - -1 -2 -3 -4 -5 -6 -7 -8 -9 -10 -11 -12 -13 -14 -15 -16 -17 clonalOverlay_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg index f9433f2f..342246d2 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h0-p1-plot.svg @@ -21,18 +21,18 @@ - - + + - - - - - - - - + + + + + + + + @@ -44,29 +44,17 @@ - - - - - -0 -2000 -4000 -6000 -Number of individuals + + + + + +0 +2000 +4000 +6000 +Number of individuals Species diversity - - - -Rarefaction -Extrapolation - - - - - -P17B -P17L clonalclonalRarefaction_h0_p1_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg index 8fc4bd97..d9653524 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h1-p2-plot.svg @@ -21,18 +21,18 @@ - - + + - - - - - - - - + + + + + + + + @@ -46,27 +46,15 @@ - - - - -0 -1000 -2000 -Number of individuals + + + + +0 +1000 +2000 +Number of individuals Sample coverage - - - -Rarefaction -Extrapolation - - - - - -P18B -P18L clonalclonalRarefaction_h1_p2_plot diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg index 2e6f838b..33091eda 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg @@ -21,18 +21,18 @@ - - + + - - - - - - - - + + + + + + + + @@ -44,29 +44,17 @@ - - - - - -0.0 -0.2 -0.4 -0.6 -Sample coverage + + + + + +0.0 +0.2 +0.4 +0.6 +Sample coverage Species diversity - - - -Rarefaction -Extrapolation - - - - - -P19B -P19L clonalclonalRarefaction_h2_p3_plot diff --git a/tests/testthat/test-StartractDiversity.R b/tests/testthat/test-StartractDiversity.R index b42673a0..208ad13f 100644 --- a/tests/testthat/test-StartractDiversity.R +++ b/tests/testthat/test-StartractDiversity.R @@ -7,12 +7,11 @@ test_that("StartracDiversity works", { test_obj$Patient <- substr(test_obj$orig.ident,1,3) test_obj$Type <- substr(test_obj$orig.ident,4,4) - #Ridiculous ggplot warning system can't suppress any internal warnings about NAs expect_doppelganger( "StartracDiversity_plot", - suppressWarnings(print(StartracDiversity(test_obj, - type = "Type", - group.by = "Patient"))) + StartracDiversity(test_obj, + type = "Type", + group.by = "Patient") ) expect_equal( @@ -21,6 +20,6 @@ test_that("StartracDiversity works", { group.by = "Patient", exportTable = TRUE), getdata("seuratFunctions", "StartracDiversity_exportTable"), - tolerance = 1e-6 + tolerance = 1e-4 ) }) diff --git a/tests/testthat/test-clonalBias.R b/tests/testthat/test-clonalBias.R index 5be4efc4..27d734cc 100644 --- a/tests/testthat/test-clonalBias.R +++ b/tests/testthat/test-clonalBias.R @@ -15,7 +15,8 @@ test_that("clonalBias works", { split.by = "Patient", group.by = "seurat_clusters", n.boots = 1, - min.expand = 2) + min.expand = 2) + + guides(fill = "none", size = "none") ) set.seed(42) expect_equal( diff --git a/tests/testthat/test-clonalOverlay.R b/tests/testthat/test-clonalOverlay.R index f04f998f..b341a7e4 100644 --- a/tests/testthat/test-clonalOverlay.R +++ b/tests/testthat/test-clonalOverlay.R @@ -13,7 +13,8 @@ test_that("clonalOverlay works", { reduction = "umap", freq.cutpoint = 1, bins = 10, - facet.by = "Patient") + facet.by = "Patient") + + guides(color = "none") ) }) diff --git a/tests/testthat/test-clonalRarefaction.R b/tests/testthat/test-clonalRarefaction.R index 02fb25dc..31848ccb 100644 --- a/tests/testthat/test-clonalRarefaction.R +++ b/tests/testthat/test-clonalRarefaction.R @@ -11,7 +11,8 @@ test_that("clonalRarefaction works", { clonalRarefaction(combined[1:2], plot.type = 1, hill.numbers = 0, - n.boots = 1) + n.boots = 1) + + guides(color = "none", shape = "none", linetype = "none") ) trial1 <- clonalRarefaction(combined[1:2], @@ -30,7 +31,8 @@ test_that("clonalRarefaction works", { clonalRarefaction(combined[3:4], plot.type = 2, hill.numbers = 1, - n.boots = 1) + n.boots = 1) + + guides(color = "none", shape = "none", linetype = "none") ) trial2 <- clonalRarefaction(combined[3:4], @@ -49,7 +51,8 @@ test_that("clonalRarefaction works", { clonalRarefaction(combined[5:6], plot.type = 3, hill.numbers = 2, - n.boots = 1) + n.boots = 1) + + guides(color = "none", shape = "none", linetype = "none") ) diff --git a/tests/testthat/test-clonalSizeDistribution.R b/tests/testthat/test-clonalSizeDistribution.R index da76682d..da6b42e2 100644 --- a/tests/testthat/test-clonalSizeDistribution.R +++ b/tests/testthat/test-clonalSizeDistribution.R @@ -3,7 +3,7 @@ test_that("clonalSizeDistribution works", { combined <- getCombined() - + set.seed(42) expect_doppelganger( "clonalSizeDistribution_plot", clonalSizeDistribution(combined, From 9bd94866ec5a77bd9dfd11df26bd769e3d45df9b Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 8 Nov 2023 09:59:00 -0600 Subject: [PATCH 133/142] Update DESCRIPTION --- DESCRIPTION | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DESCRIPTION b/DESCRIPTION index 504c0cbf..67b01271 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -29,7 +29,7 @@ Imports: reshape2, rjson, rlang, - Russ, + rqss, S4Vectors, SeuratObject, SingleCellExperiment, From 3adf21d5f19ef9e9ff0976edcda5a67d42c2cfba Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 8 Nov 2023 12:07:54 -0600 Subject: [PATCH 134/142] importing ggraph --- DESCRIPTION | 2 +- NAMESPACE | 1 + R/clonalBias.R | 2 +- R/global.R | 1 + tests/testthat/test-clonalNetwork.R | 2 +- 5 files changed, 5 insertions(+), 3 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 67b01271..bca66487 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -25,11 +25,11 @@ Imports: iNEXT, methods, plyr, + quantreg, Rcpp, reshape2, rjson, rlang, - rqss, S4Vectors, SeuratObject, SingleCellExperiment, diff --git a/NAMESPACE b/NAMESPACE index 178ba6d5..b211c862 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -86,6 +86,7 @@ importFrom(igraph,set_vertex_attr) importFrom(methods,slot) importFrom(plyr,join) importFrom(plyr,llply) +importFrom(quantreg,rqss) importFrom(reshape2,dcast) importFrom(reshape2,melt) importFrom(rjson,fromJSON) diff --git a/R/clonalBias.R b/R/clonalBias.R index af33142c..758cb07d 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -42,7 +42,7 @@ #' @param exportTable Returns the data frame used for forming the graph. #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 -#' @importClassesFrom quantreg rqss +#' @importFrom quantreg rqss #' @importFrom stringr str_sort #' @export #' @concept SC_Functions diff --git a/R/global.R b/R/global.R index a4de2616..198815ff 100644 --- a/R/global.R +++ b/R/global.R @@ -42,5 +42,6 @@ utils::globalVariables ("group.by") utils::globalVariables ("group") utils::globalVariables ("chain2_aa") + utils::globalVariables ("dotSize") invisible () } diff --git a/tests/testthat/test-clonalNetwork.R b/tests/testthat/test-clonalNetwork.R index f392e46f..a9b188ac 100644 --- a/tests/testthat/test-clonalNetwork.R +++ b/tests/testthat/test-clonalNetwork.R @@ -3,7 +3,7 @@ # library(ggraph) # Qile: I believe libraries shouldn't need to be loaded in scripts? Correct me if im wrong. test_that("clonalNetwork works", { - + library(ggraph) data("scRep_example") test_obj <- combineExpression(getCombined(), scRep_example) From daba00853803bd8d23d32b5ccbfc13001968b1cd Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Thu, 9 Nov 2023 03:24:21 -0800 Subject: [PATCH 135/142] naive faulty implementation of parseTCR --- R/RcppExports.R | 8 +- R/combineContigs.R | 8 +- R/utils.R | 26 +++--- src/RcppExports.cpp | 26 +++--- src/constructConDfAndparseTCR.cpp | 133 +++++++++++++++++++++++++++ src/parseTCR.cpp | 60 ------------ src/scRepHelper.h | 23 ++++- tests/testthat/test-combineContigs.R | 7 +- tests/testthat/test-utils.R | 8 +- 9 files changed, 199 insertions(+), 100 deletions(-) create mode 100644 src/constructConDfAndparseTCR.cpp delete mode 100644 src/parseTCR.cpp diff --git a/R/RcppExports.R b/R/RcppExports.R index 90f25dca..811b5db3 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -5,6 +5,10 @@ rcppGetAaKmerPercent <- function(seqs, motifs, k) { .Call(`_scRepertoire_rcppGetAaKmerPercent`, seqs, motifs, k) } +rcppConstructConDfAndParseTCR <- function(data2, uniqueData2Barcodes) { + .Call(`_scRepertoire_rcppConstructConDfAndParseTCR`, data2, uniqueData2Barcodes) +} + rcppGenerateUniqueNtMotifs <- function(k) { .Call(`_scRepertoire_rcppGenerateUniqueNtMotifs`, k) } @@ -13,7 +17,3 @@ rcppGetNtKmerPercent <- function(seqs, k) { .Call(`_scRepertoire_rcppGetNtKmerPercent`, seqs, k) } -rcppConstructBarcodeIndex <- function(conDfBarcodes, data2Barcodes) { - .Call(`_scRepertoire_rcppConstructBarcodeIndex`, conDfBarcodes, data2Barcodes) -} - diff --git a/R/combineContigs.R b/R/combineContigs.R index 06f22e55..9b7edc9c 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -89,17 +89,13 @@ combineTCR <- function(input.data, for (i in seq_along(out)) { data2 <- out[[i]] data2 <- .makeGenes(cellType = "T", data2) - unique_df <- unique(data2$barcode) # could potentially display % here - Con.df <- data.frame(matrix(NA, length(unique_df), 7)) - colnames(Con.df) <- c("barcode",tcr1_lines, tcr2_lines) - Con.df$barcode <- unique_df - Con.df <- .parseTCR(Con.df, unique_df, data2) # from profvis, this takes the most runtime + Con.df <- .constructConDfAndParseTCR(data2) Con.df <- .assignCT(cellType = "T", Con.df) Con.df[Con.df == "NA_NA" | Con.df == "NA;NA_NA;NA"] <- NA data3 <- merge(data2[,-which(names(data2) %in% c("TCR1","TCR2"))], Con.df, by = "barcode") if (!is.null(samples) & !is.null(ID)) { - data3<-data3[,c("barcode","sample","ID",tcr1_lines,tcr2_lines, + data3<-data3[, c("barcode","sample","ID",tcr1_lines,tcr2_lines, CT_lines)] } else if (!is.null(samples) & is.null(ID)) { data3<-data3[,c("barcode","sample",tcr1_lines,tcr2_lines, diff --git a/R/utils.R b/R/utils.R index 3034e6c8..ce9f1d54 100644 --- a/R/utils.R +++ b/R/utils.R @@ -277,22 +277,16 @@ is_seurat_or_se_object <- function(obj) { return(x) } -# Assigning positions for TCR contig data -#' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding, Qile Yang -#' @keywords internal +# to be removed .parseTCR <- function(Con.df, unique_df, data2) { - tcr1_index <- 2 tcr2_index <- 5 - data2_chain_index <- 6 - data2 <- data2 %>% arrange(., chain, cdr3_nt) - barcodeIndex <- rcppConstructBarcodeIndex(Con.df[, 1], data2[[1]]) - for (y in seq_along(unique_df)){ - location.i <- barcodeIndex[[y]] + barcode.i <- Con.df$barcode[y] + location.i <- which(barcode.i == data2$barcode) for (z in seq_along(location.i)) { - where.chain <- data2[location.i[z], data2_chain_index] + where.chain <- data2[location.i[z],"chain"] if (where.chain == "TRA" || where.chain == "TRG") { if (is.na(Con.df[y, tcr1_index])) { @@ -320,6 +314,16 @@ is_seurat_or_se_object <- function(obj) { Con.df } +# Assigning positions for TCR contig data, returning Con.df with the parsed TCR data +#' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding, Qile Yang +#' @keywords internal +.constructConDfAndParseTCR <- function(data2) { + rcppConstructConDfAndParseTCR( + data2 %>% arrange(., chain, cdr3_nt), + unique(data2$barcode) + ) +} + #Assigning positions for BCR contig data #Now assumes lambda over kappa in the context of only 2 light chains #' @author Gloria Kraus, Nick Bormann, Nick Borcherding @@ -327,7 +331,7 @@ is_seurat_or_se_object <- function(obj) { .parseBCR <- function (Con.df, unique_df, data2) { for (y in seq_along(unique_df)) { barcode.i <- Con.df$barcode[y] - location.i <- which(barcode.i == data2$barcode) + location.i <- which(barcode.i == data2$barcode) # TODO: test, then use rcppConstructBarcodeIndex for (z in seq_along(location.i)) { where.chain <- data2[location.i[z],"chain"] diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 6158ed0d..dec866ec 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -23,6 +23,18 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// rcppConstructConDfAndParseTCR +Rcpp::DataFrame rcppConstructConDfAndParseTCR(Rcpp::DataFrame& data2, std::vector uniqueData2Barcodes); +RcppExport SEXP _scRepertoire_rcppConstructConDfAndParseTCR(SEXP data2SEXP, SEXP uniqueData2BarcodesSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< Rcpp::DataFrame& >::type data2(data2SEXP); + Rcpp::traits::input_parameter< std::vector >::type uniqueData2Barcodes(uniqueData2BarcodesSEXP); + rcpp_result_gen = Rcpp::wrap(rcppConstructConDfAndParseTCR(data2, uniqueData2Barcodes)); + return rcpp_result_gen; +END_RCPP +} // rcppGenerateUniqueNtMotifs Rcpp::CharacterVector rcppGenerateUniqueNtMotifs(int k); RcppExport SEXP _scRepertoire_rcppGenerateUniqueNtMotifs(SEXP kSEXP) { @@ -46,24 +58,12 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } -// rcppConstructBarcodeIndex -std::vector> rcppConstructBarcodeIndex(std::vector& conDfBarcodes, std::vector& data2Barcodes); -RcppExport SEXP _scRepertoire_rcppConstructBarcodeIndex(SEXP conDfBarcodesSEXP, SEXP data2BarcodesSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< std::vector& >::type conDfBarcodes(conDfBarcodesSEXP); - Rcpp::traits::input_parameter< std::vector& >::type data2Barcodes(data2BarcodesSEXP); - rcpp_result_gen = Rcpp::wrap(rcppConstructBarcodeIndex(conDfBarcodes, data2Barcodes)); - return rcpp_result_gen; -END_RCPP -} static const R_CallMethodDef CallEntries[] = { {"_scRepertoire_rcppGetAaKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetAaKmerPercent, 3}, + {"_scRepertoire_rcppConstructConDfAndParseTCR", (DL_FUNC) &_scRepertoire_rcppConstructConDfAndParseTCR, 2}, {"_scRepertoire_rcppGenerateUniqueNtMotifs", (DL_FUNC) &_scRepertoire_rcppGenerateUniqueNtMotifs, 1}, {"_scRepertoire_rcppGetNtKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetNtKmerPercent, 2}, - {"_scRepertoire_rcppConstructBarcodeIndex", (DL_FUNC) &_scRepertoire_rcppConstructBarcodeIndex, 2}, {NULL, NULL, 0} }; diff --git a/src/constructConDfAndparseTCR.cpp b/src/constructConDfAndparseTCR.cpp new file mode 100644 index 00000000..3c9ee81f --- /dev/null +++ b/src/constructConDfAndparseTCR.cpp @@ -0,0 +1,133 @@ +// Rcpp replacement for .parseTCR +// By Qile Yang + +#include +#include +#include +#include +#include "scRepHelper.h" + +#define BarcodeIndciesMap std::unordered_map> +#define StringMatrix std::vector> + +std::vector> constructBarcodeIndex( + std::vector& conDfBarcodes, std::vector data2Barcodes +) { + std::vector> outputBarcodeIndex (conDfBarcodes.size()); + BarcodeIndciesMap data2BarcodeIndiciesMap = scRepHelper::stringToIndiciesMap(data2Barcodes); + + for (int i = 0; i < (int) conDfBarcodes.size(); i++) { + std::string& barcode = conDfBarcodes[i]; + if (data2BarcodeIndiciesMap.find(barcode) != data2BarcodeIndiciesMap.end()) { + outputBarcodeIndex[i] = data2BarcodeIndiciesMap[barcode]; + } + } + return outputBarcodeIndex; +} + +class TcrParser { +public: + // variable for the eventual output conDf + StringMatrix conDf; + + // variables for *references* to columns on data2 + Rcpp::CharacterVector data2Barcodes; + Rcpp::CharacterVector data2ChainTypes; + Rcpp::CharacterVector data2Tcr1; + Rcpp::CharacterVector data2Tcr2; + Rcpp::CharacterVector data2Cdr3; + Rcpp::CharacterVector data2Cdr3Nt; + + // variable for helper barcode index + std::vector> barcodeIndex; + + // constructor + TcrParser( + Rcpp::DataFrame& data2, std::vector& uniqueData2Barcodes + ) { + // construct conDf + conDf = scRepHelper::initStringMatrix( + 7, uniqueData2Barcodes.size(), "NA" + ); + conDf[0] = uniqueData2Barcodes; + + // set references to data2 columns + data2Barcodes = data2[0]; + data2ChainTypes = data2[5]; + data2Tcr1 = data2[19]; + data2Tcr2 = data2[20]; + data2Cdr3 = data2[12]; + data2Cdr3Nt = data2[13]; + + // barcodeIndex + barcodeIndex = constructBarcodeIndex( + uniqueData2Barcodes, Rcpp::as>(data2Barcodes) + ); + } + + // .parseTCR() equivalent + void parseTCR() { + for (int y = 0; y < (int) conDf[0].size(); y++) { + + std::vector& indicies = barcodeIndex[y]; + if (indicies.size() == 0) { + continue; + } + + for (int index : indicies) { + std::string chainType = std::string(data2ChainTypes[index]); + //Rcpp::Rcout << "data2ChainTypes[" << index << "] = " << chainType << "\n"; + if (chainType == "TRA" || chainType == "TRB") { + handleTcr1(y, index); + } else if (chainType == "TRB" || chainType == "TRD") { + handleTcr2(y, index); + } + } + } + } + + // parseTCR() helpers + void handleTcr1(int y, int data2index) { + handleTcr(y, data2index, data2Tcr1, 1, 2, 3); + } + + void handleTcr2(int y, int data2index) { + handleTcr(y, data2index, data2Tcr2, 4, 5, 6); + } + + void handleTcr( + int y, int data2index, Rcpp::CharacterVector data2tcr, int tcr, int cdr3aa, int cdr3nt + ) { + if (conDf[tcr][y] == "NA") { + conDf[tcr][y] = data2tcr[data2index]; + conDf[cdr3aa][y] = data2Cdr3[data2index]; + conDf[cdr3nt][y] = data2Cdr3Nt[data2index]; + return; + } + conDf[tcr][y] += ";" + data2tcr[data2index]; + conDf[cdr3aa][y] += ";" + data2Cdr3[data2index]; + conDf[cdr3nt][y] += ";" + data2Cdr3Nt[data2index]; + } + + // return Con.df after TCR parsing + Rcpp::DataFrame getConDf() { + return Rcpp::DataFrame::create( + Rcpp::Named("barcode") = conDf[0], + Rcpp::Named("TCR1") = conDf[1], + Rcpp::Named("cdr3_aa1") = conDf[2], + Rcpp::Named("cdr3_nt1") = conDf[3], + Rcpp::Named("TCR2") = conDf[4], + Rcpp::Named("cdr3_aa2") = conDf[5], + Rcpp::Named("cdr3_nt2") = conDf[6] + ); + } +}; + +// [[Rcpp::export]] +Rcpp::DataFrame rcppConstructConDfAndParseTCR( + Rcpp::DataFrame& data2, std::vector uniqueData2Barcodes +) { + TcrParser parser = TcrParser(data2, uniqueData2Barcodes); + parser.parseTCR(); + return parser.getConDf(); +} diff --git a/src/parseTCR.cpp b/src/parseTCR.cpp deleted file mode 100644 index 4f93915a..00000000 --- a/src/parseTCR.cpp +++ /dev/null @@ -1,60 +0,0 @@ -// Helpers for .parseTCR in utils.R -// By Qile Yang - -#include -#include -#include -#include - -#define BarcodeIndciesMap std::unordered_map> - -BarcodeIndciesMap stringToIndiciesMap(std::vector& v) { - BarcodeIndciesMap map; - for (int i = 0; i < (int) v.size(); i++) { - map[v[i]].push_back(i + 1); // i + 1 for R indexing - } - return map; -} - -// [[Rcpp::export]] -std::vector> rcppConstructBarcodeIndex( - std::vector& conDfBarcodes, std::vector& data2Barcodes -) { - std::vector> outputBarcodeIndex (conDfBarcodes.size()); - BarcodeIndciesMap data2BarcodeIndiciesMap = stringToIndiciesMap(data2Barcodes); - - for (int i = 0; i < (int) conDfBarcodes.size(); i++) { - std::string& barcode = conDfBarcodes[i]; - if (data2BarcodeIndiciesMap.find(barcode) != data2BarcodeIndiciesMap.end()) { - outputBarcodeIndex[i] = data2BarcodeIndiciesMap[barcode]; - } - } - - return outputBarcodeIndex; -} - -// this is an upcoming function to *maybe* replace the R .parseTCR function. -// // [[Rcpp::export]] -// Rcpp::Dataframe rcppParseTcr(Rcpp::Dataframe& conDf, Rcpp::Dataframe& data2) { -// Rcpp::CharacterVector conDfBarcodes = conDf[0]; // this is a reference and is equivalent to unique_df -// Rcpp::CharacterVector data2Barcodes = data2[0]; - -// if (conDfBarcodes.size() == 0) { -// return conDf; -// } - -// std::vector> barcodeIndex = constructBarcodeIndex(conDfBarcodes, data2Barcodes) - -// for (int y = 0; y < (int) conDfBarcodes.size(); y++) { -// std::vector indicies = barcodeIndex[y]; -// if (indicies.size() == 0) { -// continue; -// } - -// // for (int x = 0; x < (int) conDf.size(); x++) { -// // // unfinished -// // } -// } - -// return conDf; -// } \ No newline at end of file diff --git a/src/scRepHelper.h b/src/scRepHelper.h index be166002..b60413e1 100644 --- a/src/scRepHelper.h +++ b/src/scRepHelper.h @@ -1,5 +1,7 @@ #include #include +#include +#include class scRepHelper { public: @@ -20,4 +22,23 @@ class scRepHelper { } return converted; } -}; \ No newline at end of file + + static std::unordered_map> stringToIndiciesMap( + std::vector& v + ) { + std::unordered_map> map; + for (int i = 0; i < (int) v.size(); i++) { + map[v[i]].push_back(i); + } + return map; + } + + static std::vector> initStringMatrix( + int x, int y, std::string initVal + ) { + std::vector> stringMatrix ( + x, std::vector (y, initVal) + ); + return stringMatrix; + } +}; diff --git a/tests/testthat/test-combineContigs.R b/tests/testthat/test-combineContigs.R index 4522d524..3092b57d 100644 --- a/tests/testthat/test-combineContigs.R +++ b/tests/testthat/test-combineContigs.R @@ -24,16 +24,15 @@ test_that("combineTCR works", { filterMulti = TRUE ) - expected3 <- getdata("combineContigs", "combineTCR_list_filterMulti") - expect_identical(trial3, expected3) + expect_identical(trial3, getdata("combineContigs", "combineTCR_list_filterMulti")) trial4 <- combineTCR( input.data = lapply(contig_list[1:3], head), samples = c("P17B", "P17L", "P18B"), removeNA = TRUE ) - expected4 <- getdata("combineContigs", "combineTCR_list_removeNA") - expect_identical(trial4, expected4) + + expect_identical(trial4, getdata("combineContigs", "combineTCR_list_removeNA")) }) # TODO combineTCR (need more edge cases, different args, errors, etc.) diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index 02576a55..1be3e865 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -55,7 +55,7 @@ test_that("Modifying barcodes without ID works correctly", { test_that("Modifying barcodes with ID works correctly", { samples <- c("sample3", "sample4") ID <- c("id1", "id2") - modified_data <- modified_data <- .modifyBarcodes( + modified_data <- .modifyBarcodes( df = getdata("utils", "df_list"), samples = samples, ID = ID ) @@ -93,6 +93,12 @@ test_that(".theCall works", { }) #TODO .theCall Add custom header +# TODO .constructConDfAndParseTCR !!!! Need to use testing data from the old version + +test_that(".constructConDfAndParseTCR works", { + # TODO create testdata with the original .parseTCR and test here. also do edgecases +}) + # TODO .parseBCR # TODO lengthDF # TODO assignCT From dc1d7c74a3bf97fb5b7cd442b8cea54a628ea5ae Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Thu, 9 Nov 2023 04:54:49 -0800 Subject: [PATCH 136/142] fixed parseTCR and slightly refactored combineTCR --- R/utils.R | 37 ------------------------------- src/constructConDfAndparseTCR.cpp | 30 ++++++++----------------- 2 files changed, 9 insertions(+), 58 deletions(-) diff --git a/R/utils.R b/R/utils.R index ce9f1d54..6e785183 100644 --- a/R/utils.R +++ b/R/utils.R @@ -277,43 +277,6 @@ is_seurat_or_se_object <- function(obj) { return(x) } -# to be removed -.parseTCR <- function(Con.df, unique_df, data2) { - tcr1_index <- 2 - tcr2_index <- 5 - data2 <- data2 %>% arrange(., chain, cdr3_nt) - for (y in seq_along(unique_df)){ - barcode.i <- Con.df$barcode[y] - location.i <- which(barcode.i == data2$barcode) - for (z in seq_along(location.i)) { - where.chain <- data2[location.i[z],"chain"] - - if (where.chain == "TRA" || where.chain == "TRG") { - if (is.na(Con.df[y, tcr1_index])) { - Con.df[y,tcr1_lines] <- data2[location.i[z], data1_lines] - } else { - Con.df[y,tcr1_lines] <- paste( - Con.df[y, tcr1_lines], - data2[location.i[z],data1_lines], - sep=";" - ) - } - } else if (where.chain == "TRB" || where.chain == "TRD") { - if (is.na(Con.df[y, tcr2_index])) { - Con.df[y,tcr2_lines] <- data2[location.i[z], data2_lines] - } else { - Con.df[y,tcr2_lines] <- paste( - Con.df[y, tcr2_lines], - data2[location.i[z],data2_lines], - sep=";" - ) - } - } - } - } - Con.df -} - # Assigning positions for TCR contig data, returning Con.df with the parsed TCR data #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding, Qile Yang #' @keywords internal diff --git a/src/constructConDfAndparseTCR.cpp b/src/constructConDfAndparseTCR.cpp index 3c9ee81f..a55a8a9b 100644 --- a/src/constructConDfAndparseTCR.cpp +++ b/src/constructConDfAndparseTCR.cpp @@ -8,7 +8,6 @@ #include "scRepHelper.h" #define BarcodeIndciesMap std::unordered_map> -#define StringMatrix std::vector> std::vector> constructBarcodeIndex( std::vector& conDfBarcodes, std::vector data2Barcodes @@ -28,10 +27,9 @@ std::vector> constructBarcodeIndex( class TcrParser { public: // variable for the eventual output conDf - StringMatrix conDf; + std::vector> conDf; // variables for *references* to columns on data2 - Rcpp::CharacterVector data2Barcodes; Rcpp::CharacterVector data2ChainTypes; Rcpp::CharacterVector data2Tcr1; Rcpp::CharacterVector data2Tcr2; @@ -41,7 +39,6 @@ class TcrParser { // variable for helper barcode index std::vector> barcodeIndex; - // constructor TcrParser( Rcpp::DataFrame& data2, std::vector& uniqueData2Barcodes ) { @@ -52,7 +49,6 @@ class TcrParser { conDf[0] = uniqueData2Barcodes; // set references to data2 columns - data2Barcodes = data2[0]; data2ChainTypes = data2[5]; data2Tcr1 = data2[19]; data2Tcr2 = data2[20]; @@ -61,23 +57,15 @@ class TcrParser { // barcodeIndex barcodeIndex = constructBarcodeIndex( - uniqueData2Barcodes, Rcpp::as>(data2Barcodes) + uniqueData2Barcodes, Rcpp::as>(data2[0]) ); } - // .parseTCR() equivalent void parseTCR() { for (int y = 0; y < (int) conDf[0].size(); y++) { - - std::vector& indicies = barcodeIndex[y]; - if (indicies.size() == 0) { - continue; - } - - for (int index : indicies) { + for (int index : barcodeIndex[y]) { std::string chainType = std::string(data2ChainTypes[index]); - //Rcpp::Rcout << "data2ChainTypes[" << index << "] = " << chainType << "\n"; - if (chainType == "TRA" || chainType == "TRB") { + if (chainType == "TRA" || chainType == "TRG") { handleTcr1(y, index); } else if (chainType == "TRB" || chainType == "TRD") { handleTcr2(y, index); @@ -96,17 +84,17 @@ class TcrParser { } void handleTcr( - int y, int data2index, Rcpp::CharacterVector data2tcr, int tcr, int cdr3aa, int cdr3nt + int y, int data2index, Rcpp::CharacterVector& data2tcr, int tcr, int cdr3aa, int cdr3nt ) { if (conDf[tcr][y] == "NA") { conDf[tcr][y] = data2tcr[data2index]; conDf[cdr3aa][y] = data2Cdr3[data2index]; conDf[cdr3nt][y] = data2Cdr3Nt[data2index]; - return; + } else { + conDf[tcr][y] += ";" + data2tcr[data2index]; + conDf[cdr3aa][y] += ";" + data2Cdr3[data2index]; + conDf[cdr3nt][y] += ";" + data2Cdr3Nt[data2index]; } - conDf[tcr][y] += ";" + data2tcr[data2index]; - conDf[cdr3aa][y] += ";" + data2Cdr3[data2index]; - conDf[cdr3nt][y] += ";" + data2Cdr3Nt[data2index]; } // return Con.df after TCR parsing From 01217177a179abb45cd7e2aa672fc609721d2861 Mon Sep 17 00:00:00 2001 From: Qile0317 Date: Thu, 9 Nov 2023 07:06:00 -0800 Subject: [PATCH 137/142] completely fixed constructConDfAndParseTCR --- R/combineContigs.R | 7 +++---- R/utils.R | 5 ++--- src/constructConDfAndparseTCR.cpp | 15 ++++++++++----- tests/testthat/test-utils.R | 13 ++++++++++--- .../utils/constructConDfAndParseTCRInput.rds | Bin 0 -> 171012 bytes .../testdata/utils/expected_con_df.rds | Bin 0 -> 65582 bytes 6 files changed, 25 insertions(+), 15 deletions(-) create mode 100644 tests/testthat/testdata/utils/constructConDfAndParseTCRInput.rds create mode 100644 tests/testthat/testdata/utils/expected_con_df.rds diff --git a/R/combineContigs.R b/R/combineContigs.R index 9b7edc9c..26081e2d 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -82,20 +82,19 @@ combineTCR <- function(input.data, } } if (!is.null(samples)) { - out <- .modifyBarcodes(input.data, samples, ID) + out <- .modifyBarcodes(input.data, samples, ID) } else { out <- input.data } for (i in seq_along(out)) { - data2 <- out[[i]] - data2 <- .makeGenes(cellType = "T", data2) + data2 <- .makeGenes(cellType = "T", out[[i]]) Con.df <- .constructConDfAndParseTCR(data2) Con.df <- .assignCT(cellType = "T", Con.df) Con.df[Con.df == "NA_NA" | Con.df == "NA;NA_NA;NA"] <- NA data3 <- merge(data2[,-which(names(data2) %in% c("TCR1","TCR2"))], Con.df, by = "barcode") if (!is.null(samples) & !is.null(ID)) { - data3<-data3[, c("barcode","sample","ID",tcr1_lines,tcr2_lines, + data3 <- data3[, c("barcode", "sample", "ID", tcr1_lines, tcr2_lines, CT_lines)] } else if (!is.null(samples) & is.null(ID)) { data3<-data3[,c("barcode","sample",tcr1_lines,tcr2_lines, diff --git a/R/utils.R b/R/utils.R index 6e785183..0202e783 100644 --- a/R/utils.R +++ b/R/utils.R @@ -283,7 +283,7 @@ is_seurat_or_se_object <- function(obj) { .constructConDfAndParseTCR <- function(data2) { rcppConstructConDfAndParseTCR( data2 %>% arrange(., chain, cdr3_nt), - unique(data2$barcode) + unique(data2[[1]]) ) } @@ -400,8 +400,7 @@ is_seurat_or_se_object <- function(obj) { str_c(str_replace_na(v_gene), str_replace_na(j_gene), str_replace_na(c_gene), sep = "."), NA)) %>% mutate(TCR2 = ifelse(chain %in% c("TRB", "TRD"), str_c(str_replace_na(v_gene), str_replace_na(d_gene), str_replace_na(j_gene), str_replace_na(c_gene), sep = "."), NA)) - } - else { # assume BCR (`c("B")`) + } else if (cellType %in% c("B")) { heavy <- data2[data2$chain == "IGH",] %>% mutate(IGHct = str_c(str_replace_na(v_gene), str_replace_na(d_gene), str_replace_na(j_gene), str_replace_na(c_gene), sep = ".")) kappa <- data2[data2$chain == "IGK",] %>% diff --git a/src/constructConDfAndparseTCR.cpp b/src/constructConDfAndparseTCR.cpp index a55a8a9b..dcf8f9f2 100644 --- a/src/constructConDfAndparseTCR.cpp +++ b/src/constructConDfAndparseTCR.cpp @@ -26,7 +26,7 @@ std::vector> constructBarcodeIndex( class TcrParser { public: - // variable for the eventual output conDf + // variable for the eventual output Con.df std::vector> conDf; // variables for *references* to columns on data2 @@ -39,6 +39,8 @@ class TcrParser { // variable for helper barcode index std::vector> barcodeIndex; + // constructor: in the future if more columns are ever added its probably a much + // more general algo would be to just find the indicies of the dataframe by looking at the colnames TcrParser( Rcpp::DataFrame& data2, std::vector& uniqueData2Barcodes ) { @@ -48,19 +50,22 @@ class TcrParser { ); conDf[0] = uniqueData2Barcodes; - // set references to data2 columns + // set references to fixed data2 columns data2ChainTypes = data2[5]; - data2Tcr1 = data2[19]; - data2Tcr2 = data2[20]; data2Cdr3 = data2[12]; data2Cdr3Nt = data2[13]; - // barcodeIndex + // setting reference to the TCR columns assuming all extra columns come before + data2Tcr1 = data2[data2.size() - 2]; + data2Tcr2 = data2[data2.size() - 1]; + + // construct barcodeIndex barcodeIndex = constructBarcodeIndex( uniqueData2Barcodes, Rcpp::as>(data2[0]) ); } + // Rcpp implementation of .parseTCR() void parseTCR() { for (int y = 0; y < (int) conDf[0].size(); y++) { for (int index : barcodeIndex[y]) { diff --git a/tests/testthat/test-utils.R b/tests/testthat/test-utils.R index 1be3e865..5ddf7513 100644 --- a/tests/testthat/test-utils.R +++ b/tests/testthat/test-utils.R @@ -93,10 +93,17 @@ test_that(".theCall works", { }) #TODO .theCall Add custom header -# TODO .constructConDfAndParseTCR !!!! Need to use testing data from the old version - test_that(".constructConDfAndParseTCR works", { - # TODO create testdata with the original .parseTCR and test here. also do edgecases + expect_identical( + .constructConDfAndParseTCR( + getdata("utils", "constructConDfAndParseTCRInput") + ), + getdata("utils", "expected_con_df") + ) + +# TODO: add more cases! This is not comprehensive. +# there was a case in the past where this passed but +# the function caused a segmentation fault. }) # TODO .parseBCR diff --git a/tests/testthat/testdata/utils/constructConDfAndParseTCRInput.rds b/tests/testthat/testdata/utils/constructConDfAndParseTCRInput.rds new file mode 100644 index 0000000000000000000000000000000000000000..8860ab4bee5e247d8dd2e36f6e8a3476fe531821 GIT binary patch literal 171012 zcmeF2`#;m~AODA8n3?k&=6p)Sh)k#uax8`Bypcn67;>iA=8$7LENPV@$CX#493q<} zIizH$rAbcFkn>yR`}%zUiSI98H$S;`>&CU~dS1`x{c*ok^I(ww`z5gWS8U9zFHTPA zJldxr^t7K4)!IL;N50TvcQb9*LW=O#+a>agl&xdc@gV|%-x#XHfm?N<`P89OCD`6X zZm(5WXS&E}FX!HAlk2zPy;|@>aVWr~U0^k2#yvgT*HC0ozMcH|XM5YJ_NAXAO({zk zc4Ms7cH{nS{{8mk$rInE$dTI5uYO)nX#47taDVw)RlHH!cv|#kL(uJst+goLF0bnA zZcp9P?q==Ip^26q>es!zzWXa|$M@~L(O;W8Ph9?X9NT&FYIpb8q1yDeokKk4lRE0o zzd);BdnX?xZ&KGu-@e&x5)y)Jgc8!6Hrmo+b3_Oe2ORFxdep@}~X7B--(CUUrt#Y{t@|@u8m*5Q!Fs8vCH$Hw0;#qz6M_kZsR&xcTA%;1;YJcBN~ZXvm!2+6HWSJz2YA?_s< 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8 +++++--- src/RcppExports.cpp | 13 +++++++++++++ src/constructConDfAndParseBCR.cpp | 32 +++++++++++++++++++++++++++++++ src/scRepHelper.h | 11 +++++++++++ 5 files changed, 65 insertions(+), 3 deletions(-) create mode 100644 src/constructConDfAndParseBCR.cpp diff --git a/R/RcppExports.R b/R/RcppExports.R index 811b5db3..a5e729f8 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -5,6 +5,10 @@ rcppGetAaKmerPercent <- function(seqs, motifs, k) { .Call(`_scRepertoire_rcppGetAaKmerPercent`, seqs, motifs, k) } +rcppConstructBarcodeIndex <- function(conDfBarcodes, data2Barcodes) { + .Call(`_scRepertoire_rcppConstructBarcodeIndex`, conDfBarcodes, data2Barcodes) +} + rcppConstructConDfAndParseTCR <- function(data2, uniqueData2Barcodes) { .Call(`_scRepertoire_rcppConstructConDfAndParseTCR`, data2, uniqueData2Barcodes) } diff --git a/R/utils.R b/R/utils.R index 0202e783..e0055652 100644 --- a/R/utils.R +++ b/R/utils.R @@ -277,7 +277,9 @@ is_seurat_or_se_object <- function(obj) { return(x) } -# Assigning positions for TCR contig data, returning Con.df with the parsed TCR data +# Assigning positions for TCR contig data +# Used to be .parseTCR(Con.df, unique_df, data2) +# but now also constructs Con.df and runs the parseTCR algorithm on it, all in Rcpp #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding, Qile Yang #' @keywords internal .constructConDfAndParseTCR <- function(data2) { @@ -292,9 +294,9 @@ is_seurat_or_se_object <- function(obj) { #' @author Gloria Kraus, Nick Bormann, Nick Borcherding #' @keywords internal .parseBCR <- function (Con.df, unique_df, data2) { + barcodeIndex <- rcppConstructBarcodeIndex(unique_df, data2$barcode) for (y in seq_along(unique_df)) { - barcode.i <- Con.df$barcode[y] - location.i <- which(barcode.i == data2$barcode) # TODO: test, then use rcppConstructBarcodeIndex + location.i <- barcodeIndex[[y]] # *may* be wrong but should be fine. Test on old version first for (z in seq_along(location.i)) { where.chain <- data2[location.i[z],"chain"] diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index dec866ec..402d3cb4 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -23,6 +23,18 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// rcppConstructBarcodeIndex +std::vector> rcppConstructBarcodeIndex(std::vector& conDfBarcodes, std::vector data2Barcodes); +RcppExport SEXP _scRepertoire_rcppConstructBarcodeIndex(SEXP conDfBarcodesSEXP, SEXP data2BarcodesSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< std::vector& >::type conDfBarcodes(conDfBarcodesSEXP); + Rcpp::traits::input_parameter< std::vector >::type data2Barcodes(data2BarcodesSEXP); + rcpp_result_gen = Rcpp::wrap(rcppConstructBarcodeIndex(conDfBarcodes, data2Barcodes)); + return rcpp_result_gen; +END_RCPP +} // rcppConstructConDfAndParseTCR Rcpp::DataFrame rcppConstructConDfAndParseTCR(Rcpp::DataFrame& data2, std::vector uniqueData2Barcodes); RcppExport SEXP _scRepertoire_rcppConstructConDfAndParseTCR(SEXP data2SEXP, SEXP uniqueData2BarcodesSEXP) { @@ -61,6 +73,7 @@ END_RCPP static const R_CallMethodDef CallEntries[] = { {"_scRepertoire_rcppGetAaKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetAaKmerPercent, 3}, + {"_scRepertoire_rcppConstructBarcodeIndex", (DL_FUNC) &_scRepertoire_rcppConstructBarcodeIndex, 2}, {"_scRepertoire_rcppConstructConDfAndParseTCR", (DL_FUNC) &_scRepertoire_rcppConstructConDfAndParseTCR, 2}, {"_scRepertoire_rcppGenerateUniqueNtMotifs", (DL_FUNC) &_scRepertoire_rcppGenerateUniqueNtMotifs, 1}, {"_scRepertoire_rcppGetNtKmerPercent", (DL_FUNC) &_scRepertoire_rcppGetNtKmerPercent, 2}, diff --git a/src/constructConDfAndParseBCR.cpp b/src/constructConDfAndParseBCR.cpp new file mode 100644 index 00000000..b92703e3 --- /dev/null +++ b/src/constructConDfAndParseBCR.cpp @@ -0,0 +1,32 @@ +// Upcoming Rcpp replacement for .parseBCR +// By Qile Yang + +#include +#include +#include +#include "scRepHelper.h" + +#define BarcodeIndciesMap std::unordered_map> + +// Temporary function to improve theoretical time complexity of the R function +// remove when the script is done +// [[Rcpp::export]] +std::vector> rcppConstructBarcodeIndex( + std::vector& conDfBarcodes, std::vector data2Barcodes +) { + std::vector> outputBarcodeIndex (conDfBarcodes.size()); + BarcodeIndciesMap data2BarcodeIndiciesMap = scRepHelper::stringToRIndiciesMap(data2Barcodes); + + for (int i = 0; i < (int) conDfBarcodes.size(); i++) { + std::string& barcode = conDfBarcodes[i]; + if (data2BarcodeIndiciesMap.find(barcode) != data2BarcodeIndiciesMap.end()) { + outputBarcodeIndex[i] = data2BarcodeIndiciesMap[barcode]; + } + } + return outputBarcodeIndex; +} + +// class BcrParser { +// public: + +// }; diff --git a/src/scRepHelper.h b/src/scRepHelper.h index b60413e1..b3bef47c 100644 --- a/src/scRepHelper.h +++ b/src/scRepHelper.h @@ -33,6 +33,17 @@ class scRepHelper { return map; } + // remove when constructConDfAndParseBCR is done + static std::unordered_map> stringToRIndiciesMap( + std::vector& v + ) { + std::unordered_map> map; + for (int i = 0; i < (int) v.size(); i++) { + map[v[i]].push_back(i + 1); + } + return map; + } + static std::vector> initStringMatrix( int x, int y, std::string initVal ) { From 2e8b888f3d36875cbdb27d379270f11cee186c5d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sat, 11 Nov 2023 07:27:07 -0600 Subject: [PATCH 139/142] Added Immcantation support --- R/loadContigs.R | 17 ++++++++++++++++- man/loadContigs.Rd | 1 + vignettes/articles/Loading.Rmd | 1 + 3 files changed, 18 insertions(+), 1 deletion(-) diff --git a/R/loadContigs.R b/R/loadContigs.R index 0de96619..2e826a52 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -10,6 +10,7 @@ #' \item 10X = "filtered_contig_annotation.csv" #' \item AIRR = "airr_rearrangement.tsv" #' \item BD = "Contigs_AIRR.tsv" +#' \item Immcantation = "data.tsv" #' \item JSON = ".json" #' \item MiXCR = "clones.tsv" #' \item Omniscope = ".csv" @@ -42,6 +43,7 @@ loadContigs <- function(input, format.list <- list("WAT3R" = "barcode_results.csv", "10X" = "filtered_contig_annotation.csv", "AIRR" = "airr_rearrangement.tsv", + "Immcantation" = "_data.tsv", "MiXCR" = "clones.tsv", "JSON" = ".json", "TRUST4" = "barcode_report.tsv", @@ -73,7 +75,7 @@ loadContigs <- function(input, "BD" = .parseBD, "WAT3R" = .parseWAT3R, "Omniscope" = .parseOmniscope, - + "Immcantation" = .parseImmcantation, stop("Invalid format provided")) df <- loadFunc(df) @@ -214,3 +216,16 @@ loadContigs <- function(input, } return(df) } + +#' @importFrom stringr str_split +.parseImmcantation<- function(df) { + for (i in seq_along(df)) { + df[[i]] <- do.call(rbind, df[[i]]) + df[[i]][df[[i]] == ""] <- NA + df[[i]] <- as.data.frame(df[[i]]) + df[[i]] <- df[[i]][,c("sequence_id", "locus", "consensus_count", "v_call", "d_call", "j_call", "c_call", "cdr3", "cdr3_aa")] + colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") + df[[i]] <- str_split(df[[i]], "_", simplify = TRUE)[,1] + } + return(df) +} diff --git a/man/loadContigs.Rd b/man/loadContigs.Rd index 6c8494b3..00ed25a7 100644 --- a/man/loadContigs.Rd +++ b/man/loadContigs.Rd @@ -27,6 +27,7 @@ The files that this function parses includes: \item 10X = "filtered_contig_annotation.csv" \item AIRR = "airr_rearrangement.tsv" \item BD = "Contigs_AIRR.tsv" + \item Immcantation = "data.tsv" \item JSON = ".json" \item MiXCR = "clones.tsv" \item Omniscope = ".csv" diff --git a/vignettes/articles/Loading.Rmd b/vignettes/articles/Loading.Rmd index 94d98f85..a6f14458 100644 --- a/vignettes/articles/Loading.Rmd +++ b/vignettes/articles/Loading.Rmd @@ -61,6 +61,7 @@ Beyond the default 10x Genomic Cell Ranger pipeline outputs, scRepertoire suppor * AIRR * BD Rhapsody Multiomic Immune Profiling +* [Immcantation](https://immcantation.readthedocs.io/en/stable/index.html) * JSON-formatted contig data * [MiXCR](https://pubmed.ncbi.nlm.nih.gov/25924071/) * [Omniscope](https://www.omniscope.ai/) From cd35b21376c60436c15542a2c649b9b7a8b7351e Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 13 Nov 2023 10:04:16 -0600 Subject: [PATCH 140/142] Delete .github/workflows/.codecov.yml --- .github/workflows/.codecov.yml | 4 ---- 1 file changed, 4 deletions(-) delete mode 100644 .github/workflows/.codecov.yml diff --git a/.github/workflows/.codecov.yml b/.github/workflows/.codecov.yml deleted file mode 100644 index e420c7d4..00000000 --- a/.github/workflows/.codecov.yml +++ /dev/null @@ -1,4 +0,0 @@ -- name: Upload coverage reports to Codecov - uses: codecov/codecov-action@v3 - env: - CODECOV_TOKEN: ${{ secrets.CODECOV_TOKEN }} From 2b50e7804bac16fbcda8870c79aa38bbb47753db Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Thu, 16 Nov 2023 14:55:22 -0600 Subject: [PATCH 141/142] pass on linux testthat --- .../startracdiversity-plot.svg | 3 +- .../_snaps/clonalBias/clonalbias-plot.svg | 71 +++++++++--------- .../clonalOverlay/clonaloverlay-plot.svg | 12 +-- .../clonalclonalrarefaction-h2-p3-plot.svg | 2 +- .../clonalsizedistribution-plot.svg | 22 +++--- .../StartracDiversity_exportTable.rds | Bin 738 -> 729 bytes 6 files changed, 56 insertions(+), 54 deletions(-) diff --git a/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg b/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg index af181bdd..15010d3b 100644 --- a/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg +++ b/tests/testthat/_snaps/StartractDiversity/startracdiversity-plot.svg @@ -176,6 +176,7 @@ + @@ -199,7 +200,6 @@ - @@ -217,6 +217,7 @@ + diff --git a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg index 9d7c6221..95c56b69 100644 --- a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg +++ b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg @@ -21,45 +21,46 @@ - - + + - - - - - - - - - - - - - - - + + + + + + + + + + + + + + - -0.25 -0.50 -0.75 -1.00 - - - - - - - - -3 -6 -9 -Clonal Size + +0.2 +0.4 +0.6 +0.8 +1.0 + + + + + + + + + +3 +6 +9 +Clonal Size Clonal Bias -clonalBias_plot +clonalBias_plot diff --git a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg index 003cf42d..853e7b3e 100644 --- a/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg +++ b/tests/testthat/_snaps/clonalOverlay/clonaloverlay-plot.svg @@ -145,7 +145,7 @@ - + @@ -314,13 +314,13 @@ - + - + @@ -402,7 +402,7 @@ - + @@ -588,9 +588,9 @@ - + - + diff --git a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg index 33091eda..981f0138 100644 --- a/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg +++ b/tests/testthat/_snaps/clonalRarefaction/clonalclonalrarefaction-h2-p3-plot.svg @@ -29,7 +29,7 @@ - + diff --git a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg index cb2025f6..00fedb5c 100644 --- a/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg +++ b/tests/testthat/_snaps/clonalSizeDistribution/clonalsizedistribution-plot.svg @@ -38,19 +38,19 @@ - - - + + + - - - - - - + + + + + + @@ -80,14 +80,14 @@ - + -0.1 0.0 0.1 0.2 0.3 -0.4 +0.4 0.5 clonalSizeDistribution_plot diff --git a/tests/testthat/testdata/seuratFunctions/StartracDiversity_exportTable.rds b/tests/testthat/testdata/seuratFunctions/StartracDiversity_exportTable.rds index 91a5d0448927cb4d60602fa116032868f402f6ae..e815a1e047f96255cbdc3cb622d3e0ed7f103c70 100644 GIT binary patch literal 729 zcmV;~0w(<*iwFP!000001MQdDZWA#Sh9_-iqE=k6K1L&-&C4~2Yc4?Ib_AgUbyM03 zo*uBH9LKJ*brT9OQk4JL<2nClpK+XzhfUM$H^aeRv)@M9v>)ETJ@T>bMGE5QY5crt zhRs2`@g2C`m40_+*jb5Y=VV>;15kk~NCm496;_4T-9T4ypmVYZCM(Dav!bjxE6K`c z<^CJ|`wiZxMf32YX%2qHYnvWzSUu#iEZpagN1v8EJ>`)mXw~$q_6wwy1tY zARud89EDpH`Ku$0gAB7Z98e6wvJ@-s+Ja^S_EwmiR{PGwZ?P(WW&?j7%M}C|C>ABe zB3rs|ZR>K~Jgu#RnGG_I$p{1v&dEmWTQ17&Uy$KmYM6JlXZ`8qa{kt|+IT!YzrL6U zPfq)9=hN3G*Oyn5>AY=t`t>;Lt7(6xdh+9dM5hOGDBshX(C$H!TlA(NP?Pht@Qa4MZp1PDJ z|EcLx5>!WB%7JRAODXau)};)ncDlrj>ZVK3Xe(-T6*c;b8bd`*rlKaOs0k}-qKcZh zq$Vk;$(Gdl=W|9)zNDs5QnM+k8A@u#lA5WaX0E7NDr&YBHM@$MeMQZoq83w83zXD? zCACmVEnHHIl+wxG@n+Q4RMZwKYKs-MrHa~eMQx>`wzi_SuA;U+%Adah L3og2mnGyg1%hPC| literal 738 zcmV<80v-JyiwFP!000001MQdFZWA#OhPO1kQ7bMul*j1GbMoOD#5ETnal3+0fqGKf z3Z5RYR~gqXZJN>sDZole|FOM0^V$Ep&imb_X||imcKmLmY}$8kU+?=EJx@XWJc*xI z&7|2$1K)t#Tf{yQDIf6@3wRmw{%V(fyoN8!mKDO&PuYf zS-JlO{{01B@uGQn(KI_h;tf!_22tmB@ zh)gWuvIE5iLxc{Du&2uos;}GGmfiD@7&A?@5wry2d|*fJ+NG$jhcNDTeCmCyo0iT7 z^oGPCi+6=VOaPD3JH*i-Ty&6$rgALgk$kYXroA!AI;~$KA|eowHO@r2f}@wt<*mlO zgOML*8@j`!l^uH+XT~sF!vVz*%=Y1z)!gHr+4z7acrnJ(b%RzK{bGOlqgM6&vuI5a zV4!F~h=w9wd9{|;XM1bYQ-Pfm6L=}=SOyu#WCQ|-c*`H!^e2x}!=j@-9*$;bi`Svm z#)J9E#pxn=d^CJHnZG=|I6I%s7j2WH&j(qb&xd2xvu~%v)eIjFhcnHfjk_O*^P&GV zkIAnL-A3QVjI9Y&izHqOHV0=e4yl&;@B-40M-meGK#f z*M=DA7BdBtvYH%@ML-Ty8Tm{%g;YpB*i9jol23P2NX6tM-V{*GiX?pjOnS9H<3#DMj9}x|9L6oGvk=R?{VDv=ueFiW+@IjiI6@Q&AIC z)Pxl^QAM3!z>%7yq$XQZ=l_c%HTja7LP^c0q-H3o8B1!Wiki8iW~r#zR@Ce&YW5X1 zhl*NEMJ-TL3zpPECADx#EmBg8Evdzo)Z$C(e0UqFt*NLjRMZwLYD*Qh<%-%$MQv?G UZCyoeeU#sS0(#&2&~g$00Om<-wEzGB From 59b95dd30273516570f70a548a95ba3554218502 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Fri, 17 Nov 2023 05:34:13 -0600 Subject: [PATCH 142/142] remove clonalBias plot --- NEWS.md | 2 +- .../_snaps/clonalBias/clonalbias-plot.svg | 66 ------------------- tests/testthat/test-clonalBias.R | 12 +--- 3 files changed, 2 insertions(+), 78 deletions(-) delete mode 100644 tests/testthat/_snaps/clonalBias/clonalbias-plot.svg diff --git a/NEWS.md b/NEWS.md index 206ddf96..53550b56 100644 --- a/NEWS.md +++ b/NEWS.md @@ -30,7 +30,7 @@ * Universalized underlying function language and intermediate variables * License change to MIT * **group.by** and **split.by** have been consolidated into single **group.by** parameter -* Added support for .json, Omniscope, and MiXCR formats for ```loadContigs()``` +* Added support for Immcantation pipeline, .json, Omniscope, and MiXCR formats for ```loadContigs()``` * Made GitHub.io website for support/vignettes/FAQ * Restructured NEWS Tracking * Added testhat for all exported and internal functions diff --git a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg b/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg deleted file mode 100644 index 95c56b69..00000000 --- a/tests/testthat/_snaps/clonalBias/clonalbias-plot.svg +++ /dev/null @@ -1,66 +0,0 @@ - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -0.2 -0.4 -0.6 -0.8 -1.0 - - - - - - - - - -3 -6 -9 -Clonal Size -Clonal Bias -clonalBias_plot - - diff --git a/tests/testthat/test-clonalBias.R b/tests/testthat/test-clonalBias.R index 27d734cc..4a18fcb6 100644 --- a/tests/testthat/test-clonalBias.R +++ b/tests/testthat/test-clonalBias.R @@ -7,17 +7,7 @@ test_that("clonalBias works", { test_obj$Patient <- substr(test_obj$orig.ident,1,3) test_obj$Type <- substr(test_obj$orig.ident,4,4) - set.seed(42) - expect_doppelganger( # Warning: Smoothing formula not specified. 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zHbZjo7guCal1S`?xB?~QUC78Y;H$O%bcw{Ta+7pwrYqbRh;l6KeyZi@7et~}8d#{v z3O8pC-ezi%(}p+zhd}S59bQwERrQ|X8V)qv zZuv$TS9p+1;qfu>_0`(XuyG$g4ykWZ)AM^Z_dV8U)G2;EQ`uDw>L9%LKWr96WPufG zrrg-P$z>!8);o#l9(iv>sv3Mq7|bw6en+0jM3z*r-)CS1f!^Zd>_v|(X_Hd@1q5y~ zId$?N!&(sdw?Yq1T1l~`dGhm z4_KCv1=9xuyYc#d7B})*7CNwK*@|8P0O&;zBHPL=-t7Wk8eHZ#f>_`F?ph)~f!>x$ z(Ngv361c{QYE9Rx$z8;u!XaA^swjw$LP$mOR!r}RlQ)pE7KvUWyCf7?ypa(_{=xT< z*#+c(3;7Q*kPV&ce@gl&+n3jUQ*gG=y5oCrJYVcPQ8##WH`Qxj7` z!AF>*il2gHWXP8RM{R9%YF=U`9z+61+-01O+d;Y#@WsGHE7FqaFvyTwGb$;F1WUh$ zY46PbVa{DhqJV#`hy;14wN;-qz=Md|SL&E9p`H%pnC?c>{bcY^$Yo=BY7{9++7Sn=B%ZrDjbA%2q#{?pYm(yUQ;4*x$cRbUeU From 2f37b1a4c9d2d77c7b7519ed33b16cf6b168021c Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 16 Oct 2023 09:44:04 -0500 Subject: [PATCH 092/142] Delete inferCDR.R --- R/inferCDR.R | 153 --------------------------------------------------- 1 file changed, 153 deletions(-) delete mode 100644 R/inferCDR.R diff --git a/R/inferCDR.R b/R/inferCDR.R deleted file mode 100644 index 911384b9..00000000 --- a/R/inferCDR.R +++ /dev/null @@ -1,153 +0,0 @@ -#' Add portions on the CDR loop based on Vgene usage -#' -#' This function will use the Vgene to add the amino acid sequence -#' of the CDR loop for given clones. For example, recovering the -#' CDR1 and CDR2 sequences, will produce a string with -#' CDR1-CDR2-CDR3 (ex: "SGH.......RS-YFS....ETQ-CASSLTDRTYEQYF"). -#' Sequences are derived from IMGT-based nomenclature. If a -#' vgene is not present or does not match IMGT, unrecoverable -#' regions will be denoted with "!" (ex: "!-!-CASSLTDRTYEQYF"). -#' -#' @examples -#' #Making combined contig data -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' TRB_CDR123 <- inferCDR(combined[[1]], -#' chain = "TRB", -#' regions = c("CDR1", "CDR2"), -#' species = "human") -#' -#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -#' \code{\link{combineExpression}}. -#' @param chain indicate if both or a specific chain should be used - -#' e.g. "both", "TRA", "TRG", "IGH", "IGL" -#' @param regions CDR loop regions - "FR1", "CDR1", "FR2", "CDR2", and/or "FR3" -#' @param species The species of the experiment - "human" or "mouse" -#' @importFrom stringr str_split -#' @export -#' @return vector of sequences corresponding with the additional regions - -inferCDR <- function(df, - chain = "TRB", - regions = c("CDR1", "CDR2"), - species = "human") { - - .sequence.positions[["FR3.F"]] <- c(97:103) - sco <- is_seurat_object(df) | is_se_object(df) - df <- .data.wrangle(df, NULL, "CTgene", chain) - for (x in seq_along(df)) { - df[[x]] <- .off.the.chain(df[[x]], chain, "CTaa") - df[[x]] <- .off.the.chain(df[[x]], chain, "CTgene") - } - #Get the sequence position for the regions selected - sequence.pos <- .sequence.positions[grep(paste0(regions, collapse = "|"), names(.sequence.positions))] - - dat <- bind_rows(df) - cdr3.sequences <- dat[,"CTaa"] - Vgene <- str_split(dat[,"CTgene"], "[.]", simplify = TRUE)[,1] - - #Laoding reference - protein.reference <- .load.fasta(chain, species) - - #Matching the tenx Vgene nomenclature with IMGT - Vgene <- .tenX.V(Vgene, protein.reference) - - for (i in seq_len(length(regions)-1)) { - sequence.pos[[i]] <- c(sequence.pos[[i]], "-") - } - - sequence.pos <- unname(unlist(sequence.pos)) - - #Loop through the sequences - sequence.store <- c() - for(i in seq_len(length(cdr3.sequences))) { - if (Vgene[i] %in% names(protein.reference)) { - aa_reference <- protein.reference[[Vgene[i]]] - aa_sequence <- suppressWarnings(aa_reference[as.numeric(sequence.pos)]) - aa_sequence[is.na(aa_sequence)] <- "-" - aa_sequence <- paste0(aa_sequence, collapse = "") - tmp.sequence <- paste0(aa_sequence, "-", cdr3.sequences[i]) - } else { - aa_sequence <- rep("!", length(regions)) - aa_sequence <- paste0(aa_sequence, collapse = "-") - tmp.sequence <- paste0(aa_sequence, "-", cdr3.sequences[i]) - } - sequence.store <- c(sequence.store, tmp.sequence) - } - sequence.store[sequence.store == "!-!-NA"] <- NA - return(sequence.store) -} - -#Positions of specific regions -.sequence.positions <- list(FR1.A = c(1:15), - FR1.B = c(16:26), - CDR1 = c(27:38), - FR2.C = c(39:46), - FR2.Cp = c(47:55), - CDR2 = c(56:65), - FR3.C = c(66:74), - FR3.D = c(75:84), - FR3.E = c(85:96), - FR3.F = c(97:104)) - -#' @importFrom stringr str_remove_all -.tenX.V <- function(Vgene, reference) { - Vgene <- str_remove_all(Vgene, "NA_") - #Persistent Adaptive Conversion - V.dict <- as.data.frame(table(Vgene)) - unmatched.V <- as.vector(V.dict$Vgene[V.dict$Vgene %!in% names(reference)]) - rematched.V <- unmatched.V - for(i in seq_along(unmatched.V)) { - tmp.V <- unmatched.V[i] - tmp.V <- str_sort(names(reference), numeric = TRUE)[grep(tmp.V, str_sort(names(reference), numeric = TRUE))][1] - if(is.na(tmp.V)) { - tmp.V <- unmatched.V[i] - tmp.V <- str_split(tmp.V, "-", simplify = TRUE)[,1] - tmp.V <- str_sort(names(reference), numeric = TRUE)[grep(tmp.V, str_sort(names(reference), numeric = TRUE))][1] - } - rematched.V[i] <- tmp.V - } - - #Replacing Sticky Genes - names(rematched.V) <- unmatched.V - matching_indices <- match(Vgene, names(rematched.V)) - Vgene[!is.na(matching_indices)] <- rematched.V[matching_indices[!is.na(matching_indices)]] - return(Vgene) -} - -#Pulling the reference sequences form IMGT -#' @importFrom seqinr read.fasta translate -#' @importFrom stringr str_split str_replace_all -.load.fasta <- function(chain, species) { - species <- tolower(species) - species.link <- switch(species, - "human" = "Homo_sapiens", - "mouse" = "Mus_musculus", - stop("Please select either human or mouse for species")) - - chain.link <- switch(chain, - "TRB" = "TR/TRBV", - "TRA" = "TR/TRAV", - "TRG" = "TR/TRGV", - "TRD" = "TR/TRDV", - "Heavy" = "IG/IGHV", - "Light" = c("IG/IGLV", "IG/IGKV"), - stop("Please select chain: TRB, TRA, TRG, TRD, Heavy, Light")) - - dir <- paste0("https://www.imgt.org/download/V-QUEST/IMGT_V-QUEST_reference_directory/", - species.link, "/", chain.link, ".fasta") - reference <- lapply(dir, read.fasta) - if(chain == "Light") { - reference <- c(reference[[1]], reference[[2]]) - } else { - reference <- reference[[1]] - } - names(reference) <- str_split(names(reference), "[|]", simplify = TRUE)[,2] - protein.reference <- lapply(reference, function(x) { - tmp <- seqinr::translate(x) - tmp <- str_replace_all(tmp, "X", ".") - tmp - }) - -} From 6ebe96cfca9f6c0f00ff63105d1f329827fba314 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Tue, 17 Oct 2023 12:09:08 -0500 Subject: [PATCH 093/142] MiXCR support Add MiXCR --- NEWS | 2 +- R/loadContigs.R | 19 +++++++++++++++++-- 2 files changed, 18 insertions(+), 3 deletions(-) diff --git a/NEWS b/NEWS index 41585d6f..fc0c409d 100644 --- a/NEWS +++ b/NEWS @@ -26,7 +26,7 @@ UNDERLYING CHANGES * Added features to clonalCompare to allow for highlighting sequences, relabeling clonotypes. * License change to MIT * group.by and split.by have been consolidated into single group.by parameter -* Added support for .json and Omniscope formats +* Added support for .json, Omniscope, and MiXCR formats DEPRECATED AND DEFUNCT diff --git a/R/loadContigs.R b/R/loadContigs.R index f6c0b686..cad877c8 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -11,8 +11,9 @@ #' \itemize{ #' \item 10X = "filtered_contig_annotation.csv" #' \item AIRR = "airr_rearrangement.tsv" -#' \item JSON = ".json" #' \item BD = "Contigs_AIRR.tsv" +#' \item JSON = ".json" +#' \item MiXCR = "clones.txt" #' \item Omniscope = ".csv" #' \item TRUST4 = "barcode_report.tsv" #' \item WAT3R = "barcode_results.csv" @@ -31,7 +32,7 @@ #' #' @param dir The directory in which contigs are located or a list with contig elements #' @param format The format of the single-cell contig, currently supporting: -#' "10X", "AIRR", "BD", "JSON", "Omniscope", "TRUST4", and "WAT3R" +#' "10X", "AIRR", "BD", "JSON", "MiXCR", "Omniscope", "TRUST4", and "WAT3R" #' @importFrom utils read.csv read.delim #' @importFrom rjson fromJSON #' @export @@ -43,6 +44,7 @@ loadContigs <- function(dir, format.list <- list("WAT3R" = "barcode_results.csv", "10X" = "filtered_contig_annotation.csv", "AIRR" = "airr_rearrangement.tsv", + "MiXCR" = "clones.txt", "JSON" = ".json", "TRUST4" = "barcode_report.tsv", "BD" = "Contigs_AIRR.tsv", @@ -68,6 +70,7 @@ loadContigs <- function(dir, "10X" = .parse10x, "AIRR" = .parseAIRR, "JSON" = .parseJSON, + "MiXCR" = .parseMiXCR, "TRUST4" = .parseTRUST4, "BD" = .parseBD, "WAT3R" = .parseWAT3R, @@ -202,3 +205,15 @@ loadContigs <- function(dir, } return(df) } + +.parseMiXCR <- function(df) { + for (i in seq_along(df)) { + df[[i]] <- do.call(rbind, df[[i]]) + df[[i]][df[[i]] == ""] <- NA + df[[i]] <- as.data.frame(df[[i]]) + df[[i]] <- df[[i]][,c("tagValueCELL", "readCount", "vGene", "dGene", "jGene", "cGene", "nSeqCDR3", "aaSeqCDR3")] + colnames(df[[i]]) <- c("barcode", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") + df[[i]]$chain <- substring(df[[i]]$v_gene, 1,3) + } + return(df) +} From 72fb3c88772a48544096b5a704a35ae8f5e6297d Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 18 Oct 2023 07:52:50 -0500 Subject: [PATCH 094/142] Redo of clustering --- NAMESPACE | 9 ++-- R/clusterTCR.R | 112 ----------------------------------------- R/utils.R | 103 ++++++++++++++++++++++++++++++++++++- man/clonalCluster.Rd | 56 +++++++++++++++++++++ man/clusterTCR.Rd | 47 ----------------- man/inferCDR.Rd | 42 ---------------- man/loadContigs.Rd | 5 +- vignettes/vignette.Rmd | 47 +++++++++++++---- 8 files changed, 200 insertions(+), 221 deletions(-) delete mode 100644 R/clusterTCR.R create mode 100644 man/clonalCluster.Rd delete mode 100644 man/clusterTCR.Rd delete mode 100644 man/inferCDR.Rd diff --git a/NAMESPACE b/NAMESPACE index c41eeaeb..f03298d8 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -5,6 +5,7 @@ export(addVariable) export(alluvialClones) export(clonalAbundance) export(clonalBias) +export(clonalCluster) export(clonalCompare) export(clonalDiversity) export(clonalHomeostasis) @@ -18,7 +19,6 @@ export(clonalQuant) export(clonalRarefaction) export(clonalScatter) export(clonalSizeDistribution) -export(clusterTCR) export(combineBCR) export(combineExpression) export(combineTCR) @@ -26,7 +26,6 @@ export(createHTOContigList) export(exportClones) export(getCirclize) export(highlightClones) -export(inferCDR) export(loadContigs) export(percentAA) export(percentGenes) @@ -82,6 +81,8 @@ importFrom(igraph,V) importFrom(igraph,`V<-`) importFrom(igraph,components) importFrom(igraph,graph_from_data_frame) +importFrom(igraph,graph_from_edgelist) +importFrom(igraph,set_vertex_attr) importFrom(methods,slot) importFrom(plyr,join) importFrom(plyr,llply) @@ -89,8 +90,6 @@ importFrom(reshape2,dcast) importFrom(reshape2,melt) importFrom(rjson,fromJSON) importFrom(rlang,"%||%") -importFrom(seqinr,read.fasta) -importFrom(seqinr,translate) importFrom(stats,as.dist) importFrom(stats,hclust) importFrom(stats,mad) @@ -101,9 +100,7 @@ importFrom(stats,quantile) importFrom(stats,sd) importFrom(stats,setNames) importFrom(stringdist,stringdist) -importFrom(stringdist,stringdistmatrix) importFrom(stringr,str_c) -importFrom(stringr,str_remove_all) importFrom(stringr,str_replace_all) importFrom(stringr,str_replace_na) importFrom(stringr,str_sort) diff --git a/R/clusterTCR.R b/R/clusterTCR.R deleted file mode 100644 index 88c2e917..00000000 --- a/R/clusterTCR.R +++ /dev/null @@ -1,112 +0,0 @@ -#' Clustering T cell receptors -#' -#' This function uses edit distances of either the nucleotide or amino acid -#' sequences of the CDR3 and V genes to cluster similar TCRs together. -#' The distance clustering will then be amended to the end of the list of -#' combined contigs. The cluster will appear as CHAIN.num if a unique sequence or -#' CHAIN:LD.num if clustered together.T his function will only two chains -#' recovered, multiple chains will automatically be reduced. This function -#' also underlies the combineBCR() function and therefore not needed for -#' B cells. This may take some time to calculate the distances and cluster. -#' -#' @examples -# Getting the combined contigs -#' combined <- combineTCR(contig_list, -#' samples = c("P17B", "P17L", "P18B", "P18L", -#' "P19B","P19L", "P20B", "P20L")) -#' -#' sub_combined <- clusterTCR(combined[[2]], chain = "TRA", sequence = "aa") -#' -#' @param df The product of \code{\link{combineTCR}} or \code{\link{combineExpression}}. -#' @param chain The TCR to cluster - TRA, TRB, TRG, TRD -#' @param sequence Clustering based on either "aa" or "nt" -#' @param threshold The normalized edit distance to consider. The higher the number the more -#' similarity of sequence will be used for clustering. -#' @param group.by The column header used for to group contigs. -#' @importFrom stringdist stringdist -#' @importFrom igraph graph_from_data_frame components -#' @importFrom plyr join -#' @importFrom dplyr bind_rows -#' @importFrom stringr str_split str_replace_all -#' @importFrom rlang %||% -#' @importFrom SummarizedExperiment colData<- colData -#' @importFrom stats na.omit -#' @export -#' @return List of clonotypes for individual cell barcodes - -clusterTCR <- function(df, - chain = NULL, - sequence = NULL, - threshold = 0.85, - group.by = NULL) { - output.list <- list() - dat <- .list.input.return(df, group.by) - if (inherits(x=df, what ="Seurat") | - inherits(x=df, what ="SummarizedExperiment")) { - for(y in seq_along(dat)) { - dat[[y]]$cdr3_aa1 <- str_split(dat[[y]]$CTaa, "_", simplify = TRUE)[,1] - dat[[y]]$cdr3_aa2 <- str_split(dat[[y]]$CTaa, "_", simplify = TRUE)[,2] - dat[[y]]$cdr3_nt1 <- str_split(dat[[y]]$CTnt, "_", simplify = TRUE)[,1] - dat[[y]]$cdr3_nt2 <- str_split(dat[[y]]$CTnt, "_", simplify = TRUE)[,2] - } - } - dat <- .checkList(dat) - if(chain %in% c("TRA", "TRG")) { - ref <- 1 - } else if(chain %in% c("TRB", "TRD")) { - ref <- 2 - } - ref2 <- paste0("cdr3_", sequence, ref) - bound <- bind_rows(dat) - #Should make it work as either grouped or non-grouped - if (!is.null(group.by)) { - bound <- split(bound, bound[,group.by]) - list.length <- length(bound) - } else { - bound <- list(bound) - list.length <- 1 - } - #Defining clusters by edit distance - output.list <- lapply(bound, function(x) { - cluster <- .lvCompare(x, gene = paste0("TCR", ref), chain = ref2, threshold = threshold) - cluster - }) - #Reformating output for compatibility - for (i in seq_along(output.list)) { - output.list[[i]][,1] <- str_replace_all(output.list[[i]][,1], paste0("TCR", ref), chain) - if (length(output.list) > 1) { - output.list[[i]][,1] <- paste0(names(bound)[i], ".", output.list[[i]][,1]) - } - colnames(output.list[[i]])[1] <- paste0(chain, "_cluster") - colnames(output.list[[i]])[2] <- paste0(ref2) - } - #Merging with contig info - for (i in seq_along(bound)) { - tmp <- bound[[i]] - output <- bind_rows(output.list) - tmp[,ref2] <- str_split(tmp[,ref2], ";", simplify = TRUE)[,1] - output2 <- output[output[,2] %in% tmp[,ref2],] - - tmp <- unique(suppressMessages(join(tmp, output2))) - bound[[i]] <- tmp - } - #Adding to potential single-cell object - if(inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { - PreMeta <- bind_rows(bound) - x <- colnames(PreMeta)[ncol(PreMeta)] - PreMeta <- as.data.frame(PreMeta[,x], row.names = PreMeta$barcode) - colnames(PreMeta) <- x - if (inherits(x=df, what ="Seurat")) { - col.name <- names(PreMeta) %||% colnames(PreMeta) - df[[col.name]] <- PreMeta - } else { - rownames <- rownames(colData(df)) - colData(df) <- cbind(colData(df), PreMeta[rownames,])[, union(colnames(colData(df)), colnames(PreMeta))] - rownames(colData(df)) <- rownames - } - } else { - df <- bound - } - return(df) -} - diff --git a/R/utils.R b/R/utils.R index dc94d286..d6d57c60 100644 --- a/R/utils.R +++ b/R/utils.R @@ -93,7 +93,7 @@ is_seurat_or_se_object <- function(obj) { .list.input.return <- function(df, split.by) { if (is_seurat_or_se_object(df)) { if(is.null(split.by)){ - split.by <- "cluster" + split.by <- "ident" } df <- .expression2List(df, split.by) } @@ -443,7 +443,7 @@ is_df_or_list_of_df <- function(x) { } meta <- .grabMeta(sc) if(is.null(split.by)){ - split.by <- "cluster" + split.by <- "ident" } unique <- str_sort(as.character(unique(meta[,split.by])), numeric = TRUE) df <- NULL @@ -467,3 +467,102 @@ is_df_or_list_of_df <- function(x) { } return(df) } + +# Calculates the normalized Levenshtein Distance between the contig +# nucleotide sequence. +#' @importFrom stringdist stringdist +#' @importFrom igraph graph_from_data_frame components graph_from_edgelist +#' @importFrom dplyr bind_rows +.lvCompare <- function(dictionary, gene, chain, threshold, exportGraph = FALSE) { + overlap <- NULL + out <- NULL + dictionary[dictionary == "None"] <- NA + dictionary$v.gene <- stringr::str_split(dictionary[,gene], "[.]", simplify = TRUE)[,1] + tmp <- na.omit(unique(dictionary[,c(chain, "v.gene")])) + #chunking the sequences for distance by v.gene + unique.v <- na.omit(unique(tmp$v.gene)) + + edge.list <- lapply(unique.v, function(y) { + #for(y in unique.v) { + secondary.list <- list() + filtered_df <- tmp %>% filter(v.gene == y) + filtered_df <- filtered_df[!is.na(filtered_df[,chain]),] + nucleotides <- unique(filtered_df[,chain]) + if (length(nucleotides) > 1) { + chain_col_number <- 1 + nucleotide_lengths <- nchar(nucleotides) + # Pre-allocate list + list <- vector("list", length = length(nucleotides)) + + for (i in 1:(length(nucleotides) - 1)) { + temp_list <- vector("list", length = length(nucleotides) - i) + + idx_i <- tmp[, chain_col_number] == nucleotides[i] + len_i <- nucleotide_lengths[i] + + for (j in (i + 1):length(nucleotides)) { + distance <- stringdist::stringdist(nucleotides[i], nucleotides[j], method = "lv") + distance_norm <- 1 - distance / ((len_i + nucleotide_lengths[j]) / 2) + + if (!is.na(distance_norm) & distance_norm > threshold) { + idx_j <- tmp[, chain_col_number] == nucleotides[j] + stored_positions <- idx_j & !idx_i + + if(any(stored_positions)) { + # Store this pair in the edge list that is not the same chain + temp_list[[j - i]] <- data.frame(from = which(idx_i), + to = which(stored_positions)) + } + } + } + list[[i]] <- do.call(rbind, temp_list) # Collapsing all data.frames in temp_list + } + + #Remove any NULL or 0 list elements + if(length(list) > 0) { + list <- list[-which(unlist(lapply(list, is.null)))] + list <- list[lapply(list,length)>0] + list <- bind_rows(list) %>% as.data.frame() + secondary.list[[i]] <- list + } + #Remove any NULL or 0 list elements + if(length(secondary.list) > 0) { + secondary.list <- secondary.list[-which(unlist(lapply(secondary.list, is.null)))] + secondary.list <- secondary.list[lapply(secondary.list,length)>0] + } + } + secondary.list + }) + edge.list <- bind_rows(edge.list) + if(exportGraph) { + edge.list[,1] <- tmp[,1][edge.list[,1]] + edge.list[,2] <- tmp[,1][edge.list[,2]] + graph <- graph_from_edgelist(as.matrix(edge.list)) + return(graph) + } + + if (nrow(edge.list) > 0) { + edge.list <- unique(edge.list) + g <- graph_from_data_frame(edge.list) + components <- components(g, mode = c("weak")) + out <- data.frame("cluster" = components$membership, + "filtered" = names(components$membership)) + filter <- which(table(out$cluster) > 1) + out <- subset(out, cluster %in% filter) + if(nrow(out) > 1) { + out$cluster <- paste0(gene, ":Cluster", ".", out$cluster) + out$filtered <- tmp[,1][as.numeric(out$filtered)] + uni_IG <- as.data.frame(unique(tmp[,1][tmp[,1] %!in% out$filtered])) + } + } else { + uni_IG <- as.data.frame(unique(tmp[,1])) + } + colnames(uni_IG)[1] <- "filtered" + if (nrow(uni_IG) > 0) { + uni_IG$cluster <- paste0(gene, ".", seq_len(nrow(uni_IG))) + } + output <- rbind.data.frame(out, uni_IG) + colnames(output) <- c("Cluster", "clone") + return(output) +} + diff --git a/man/clonalCluster.Rd b/man/clonalCluster.Rd new file mode 100644 index 00000000..5489856a --- /dev/null +++ b/man/clonalCluster.Rd @@ -0,0 +1,56 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/clonalCluster.R +\name{clonalCluster} +\alias{clonalCluster} +\title{Clustering adaptive receptor sequences} +\usage{ +clonalCluster( + df, + chain = "TRB", + sequence = "aa", + samples = NULL, + threshold = 0.85, + group.by = NULL, + exportGraph = FALSE +) +} +\arguments{ +\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} +or \code{\link{combineExpression}}.} + +\item{chain}{Indicate if both or a specific chain should be used - +e.g. "both", "TRA", "TRG", "IGH", "IGL".} + +\item{sequence}{Clustering based on either "aa" or "nt".} + +\item{samples}{The specific samples to isolate for visualization.} + +\item{threshold}{The normalized edit distance to consider. The higher the number the more +similarity of sequence will be used for clustering.} + +\item{group.by}{The column header used for to group contigs.} + +\item{exportGraph}{Return an igraph object of connected sequences (TRUE) or the amended +input with a new cluster-based variable (FALSE)} +} +\value{ +Either amended input with edit-distanced clusters added +or igraph object of connect sequences +} +\description{ +This function uses edit distances of either the nucleotide or amino acid +sequences of the CDR3 and V genes to cluster similar TCR/BCRs together. +As a default, the function takes the input from \code{\link{combineTCR}}, +\code{\link{combineBCR}} or \code{\link{combineExpression}} and amends a +cluster to the data frame or meta data. If **exportGraph** is set to TRUE, +the function returns an igraph object of the connected sequences. +} +\examples{ +# Getting the combined contigs +combined <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + +sub_combined <- clonalCluster(combined[[2]], chain = "TRA", sequence = "aa") + +} diff --git a/man/clusterTCR.Rd b/man/clusterTCR.Rd deleted file mode 100644 index 1b05c4f9..00000000 --- a/man/clusterTCR.Rd +++ /dev/null @@ -1,47 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/clusterTCR.R -\name{clusterTCR} -\alias{clusterTCR} -\title{Clustering T cell receptors} -\usage{ -clusterTCR( - df, - chain = NULL, - sequence = NULL, - threshold = 0.85, - group.by = NULL -) -} -\arguments{ -\item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineExpression}}.} - -\item{chain}{The TCR to cluster - TRA, TRB, TRG, TRD} - -\item{sequence}{Clustering based on either "aa" or "nt"} - -\item{threshold}{The normalized edit distance to consider. The higher the number the more -similarity of sequence will be used for clustering.} - -\item{group.by}{The column header used for to group contigs.} -} -\value{ -List of clonotypes for individual cell barcodes -} -\description{ -This function uses edit distances of either the nucleotide or amino acid -sequences of the CDR3 and V genes to cluster similar TCRs together. -The distance clustering will then be amended to the end of the list of -combined contigs. The cluster will appear as CHAIN.num if a unique sequence or -CHAIN:LD.num if clustered together.T his function will only two chains -recovered, multiple chains will automatically be reduced. This function -also underlies the combineBCR() function and therefore not needed for -B cells. This may take some time to calculate the distances and cluster. -} -\examples{ -combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) - -sub_combined <- clusterTCR(combined[[2]], chain = "TRA", sequence = "aa") - -} diff --git a/man/inferCDR.Rd b/man/inferCDR.Rd deleted file mode 100644 index 4d7486a9..00000000 --- a/man/inferCDR.Rd +++ /dev/null @@ -1,42 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/inferCDR.R -\name{inferCDR} -\alias{inferCDR} -\title{Add portions on the CDR loop based on Vgene usage} -\usage{ -inferCDR(df, chain = "TRB", regions = c("CDR1", "CDR2"), species = "human") -} -\arguments{ -\item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or -\code{\link{combineExpression}}.} - -\item{chain}{indicate if both or a specific chain should be used - -e.g. "both", "TRA", "TRG", "IGH", "IGL"} - -\item{regions}{CDR loop regions - "FR1", "CDR1", "FR2", "CDR2", and/or "FR3"} - -\item{species}{The species of the experiment - "human" or "mouse"} -} -\value{ -vector of sequences corresponding with the additional regions -} -\description{ -This function will use the Vgene to add the amino acid sequence -of the CDR loop for given clones. For example, recovering the -CDR1 and CDR2 sequences, will produce a string with -CDR1-CDR2-CDR3 (ex: "SGH.......RS-YFS....ETQ-CASSLTDRTYEQYF"). -Sequences are derived from IMGT-based nomenclature. If a -vgene is not present or does not match IMGT, unrecoverable -regions will be denoted with "!" (ex: "!-!-CASSLTDRTYEQYF"). -} -\examples{ -#Making combined contig data -combined <- combineTCR(contig_list, - samples = c("P17B", "P17L", "P18B", "P18L", - "P19B","P19L", "P20B", "P20L")) -TRB_CDR123 <- inferCDR(combined[[1]], - chain = "TRB", - regions = c("CDR1", "CDR2"), - species = "human") - -} diff --git a/man/loadContigs.Rd b/man/loadContigs.Rd index 075eaac3..65ddd472 100644 --- a/man/loadContigs.Rd +++ b/man/loadContigs.Rd @@ -10,7 +10,7 @@ loadContigs(dir, format = "10X") \item{dir}{The directory in which contigs are located or a list with contig elements} \item{format}{The format of the single-cell contig, currently supporting: -"10X", "AIRR", "BD", "JSON", "Omniscope", "TRUST4", and "WAT3R"} +"10X", "AIRR", "BD", "JSON", "MiXCR", "Omniscope", "TRUST4", and "WAT3R"} } \value{ List of contigs for further processing in scRepertoire @@ -28,8 +28,9 @@ The files that this function parses includes: \itemize{ \item 10X = "filtered_contig_annotation.csv" \item AIRR = "airr_rearrangement.tsv" - \item JSON = ".json" \item BD = "Contigs_AIRR.tsv" + \item JSON = ".json" + \item MiXCR = "clones.txt" \item Omniscope = ".csv" \item TRUST4 = "barcode_report.tsv" \item WAT3R = "barcode_results.csv" diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index d02ff6f8..c6f4634f 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -835,26 +835,53 @@ chordDiagram(circles, link.arr.type = "big.arrow") ``` -## clusterTCR +## clonalCluster -The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the T cell receptors. It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. +The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the B or T cell receptors at the level of nucleotides or amino acids. It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. Clonotype cluster by distance: -* TCRA:LD.1 [chain:] + :LD + [number] +* TCRA:Cluster.1 [chain:] + :Cluster + [number] * TCRA.123 [chain:] + [number] -LD denotes if the cluster was called using the normalized Levenshtein distance. +Cluster denotes if the cluster was called using the normalized Levenshtein distance, which takes the edit distance calculated between 2 sequences and divides that by the mean of the sequence lengths. -If performed over the number of samples, such as the list elements, **group.by** can used to calculate only the clusters on the setting of patient sample (group.by = "Patient") or tissue type (group.by = "Type"). This will add the selected group to the beginning of the cluster designation. +If performed over the number of samples, such as the list elements, **group.by** can used to calculate only the clusters on the setting of patient sample (**group.by** = "Patient") or tissue type (group.by = "Type"). This will add the selected group to the beginning of the cluster designation. ```{r} -sub_combined <- clusterTCR(combined[[2]], - chain = "TRA", - sequence = "aa", - threshold = 0.85, - group.by = NULL) +sub_combined <- clonalCluster(combined[[2]], + chain = "TRA", + sequence = "aa", + threshold = 0.85, + group.by = NULL) +``` + + +Using ```clonalCluster()```, we can also return an igraph object of all the related sequences using **exportGraph** = TRUE. The returned igraph object contains only the sequences that have at least one conenction with another sequence. The igraph can then be directly visualized (below) or used for downstream analysis (see the igraph [website](https://r.igraph.org/). + +```{r} +igraph.object <- clonalCluster(combined[c(1,2)], + chain = "TRB", + sequence = "aa", + group.by = "sample", + threshold = 0.85, + exportGraph = TRUE) + +#Setting color scheme +col_legend <- factor(igraph::V(igraph.object)$group) +col_samples <- hcl.colors(3,"inferno")[as.numeric(col_legend)] +color.legend <- factor(unique(igraph::V(igraph.object)$group)) + +#Plotting +plot( + igraph.object, + vertex.size = sqrt(igraph::V(igraph.object)$size), + vertex.label = NA, + edge.arrow.size = .25, + vertex.color = col_samples +) +legend("topleft", legend = levels(color.legend), pch = 16, col = unique(col_samples), bty = "n") ``` ## StartracDiversity From 43838996780d96b50148887e98269f3d2b249972 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Wed, 18 Oct 2023 12:07:01 -0500 Subject: [PATCH 095/142] Update combineContigs.R --- R/combineContigs.R | 96 +--------------------------------------------- 1 file changed, 2 insertions(+), 94 deletions(-) diff --git a/R/combineContigs.R b/R/combineContigs.R index 9312e08a..ed8ed0aa 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -150,7 +150,6 @@ combineTCR <- function(df, #' BCR <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv") #' combined <- combineBCR(BCR, #' samples = "Patient1", -#' ID = "Time1", #' threshold = 0.85) #' #' @param df List of filtered contig annotations or outputs from \code{\link{loadContigs}}. @@ -220,8 +219,8 @@ combineBCR <- function(df, } for(i in seq_along(final)) { if(call.related.clones) { - final[[i]]<-merge(final[[i]],IGH,by.x="cdr3_nt1",by.y="IG",all.x=TRUE) - final[[i]]<-merge(final[[i]],IGLC,by.x="cdr3_nt2",by.y="IG",all.x=TRUE) + final[[i]]<-merge(final[[i]],IGH,by.x="cdr3_nt1",by.y="clone",all.x=TRUE) + final[[i]]<-merge(final[[i]],IGLC,by.x="cdr3_nt2",by.y="clone",all.x=TRUE) num <- ncol(final[[i]]) final[[i]][,"CTstrict"] <- paste0(final[[i]][,num-1],".", final[[i]][,"vgene1"],"_",final[[i]][,num],".",final[[i]][,"vgene2"]) @@ -263,94 +262,3 @@ combineBCR <- function(df, return(final) } -# Calculates the normalized Levenshtein Distance between the contig -# nucleotide sequence. -#' @importFrom stringdist stringdistmatrix -#' @importFrom igraph graph_from_data_frame components -#' @importFrom dplyr bind_rows -.lvCompare <- function(dictionary, gene, chain, threshold) { - overlap <- NULL - out <- NULL - dictionary[dictionary == "None"] <- NA - dictionary$v.gene <- stringr::str_split(dictionary[,gene], "[.]", simplify = TRUE)[,1] - tmp <- na.omit(unique(dictionary[,c(chain, "v.gene")])) - #chunking the sequences for distance by v.gene - unique.v <- na.omit(unique(tmp$v.gene)) - - edge.list <- lapply(unique.v, function(y) { - #for(y in unique.v) { - secondary.list <- list() - filtered_df <- tmp %>% filter(v.gene == y) - filtered_df <- filtered_df[!is.na(filtered_df[,chain]),] - nucleotides <- unique(filtered_df[,chain]) - if (length(nucleotides) > 1) { - chain_col_number <- 1 - nucleotide_lengths <- nchar(nucleotides) - # Pre-allocate list - list <- vector("list", length = length(nucleotides)) - - for (i in 1:(length(nucleotides) - 1)) { - temp_list <- vector("list", length = length(nucleotides) - i) - - idx_i <- tmp[, chain_col_number] == nucleotides[i] - len_i <- nucleotide_lengths[i] - - for (j in (i + 1):length(nucleotides)) { - distance <- stringdist::stringdist(nucleotides[i], nucleotides[j], method = "lv") - distance_norm <- 1 - distance / ((len_i + nucleotide_lengths[j]) / 2) - - if (!is.na(distance_norm) & distance_norm > threshold) { - idx_j <- tmp[, chain_col_number] == nucleotides[j] - stored_positions <- idx_j & !idx_i - - if(any(stored_positions)) { - # Store this pair in the edge list that is not the same chain - temp_list[[j - i]] <- data.frame(from = which(idx_i), - to = which(stored_positions)) - } - } - } - list[[i]] <- do.call(rbind, temp_list) # Collapsing all data.frames in temp_list - } - - #Remove any NULL or 0 list elements - if(length(list) > 0) { - list <- list[-which(unlist(lapply(list, is.null)))] - list <- list[lapply(list,length)>0] - list <- bind_rows(list) %>% as.data.frame() - secondary.list[[i]] <- list - } - #Remove any NULL or 0 list elements - if(length(secondary.list) > 0) { - secondary.list <- secondary.list[-which(unlist(lapply(secondary.list, is.null)))] - secondary.list <- secondary.list[lapply(secondary.list,length)>0] - } - } - secondary.list - }) - edge.list <- bind_rows(edge.list) - - if (nrow(edge.list) > 0) { - edge.list <- unique(edge.list) - g <- graph_from_data_frame(edge.list) - components <- components(g, mode = c("weak")) - out <- data.frame("cluster" = components$membership, - "filtered" = names(components$membership)) - filter <- which(table(out$cluster) > 1) - out <- subset(out, cluster %in% filter) - if(nrow(out) > 1) { - out$cluster <- paste0(gene, ":LD", ".", out$cluster) - out$filtered <- tmp[,1][as.numeric(out$filtered)] - uni_IG <- as.data.frame(unique(tmp[,1][tmp[,1] %!in% out$filtered])) - } - } else { - uni_IG <- as.data.frame(unique(tmp[,1])) - } - colnames(uni_IG)[1] <- "filtered" - if (nrow(uni_IG) > 0) { - uni_IG$cluster <- paste0(gene, ".", seq_len(nrow(uni_IG))) - } - output <- rbind.data.frame(out, uni_IG) - colnames(output) <- c("Hclonotype", "IG") - return(output) -} From 3cfc5d9ca010ee00f8226d67eb011f144d6decde Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Sun, 22 Oct 2023 16:37:52 -0500 Subject: [PATCH 096/142] pkgdown first deploy --- .Rbuildignore | 4 + .github/workflows/pkgdown.yaml | 48 +++ .gitignore | 2 + DESCRIPTION | 2 +- NAMESPACE | 2 +- R/RcppExports.R | 2 +- R/addVariable.R | 1 + R/alluvialClones.R | 1 + R/clonalAbundance.R | 1 + R/clonalBias.R | 5 +- R/clonalCluster.R | 160 ++++++++++ R/clonalCompare.R | 1 + R/clonalDiversity.R | 1 + R/clonalHomeostasis.R | 1 + R/clonalLength.R | 1 + R/clonalNetwork.R | 2 +- R/clonalOccupy.R | 1 + R/clonalOverlap.R | 1 + R/clonalOverlay.R | 1 + R/clonalProportion.R | 17 +- R/clonalQuant.R | 1 + R/clonalRarefaction.R | 1 + R/clonalScatter.R | 1 + R/clonalSizeDistribution.R | 3 +- R/combineContigs.R | 2 + R/combineExpression.R | 1 + R/createHTOContigList.R | 1 + R/data.R | 4 + R/exportClones.R | 1 + R/getCircilize.R | 1 + R/highlightClones.R | 1 + R/loadContigs.R | 14 +- R/percentAA.R | 1 + R/percentGenes.R | 1 + R/percentKmer.R | 1 + R/percentVJ.R | 1 + R/seurat_command.R | 2 + R/startracDiversity.R | 1 + R/{subsetContig.R => subsetClone.R} | 8 +- R/utils.R | 35 ++- R/vizGenes.R | 1 + README.md | 17 +- _pkgdown.yml | 116 ++++++++ index.md | 65 +++++ inst/pkgdown.yml | 26 ++ man/StartracDiversity.Rd | 1 + man/addVariable.Rd | 1 + man/alluvialClones.Rd | 1 + man/clonalAbundance.Rd | 1 + man/clonalBias.Rd | 1 + man/clonalCluster.Rd | 1 + man/clonalCompare.Rd | 1 + man/clonalDiversity.Rd | 1 + man/clonalHomeostasis.Rd | 1 + man/clonalLength.Rd | 1 + man/clonalNetwork.Rd | 1 + man/clonalOccupy.Rd | 1 + man/clonalOverlap.Rd | 1 + man/clonalOverlay.Rd | 1 + man/clonalProportion.Rd | 7 +- man/clonalQuant.Rd | 1 + man/clonalRarefaction.Rd | 1 + man/clonalScatter.Rd | 1 + man/clonalSizeDistribution.Rd | 3 +- man/combineBCR.Rd | 2 +- man/combineExpression.Rd | 1 + man/combineTCR.Rd | 1 + man/contig_list.Rd | 1 + man/createHTOContigList.Rd | 1 + man/exportClones.Rd | 1 + man/figures/screpertoire_hex.png | Bin 0 -> 210069 bytes man/getCirclize.Rd | 1 + man/highlightClones.Rd | 1 + man/loadContigs.Rd | 7 +- man/mini_contig_list.Rd | 1 + man/percentAA.Rd | 1 + man/percentGenes.Rd | 1 + man/percentKmer.Rd | 1 + man/percentVJ.Rd | 1 + man/scRepertoire-package.Rd | 7 + man/screp_example.Rd | 1 + man/{subsetContig.Rd => subsetClones.Rd} | 13 +- man/vizGenes.Rd | 1 + vignettes/articles/Attaching_SC.Rmd | 122 ++++++++ vignettes/articles/Clonal_Bias.Rmd | 100 +++++++ vignettes/articles/Clonal_Cluster.Rmd | 133 +++++++++ vignettes/articles/Clonal_Diversity.Rmd | 123 ++++++++ vignettes/articles/Clonal_Dynamics.Rmd | 93 ++++++ vignettes/articles/Clonal_Rarefaction.Rmd | 120 ++++++++ vignettes/articles/Clonal_Visualizations.Rmd | 205 +++++++++++++ vignettes/articles/Combining_Contigs.Rmd | 107 +++++++ vignettes/articles/FAQ.Rmd | 80 +++++ vignettes/articles/Ibex.Rmd | 214 ++++++++++++++ vignettes/articles/Installation.Rmd | 64 ++++ vignettes/articles/Loading.Rmd | 113 +++++++ vignettes/articles/Processing.Rmd | 99 +++++++ vignettes/articles/Repertoire_Summary.Rmd | 201 +++++++++++++ vignettes/articles/SC_Visualizations.Rmd | 202 +++++++++++++ vignettes/articles/Trex.Rmd | 291 +++++++++++++++++++ vignettes/vignette.Rmd | 49 ++-- www/screpertoire_hex.png | Bin 210069 -> 223283 bytes 101 files changed, 2876 insertions(+), 70 deletions(-) create mode 100644 .github/workflows/pkgdown.yaml create mode 100644 R/clonalCluster.R rename R/{subsetContig.R => subsetClone.R} (81%) create mode 100644 _pkgdown.yml create mode 100644 index.md create mode 100644 inst/pkgdown.yml create mode 100644 man/figures/screpertoire_hex.png rename man/{subsetContig.Rd => subsetClones.Rd} (71%) create mode 100644 vignettes/articles/Attaching_SC.Rmd create mode 100644 vignettes/articles/Clonal_Bias.Rmd create mode 100644 vignettes/articles/Clonal_Cluster.Rmd create mode 100644 vignettes/articles/Clonal_Diversity.Rmd create mode 100644 vignettes/articles/Clonal_Dynamics.Rmd create mode 100644 vignettes/articles/Clonal_Rarefaction.Rmd create mode 100644 vignettes/articles/Clonal_Visualizations.Rmd create mode 100644 vignettes/articles/Combining_Contigs.Rmd create mode 100644 vignettes/articles/FAQ.Rmd create mode 100644 vignettes/articles/Ibex.Rmd create mode 100644 vignettes/articles/Installation.Rmd create mode 100644 vignettes/articles/Loading.Rmd create mode 100644 vignettes/articles/Processing.Rmd create mode 100644 vignettes/articles/Repertoire_Summary.Rmd create mode 100644 vignettes/articles/SC_Visualizations.Rmd create mode 100644 vignettes/articles/Trex.Rmd diff --git a/.Rbuildignore b/.Rbuildignore index 28c121f5..184dd927 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -6,3 +6,7 @@ ^LICENSE\.md$ ^JOSS$ ^draft-pdf\.yml$ +^_pkgdown\.yml$ +^docs$ +^pkgdown$ +^index\.md$ diff --git a/.github/workflows/pkgdown.yaml b/.github/workflows/pkgdown.yaml new file mode 100644 index 00000000..ed7650c7 --- /dev/null +++ b/.github/workflows/pkgdown.yaml @@ -0,0 +1,48 @@ +# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples +# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help +on: + push: + branches: [main, master] + pull_request: + branches: [main, master] + release: + types: [published] + workflow_dispatch: + +name: pkgdown + +jobs: + pkgdown: + runs-on: ubuntu-latest + # Only restrict concurrency for non-PR jobs + concurrency: + group: pkgdown-${{ github.event_name != 'pull_request' || github.run_id }} + env: + GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} + permissions: + contents: write + steps: + - uses: actions/checkout@v3 + + - uses: r-lib/actions/setup-pandoc@v2 + + - uses: r-lib/actions/setup-r@v2 + with: + use-public-rspm: true + + - uses: r-lib/actions/setup-r-dependencies@v2 + with: + extra-packages: any::pkgdown, local::. + needs: website + + - name: Build site + run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE) + shell: Rscript {0} + + - name: Deploy to GitHub pages 🚀 + if: github.event_name != 'pull_request' + uses: JamesIves/github-pages-deploy-action@v4.4.1 + with: + clean: false + branch: gh-pages + folder: docs diff --git a/.gitignore b/.gitignore index d39625f1..dba52245 100644 --- a/.gitignore +++ b/.gitignore @@ -4,3 +4,5 @@ .Rhistory local_tests.R .RData +docs +vignettes/articles/scRep_example_full.rds diff --git a/DESCRIPTION b/DESCRIPTION index d83550a5..309dae38 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -30,7 +30,6 @@ Imports: rjson, rlang, S4Vectors, - seqinr, SeuratObject, SingleCellExperiment, stats, @@ -58,3 +57,4 @@ Config/testthat/edition: 3 Language: en-US LinkingTo: Rcpp +URL: https://ncborcherding.github.io/scRepertoire/ diff --git a/NAMESPACE b/NAMESPACE index f03298d8..178ba6d5 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -31,7 +31,7 @@ export(percentAA) export(percentGenes) export(percentKmer) export(percentVJ) -export(subsetContig) +export(subsetClones) export(vizGenes) import(dplyr) import(ggplot2) diff --git a/R/RcppExports.R b/R/RcppExports.R index 7b828278..0f85f4b5 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -1,6 +1,6 @@ # Generated by using Rcpp::compileAttributes() -> do not edit by hand # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 - +#' @keywords internal rcpp_hello_world <- function() { invisible(.Call(`_scRepertoire_rcpp_hello_world`)) } diff --git a/R/addVariable.R b/R/addVariable.R index f5bdbb2f..7237d671 100644 --- a/R/addVariable.R +++ b/R/addVariable.R @@ -18,6 +18,7 @@ #' @param name The column header to add. #' @param variables The exact values to add to each element of the list. #' @export +#' @concept Loading_and_Processing_Contigs #' @return list of contigs with a new column (name). addVariable <- function(df, name = NULL, variables = NULL) { if (length(df) != length(variables)) { diff --git a/R/alluvialClones.R b/R/alluvialClones.R index d7dfc4e3..685e1316 100644 --- a/R/alluvialClones.R +++ b/R/alluvialClones.R @@ -65,6 +65,7 @@ #' @importFrom dplyr %>% mutate #' #' @export +#' @concept SC_Functions #' @return Alluvial ggplot comparing clonotype distribution. alluvialClones <- function(sc, cloneCall = "strict", diff --git a/R/clonalAbundance.R b/R/clonalAbundance.R index 6b9bd976..93e0267d 100644 --- a/R/clonalAbundance.R +++ b/R/clonalAbundance.R @@ -30,6 +30,7 @@ #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @importFrom ggplot2 ggplot #' @export +#' @concept Visualizing_Clones #' @return ggplot of the total or relative abundance of clonotypes #' across quanta clonalAbundance <- function(df, diff --git a/R/clonalBias.R b/R/clonalBias.R index 7c94cd77..1cf269c2 100644 --- a/R/clonalBias.R +++ b/R/clonalBias.R @@ -44,6 +44,7 @@ #' @import ggplot2 #' @importFrom stringr str_sort #' @export +#' @concept SC_Functions #' @return Returns ggplot of the clonotype bias clonalBias <- function(df, cloneCall="strict", @@ -97,10 +98,10 @@ clonalBias <- function(df, meta <- unique(meta[,c(cloneCall, split.by, "cloneSize")]) bias$cloneSize <- NA - for(i in seq_len(nrow(meta))) { + for(i in seq_len(nrow(bias))) { split <- bias[,1][i] clone <- bias[,3][i] - bias$cloneSize[i] <- as.vector(meta[which(meta[,cloneCall] == clone & meta[,split.by] == split),"cloneSize"]) + bias$cloneSize[i] <- as.vector(meta[which(meta[,cloneCall] == clone & meta[,split.by] == split),"cloneSize"])[1] } bias$cloneSize <- factor(bias$cloneSize , rev(levels(meta[,"cloneSize"]))) diff --git a/R/clonalCluster.R b/R/clonalCluster.R new file mode 100644 index 00000000..9cbec399 --- /dev/null +++ b/R/clonalCluster.R @@ -0,0 +1,160 @@ +#' Clustering adaptive receptor sequences +#' +#' This function uses edit distances of either the nucleotide or amino acid +#' sequences of the CDR3 and V genes to cluster similar TCR/BCRs together. +#' As a default, the function takes the input from \code{\link{combineTCR}}, +#' \code{\link{combineBCR}} or \code{\link{combineExpression}} and amends a +#' cluster to the data frame or meta data. If **exportGraph** is set to TRUE, +#' the function returns an igraph object of the connected sequences. +#' +#' @examples +#' # Getting the combined contigs +#' combined <- combineTCR(contig_list, +#' samples = c("P17B", "P17L", "P18B", "P18L", +#' "P19B","P19L", "P20B", "P20L")) +#' +#' sub_combined <- clonalCluster(combined[[2]], chain = "TRA", sequence = "aa") +#' +#' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}} +#' or \code{\link{combineExpression}}. +#' @param chain Indicate if both or a specific chain should be used - +#' e.g. "both", "TRA", "TRG", "IGH", "IGL". +#' @param sequence Clustering based on either "aa" or "nt". +#' @param samples The specific samples to isolate for visualization. +#' @param threshold The normalized edit distance to consider. The higher the number the more +#' similarity of sequence will be used for clustering. +#' @param group.by The column header used for to group contigs. +#' @param exportGraph Return an igraph object of connected sequences (TRUE) or the amended +#' input with a new cluster-based variable (FALSE) +#' @importFrom stringdist stringdist +#' @importFrom igraph set_vertex_attr V +#' @importFrom plyr join +#' @importFrom dplyr bind_rows +#' @importFrom stringr str_split str_replace_all +#' @importFrom rlang %||% +#' @importFrom SummarizedExperiment colData<- colData +#' @importFrom stats na.omit +#' @export +#' @concept Visualizing_Clones +#' @return Either amended input with edit-distanced clusters added +#' or igraph object of connect sequences + +clonalCluster <- function(df, + chain = "TRB", + sequence = "aa", + samples = NULL, + threshold = 0.85, + group.by = NULL, + exportGraph = FALSE) { + output.list <- list() + if(chain == "both") { + stop("Please select an individual chain for clustering") + } + #Prepping any single-cell object + is.list <- inherits(df, "list") + if (inherits(x = df, what = c("Seurat", "SummarizedExperiment"))) { + dat <- .grabMeta(df) + } else { + dat <- df + } + dat <- .checkList(dat) + dat <- .data.wrangle(dat, group.by, "CTgene", chain) + + if (inherits(x = df, what = c("Seurat", "SummarizedExperiment"))) { + for (y in seq_along(dat)) { + cdr3_aa <- str_split(dat[[y]]$CTaa, "_", simplify = TRUE) + cdr3_nt <- str_split(dat[[y]]$CTnt, "_", simplify = TRUE) + + dat[[y]]$cdr3_aa1 <- cdr3_aa[, 1] + dat[[y]]$cdr3_aa2 <- cdr3_aa[, 2] + dat[[y]]$cdr3_nt1 <- cdr3_nt[, 1] + dat[[y]]$cdr3_nt2 <- cdr3_nt[, 2] + } + } + if (!is.null(samples)) { + dat <- dat[which(names(dat) %in% samples)] + } + dat <- .checkList(dat) + + #Getting column labels to pull from + if (grepl("TRA|TRG", chain)) { + gene <- "TCR" + ref <- 1 + } else if (grepl("TRB|TRD", chain)) { + gene <- "TCR" + ref <- 2 + } else if (grepl("IGH", chain)) { + gene <- "BCR" + ref <- 1 + } else if (grepl("IGL", chain)) { + gene <- "BCR" + ref <- 2 + } + ref2 <- paste0("cdr3_", sequence, ref) + + if (!is.null(group.by)) { + bound <- bind_rows(dat, .id = "group.by") + + graph.variables <- bound %>% + group_by(bound[,ref2]) %>% + summarize(sample_count = n(), + unique_samples = paste0(unique(group.by), collapse = ",")) + + } else { + bound <- bind_rows(dat) + graph.variables <- bound %>% + group_by(bound[,ref2]) %>% + summarize(sample_count = n()) + } + + #Generating Connected Component + cluster <- .lvCompare(bound, + gene = "CTgene", + chain = ref2, + threshold = threshold, + exportGraph = exportGraph) + + if(exportGraph) { + vertex <- names(V(cluster)) + data_df <- unique(data.frame( + id = V(cluster)$name + )) + data_df <- merge(data_df, graph.variables, by = 1) + cluster <- set_vertex_attr(cluster, name = "size", index = data_df$id, value = data_df[,2]) + if(ncol(data_df) == 3) { #add grouping variable + cluster <- set_vertex_attr(cluster, name = "group", index = data_df$id, value = data_df[,3]) + } + return(cluster) + } + + cluster[,1] <- str_replace_all(cluster[,1], "CTgene", chain) + colnames(cluster) <- c(paste0(chain, "_cluster"), ref2) + + #Merging with contig info + tmp <- bound + tmp[,ref2] <- str_split(tmp[,ref2], ";", simplify = TRUE)[,1] + output2 <- cluster[cluster[,2] %in% tmp[,ref2],] + bound <- suppressMessages(join(tmp, output2, match = "first")) + + #Adding to potential single-cell object + if(inherits(x=df, what ="Seurat") | inherits(x=df, what ="SummarizedExperiment")) { + PreMeta <- bound + x <- colnames(PreMeta)[ncol(PreMeta)] + PreMeta <- as.data.frame(PreMeta[,x], row.names = rownames(bound)) + colnames(PreMeta) <- x + if (inherits(x=df, what ="Seurat")) { + col.name <- names(PreMeta) %||% colnames(PreMeta) + df[[col.name]] <- PreMeta + } else { + rownames <- rownames(colData(df)) + colData(df) <- cbind(colData(df), PreMeta[rownames,])[, union(colnames(colData(df)), colnames(PreMeta))] + rownames(colData(df)) <- rownames + } + } else { + df <- bound + } + return(df) +} + + + \ No newline at end of file diff --git a/R/clonalCompare.R b/R/clonalCompare.R index d01bee40..f6b766b1 100644 --- a/R/clonalCompare.R +++ b/R/clonalCompare.R @@ -39,6 +39,7 @@ #' @importFrom stringr str_sort #' #' @export +#' @concept Visualizing_Clones #' @return ggplot of the proportion of total sequencing read of #' selecting clones clonalCompare <- function(df, diff --git a/R/clonalDiversity.R b/R/clonalDiversity.R index 0cab22f6..e1477c73 100644 --- a/R/clonalDiversity.R +++ b/R/clonalDiversity.R @@ -67,6 +67,7 @@ #' @importFrom dplyr sample_n #' @import ggplot2 #' @export +#' @concept Visualizing_Clones #' @return ggplot of the diversity of clones by group #' @author Andrew Malone, Nick Borcherding clonalDiversity <- function(df, diff --git a/R/clonalHomeostasis.R b/R/clonalHomeostasis.R index 5e64730e..725a5b2a 100644 --- a/R/clonalHomeostasis.R +++ b/R/clonalHomeostasis.R @@ -31,6 +31,7 @@ #' @importFrom reshape2 melt #' @importFrom dplyr bind_rows #' @export +#' @concept Visualizing_Clones #' @return ggplot of the space occupied by the specific proportion of clonotypes clonalHomeostasis <- function(df, cloneSize = diff --git a/R/clonalLength.R b/R/clonalLength.R index ce9d0e19..7b9abf3c 100644 --- a/R/clonalLength.R +++ b/R/clonalLength.R @@ -30,6 +30,7 @@ #' @importFrom stringr str_split #' @importFrom ggplot2 ggplot #' @export +#' @concept Visualizing_Clones #' @return ggplot of the discrete or relative length distributions of #' clonotype sequences clonalLength <- function(df, diff --git a/R/clonalNetwork.R b/R/clonalNetwork.R index c6cb9957..ea78cb8e 100644 --- a/R/clonalNetwork.R +++ b/R/clonalNetwork.R @@ -54,9 +54,9 @@ #' @importFrom ggraph ggraph geom_edge_bend geom_node_point scale_edge_colour_gradientn circle guide_edge_colourbar #' @importFrom stats setNames #' @export +#' @concept SC_Functions #' @return ggplot object #' -#' clonalNetwork <- function(sc, reduction = "umap", group.by = "ident", diff --git a/R/clonalOccupy.R b/R/clonalOccupy.R index 9556c747..2032499f 100644 --- a/R/clonalOccupy.R +++ b/R/clonalOccupy.R @@ -36,6 +36,7 @@ #' @importFrom reshape2 melt #' @import ggplot2 #' @export +#' @concept SC_Functions #' @return Stacked bar plot of counts of cells by clonotype frequency group clonalOccupy <- function(sc, diff --git a/R/clonalOverlap.R b/R/clonalOverlap.R index 8875b9f9..290528d5 100644 --- a/R/clonalOverlap.R +++ b/R/clonalOverlap.R @@ -53,6 +53,7 @@ #' @importFrom reshape2 melt #' @importFrom stats quantile #' @export +#' @concept Visualizing_Clones #' @return ggplot of the overlap of clones by group clonalOverlap <- function(df, cloneCall = "strict", diff --git a/R/clonalOverlay.R b/R/clonalOverlay.R index ad3b8f64..2de77031 100644 --- a/R/clonalOverlay.R +++ b/R/clonalOverlay.R @@ -33,6 +33,7 @@ #' @import ggplot2 #' @importFrom SeuratObject Embeddings #' @export +#' @concept SC_Functions #' @author Francesco Mazziotta, Nick Borcherding #' #' @return ggplot object diff --git a/R/clonalProportion.R b/R/clonalProportion.R index 18b12c99..200ac130 100644 --- a/R/clonalProportion.R +++ b/R/clonalProportion.R @@ -2,7 +2,7 @@ #' #' This function calculates the relative clonal space occupied by the #' clonotypes. The grouping of these clonotypes is based on the parameter -#' **clonal.split**, at default, **clonal.split** will group the clonotypes +#' **clonalSplit**, at default, **clonalSplit** will group the clonotypes #' into bins of 1:10, 11:100, 101:1001, etc. To adjust the clonotypes #' selected, change the numbers in the variable split. If a matrix output #' for the data is preferred, set exportTable = TRUE. @@ -16,7 +16,7 @@ #' #' @param df The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or #' \code{\link{combineExpression}}. -#' @param clonal.split The cut points for the specific clonotypes. +#' @param clonalSplit The cut points for the specific clonotypes. #' @param cloneCall How to call the clonotype - VDJC gene (gene), #' CDR3 nucleotide (nt), CDR3 amino acid (aa), or #' VDJC gene + CDR3 nucleotide (strict). @@ -33,9 +33,10 @@ #' @importFrom dplyr bind_rows #' #' @export +#' @concept Visualizing_Clones #' @return ggplot of the space occupied by the specific rank of clonotypes clonalProportion <- function(df, - clonal.split = c(10, 100, 1000, 10000, 30000, 100000), + clonalSplit = c(10, 100, 1000, 10000, 30000, 100000), cloneCall = "strict", chain = "both", group.by = NULL, @@ -50,8 +51,8 @@ clonalProportion <- function(df, } #Generating data matrix to store value - mat <- matrix(0, length(df), length(clonal.split), dimnames = list(names(df), - paste0('[', c(1, clonal.split[-length(clonal.split)] + 1), ':', clonal.split, ']'))) + mat <- matrix(0, length(df), length(clonalSplit), dimnames = list(names(df), + paste0('[', c(1, clonalSplit[-length(clonalSplit)] + 1), ':', clonalSplit, ']'))) #Assigning the clonal grouping df <- lapply(df, '[[', cloneCall) @@ -60,9 +61,9 @@ clonalProportion <- function(df, for (i in seq_along(df)) { df[[i]] <- rev(sort(as.numeric(df[[i]][,2]))) } - cut <- c(1, clonal.split[-length(clonal.split)] + 1) - for (i in seq_along(clonal.split)) { - mat[,i] <- vapply(df, function (x) sum(na.omit(x[cut[i]:clonal.split[i]])), + cut <- c(1, clonalSplit[-length(clonalSplit)] + 1) + for (i in seq_along(clonalSplit)) { + mat[,i] <- vapply(df, function (x) sum(na.omit(x[cut[i]:clonalSplit[i]])), FUN.VALUE = numeric(1)) } if (exportTable == TRUE) { diff --git a/R/clonalQuant.R b/R/clonalQuant.R index 9115d807..d77bb520 100644 --- a/R/clonalQuant.R +++ b/R/clonalQuant.R @@ -25,6 +25,7 @@ #' @param palette Colors to use in visualization - input any \link[grDevices]{hcl.pals}. #' @import ggplot2 #' @export +#' @concept Visualizing_Clones #' @return ggplot of the total or relative unique clonotypes clonalQuant <- function(df, cloneCall = "strict", diff --git a/R/clonalRarefaction.R b/R/clonalRarefaction.R index 1ef171ac..321da0e9 100644 --- a/R/clonalRarefaction.R +++ b/R/clonalRarefaction.R @@ -35,6 +35,7 @@ #' @importFrom iNEXT iNEXT ggiNEXT #' @import ggplot2 #' @export +#' @concept Visualizing_Clones clonalRarefaction <- function(df, cloneCall = "strict", chain = "both", diff --git a/R/clonalScatter.R b/R/clonalScatter.R index 2f1d934d..791e00bb 100644 --- a/R/clonalScatter.R +++ b/R/clonalScatter.R @@ -33,6 +33,7 @@ #' @import ggplot2 #' #' @export +#' @concept Visualizing_Clones #' @return ggplot of the relative clonotype numbers clonalScatter <- function(df, diff --git a/R/clonalSizeDistribution.R b/R/clonalSizeDistribution.R index 05c99d33..931bb346 100644 --- a/R/clonalSizeDistribution.R +++ b/R/clonalSizeDistribution.R @@ -18,7 +18,7 @@ #' \item{\eqn{\xi} is a shape parameter} #' \item{\eqn{x \ge \mu} if \eqn{\xi \ge 0} and \eqn{\mu \le x \le \mu - \sigma/\xi} if \eqn{\xi < 0}} #' } -#' +#' #' The probability density function (pdf) for the Gamma Distribution is given by: #' \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ #' f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) @@ -57,6 +57,7 @@ #' @importFrom ggdendro dendro_data segment label #' @importFrom stats hclust optim pgamma as.dist #' @export +#' @concept Visualizing_Clones #' @return ggplot dendrogram of the clone size distribution #' @author Hillary Koch diff --git a/R/combineContigs.R b/R/combineContigs.R index ed8ed0aa..a9397f11 100644 --- a/R/combineContigs.R +++ b/R/combineContigs.R @@ -44,6 +44,7 @@ utils::globalVariables(c( #' #' @import dplyr #' @export +#' @concept Loading_and_Processing_Contigs #' @return List of clonotypes for individual cell barcodes #' combineTCR <- function(df, @@ -165,6 +166,7 @@ combineTCR <- function(df, #' chains, if not calling related clones. #' @import dplyr #' @export +#' @concept Loading_and_Processing_Contigs #' @return List of clonotypes for individual cell barcodes combineBCR <- function(df, samples = NULL, diff --git a/R/combineExpression.R b/R/combineExpression.R index 435633e9..38e52c3b 100644 --- a/R/combineExpression.R +++ b/R/combineExpression.R @@ -48,6 +48,7 @@ #' @importFrom rlang %||% #' @importFrom SummarizedExperiment colData<- colData #' @export +#' @concept SC_Functions #' @return seurat or SingleCellExperiment object with attached clonotype #' information #' diff --git a/R/createHTOContigList.R b/R/createHTOContigList.R index 2fe2af4c..2ad405a7 100644 --- a/R/createHTOContigList.R +++ b/R/createHTOContigList.R @@ -25,6 +25,7 @@ #' @param multi.run If using integrated single-cell object, the meta data variable #' that indicates the sequencing run. #' @export +#' @concept Loading_and_Processing_Contigs #' @return Returns a list of contigs corresponding to the multiplexed single-cell object diff --git a/R/data.R b/R/data.R index 4342e327..1a46f4a9 100644 --- a/R/data.R +++ b/R/data.R @@ -1,6 +1,7 @@ #' A data set of T cell contigs as a list outputed from the #' filter_contig_annotation files. #' @docType data +#' @concept Data #' @name contig_list #' NULL @@ -12,6 +13,7 @@ NULL #' sequencing data can be added with `combineExpression`. #' #' @name scRep_example +#' @concept Data #' @docType data #' NULL @@ -26,6 +28,8 @@ NULL #' #' @format An R `list` of `data.frame` objects #' +#' @concept Data +#' #' @docType data #' #' @seealso \code{\link{contig_list}} diff --git a/R/exportClones.R b/R/exportClones.R index 91a49b18..43e782e7 100644 --- a/R/exportClones.R +++ b/R/exportClones.R @@ -22,6 +22,7 @@ #' @importFrom stringr str_split #' @importFrom utils write.csv #' @export +#' @concept Loading_and_Processing_Contigs #' @return ggplot of percentage of V and J gene pairings as a heatmap #' @author Jonathan Noonan, Nick Borcherding exportClones <- function(df, diff --git a/R/getCircilize.R b/R/getCircilize.R index fa1bacc7..6783f434 100644 --- a/R/getCircilize.R +++ b/R/getCircilize.R @@ -38,6 +38,7 @@ #' #' @importFrom reshape2 dcast #' @export +#' @concept SC_Functions #' @return data frame of shared clonotypes between groups #' @author Dillon Corvino, Nick Borcherding getCirclize <- function(sc, diff --git a/R/highlightClones.R b/R/highlightClones.R index 674c4879..0bcfe156 100644 --- a/R/highlightClones.R +++ b/R/highlightClones.R @@ -26,6 +26,7 @@ #' @param sequence The specific sequence or sequence to highlight #' @importFrom S4Vectors DataFrame #' @export +#' @concept SC_Functions #' @return Single-cell object object with new meta data column for indicated clones highlightClones <- function(sc, cloneCall = c("gene", "nt", "aa", "strict"), diff --git a/R/loadContigs.R b/R/loadContigs.R index cad877c8..41719735 100644 --- a/R/loadContigs.R +++ b/R/loadContigs.R @@ -3,9 +3,7 @@ #' This function generates a contig list and formats the data to allow for #' function with \code{\link{combineTCR}} or \code{\link{combineBCR}}. If #' using data derived from filtered outputs of 10X Genomics, there is no -#' need to use this function as the data is already compatible. The function -#' assumes if listing multiple directories, there are distinct outputs with -#' unmodified file names in them. +#' need to use this function as the data is already compatible. #' #' The files that this function parses includes: #' \itemize{ @@ -13,7 +11,7 @@ #' \item AIRR = "airr_rearrangement.tsv" #' \item BD = "Contigs_AIRR.tsv" #' \item JSON = ".json" -#' \item MiXCR = "clones.txt" +#' \item MiXCR = "clones.tsv" #' \item Omniscope = ".csv" #' \item TRUST4 = "barcode_report.tsv" #' \item WAT3R = "barcode_results.csv" @@ -36,6 +34,7 @@ #' @importFrom utils read.csv read.delim #' @importFrom rjson fromJSON #' @export +#' @concept Loading_and_Processing_Contigs #' @return List of contigs for further processing in scRepertoire loadContigs <- function(dir, format = "10X") { @@ -44,7 +43,7 @@ loadContigs <- function(dir, format.list <- list("WAT3R" = "barcode_results.csv", "10X" = "filtered_contig_annotation.csv", "AIRR" = "airr_rearrangement.tsv", - "MiXCR" = "clones.txt", + "MiXCR" = "clones.tsv", "JSON" = ".json", "TRUST4" = "barcode_report.tsv", "BD" = "Contigs_AIRR.tsv", @@ -211,9 +210,8 @@ loadContigs <- function(dir, df[[i]] <- do.call(rbind, df[[i]]) df[[i]][df[[i]] == ""] <- NA df[[i]] <- as.data.frame(df[[i]]) - df[[i]] <- df[[i]][,c("tagValueCELL", "readCount", "vGene", "dGene", "jGene", "cGene", "nSeqCDR3", "aaSeqCDR3")] - colnames(df[[i]]) <- c("barcode", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") - df[[i]]$chain <- substring(df[[i]]$v_gene, 1,3) + df[[i]] <- df[[i]][,c("tagValueCELL", "topChains", "readCount", "vGene", "dGene", "jGene", "cGene", "nSeqCDR3", "aaSeqCDR3")] + colnames(df[[i]]) <- c("barcode", "chain", "reads", "v_gene", "d_gene", "j_gene", "c_gene", "cdr3_nt", "cdr3") } return(df) } diff --git a/R/percentAA.R b/R/percentAA.R index e223e8ed..385c065d 100644 --- a/R/percentAA.R +++ b/R/percentAA.R @@ -20,6 +20,7 @@ #' @import ggplot2 #' @importFrom reshape2 melt #' @export +#' @concept Summarize_Repertoire #' @return ggplot of stacked bar graphs of amino acid proportions percentAA <- function(df, chain = "TRB", diff --git a/R/percentGenes.R b/R/percentGenes.R index a3cf60ed..7544b2bf 100644 --- a/R/percentGenes.R +++ b/R/percentGenes.R @@ -21,6 +21,7 @@ #' @importFrom stringr str_split str_sort #' @importFrom reshape2 melt #' @export +#' @concept Summarize_Repertoire #' @return ggplot of percentage of indicated genes as a heatmap #' percentGenes <- function(df, diff --git a/R/percentKmer.R b/R/percentKmer.R index 2b88312a..d5bd84ee 100644 --- a/R/percentKmer.R +++ b/R/percentKmer.R @@ -25,6 +25,7 @@ #' @importFrom reshape2 melt #' @importFrom stats mad #' @export +#' @concept Summarize_Repertoire #' @return ggplot of percentage of kmers as a heatmap percentKmer <- function(df, diff --git a/R/percentVJ.R b/R/percentVJ.R index bcd5d501..d6bcee44 100644 --- a/R/percentVJ.R +++ b/R/percentVJ.R @@ -20,6 +20,7 @@ #' @importFrom stringr str_split str_sort #' @importFrom reshape2 melt #' @export +#' @concept Summarize_Repertoire #' @return ggplot of percentage of V and J gene pairings as a heatmap #' percentVJ <- function(df, diff --git a/R/seurat_command.R b/R/seurat_command.R index fa2e3206..aaf87da4 100644 --- a/R/seurat_command.R +++ b/R/seurat_command.R @@ -3,6 +3,7 @@ # is modified. # function needed for make_screp_seurat_cmd +#' @keywords internal seurat_extractfield <- function(string, field = 1, delim = "_") { fields <- as.numeric( x = unlist(x = strsplit(x = as.character(x = field), split = ",")) @@ -17,6 +18,7 @@ seurat_extractfield <- function(string, field = 1, delim = "_") { # seurat's command adding but if a param is a dataframe or list of dataframes, # completely omits them to save memory. +#' @keywords internal make_screp_seurat_cmd <- function(call_time, assay) { if (as.character(x = sys.calls()[[1]])[1] == "do.call") { diff --git a/R/startracDiversity.R b/R/startracDiversity.R index f59ce58c..fecb2fa2 100644 --- a/R/startracDiversity.R +++ b/R/startracDiversity.R @@ -42,6 +42,7 @@ #' @importFrom reshape2 melt #' @import ggplot2 #' @export +#' @concept SC_Functions #' @return ggplot object of Startrac diversity metrics #' @author Liangtao Zheng StartracDiversity <- function(sc, diff --git a/R/subsetContig.R b/R/subsetClone.R similarity index 81% rename from R/subsetContig.R rename to R/subsetClone.R index e78f7f90..aec9323f 100644 --- a/R/subsetContig.R +++ b/R/subsetClone.R @@ -1,5 +1,4 @@ - -#' Subset the product of combineTCR() combineBCR() +#' Subset the product of combineTCR() or combineBCR() #' #' This function allows for the subsetting of the product of #' \code{\link{combineTCR}} or \code{\link{combineBCR}} @@ -10,15 +9,16 @@ #' combined <- combineTCR(contig_list, #' samples = c("P17B", "P17L", "P18B", "P18L", #' "P19B","P19L", "P20B", "P20L")) -#' subset <- subsetContig(combined, name = "sample", variables = c("P17B")) +#' subset <- subsetClones(combined, name = "sample", variables = c("P17B")) #' #' @param df The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}. #' @param name The column header you'd like to use to subset. #' @param variables The values to subset by, must be in the names(df). #' @export +#' @concept Loading_and_Processing_Contigs #' @return list of contigs that have been filtered for the name parameter -subsetContig <- function(df, name, variables = NULL) { +subsetClones <- function(df, name, variables = NULL) { names2 <- NULL df2 <- list() for (i in seq_along(df)) { diff --git a/R/utils.R b/R/utils.R index d6d57c60..fc7e6173 100644 --- a/R/utils.R +++ b/R/utils.R @@ -7,6 +7,7 @@ is_seurat_or_se_object <- function(obj) { } #Use to shuffle between chains +#' @keywords internal .off.the.chain <- function(dat, chain, cloneCall) { chain1 <- toupper(chain) #to just make it easier if (chain1 %in% c("TRA", "TRG", "IGH")) { @@ -22,6 +23,7 @@ is_seurat_or_se_object <- function(obj) { #Pulling a color palette for visualizations #' @importFrom grDevices hcl.colors +#' @keywords internal .colorizer <- function(palette = "inferno", n= NULL) { colors <- hcl.colors(n=n, palette = palette, fixup = TRUE) @@ -31,6 +33,7 @@ is_seurat_or_se_object <- function(obj) { #Remove list elements that contain all NA values +#' @keywords internal .checkBlanks <- function(df, cloneCall) { count <- NULL for (i in seq_along(df)) { @@ -49,6 +52,7 @@ is_seurat_or_se_object <- function(obj) { } #reshuffling df +#' @keywords internal .groupList <- function(df, group.by) { df <- bind_rows(df) df <- split(df, df[,group.by]) @@ -56,6 +60,7 @@ is_seurat_or_se_object <- function(obj) { } # Ensure df is in list format +#' @keywords internal .checkList <- function(df) { df <- tryCatch( { @@ -73,6 +78,7 @@ is_seurat_or_se_object <- function(obj) { df } +#' @keywords internal .checkContigs <- function(df) { df <- lapply(seq_len(length(df)), function(x) { df[[x]] <- if(!is.data.frame(df[[x]])) as.data.frame(df[[x]]) else df[[x]] @@ -83,6 +89,7 @@ is_seurat_or_se_object <- function(obj) { } #' @importFrom dplyr bind_rows +#' @keywords internal .bound.input.return <- function(df) { if (is_seurat_or_se_object(df)) { return(.grabMeta(df)) @@ -90,6 +97,7 @@ is_seurat_or_se_object <- function(obj) { bind_rows(df, .id = "element.names") } +#' @keywords internal .list.input.return <- function(df, split.by) { if (is_seurat_or_se_object(df)) { if(is.null(split.by)){ @@ -102,6 +110,7 @@ is_seurat_or_se_object <- function(obj) { #Get UMAP or other coordinates #' @importFrom SingleCellExperiment reducedDim +#' @keywords internal .get.coord <- function(sc, reduction) { if (is.null(reduction)) { reduction <- "pca" @@ -115,6 +124,7 @@ is_seurat_or_se_object <- function(obj) { } #This is to check the single-cell expression object +#' @keywords internal .checkSingleObject <- function(sc) { if (!is_seurat_or_se_object(sc)){ stop("Object indicated is not of class 'Seurat' or @@ -127,6 +137,7 @@ is_seurat_or_se_object <- function(obj) { #' @importFrom SingleCellExperiment colData #' @importFrom SeuratObject Idents #' @importFrom methods slot +#' @keywords internal .grabMeta <- function(sc) { if (is_seurat_object(sc)) { meta <- data.frame(sc[[]], slot(sc, "active.ident")) @@ -142,6 +153,7 @@ is_seurat_or_se_object <- function(obj) { } #This is to add the sample and ID prefixes for combineBCR()/TCR() +#' @keywords internal .modifyBarcodes <- function(df, samples, ID) { out <- NULL for (x in seq_along(df)) { @@ -156,6 +168,7 @@ is_seurat_or_se_object <- function(obj) { } #Removing barcodes with NA recovered +#' @keywords internal .removingNA <- function(final) { for(i in seq_along(final)) { final[[i]] <- na.omit(final[[i]])} @@ -163,6 +176,7 @@ is_seurat_or_se_object <- function(obj) { } #Removing barcodes with > 2 clones recovered +#' @keywords internal .removingMulti <- function(final){ for(i in seq_along(final)) { final[[i]] <- filter(final[[i]], !grepl(";",CTnt))} @@ -171,6 +185,7 @@ is_seurat_or_se_object <- function(obj) { #Removing extra clonotypes in barcodes with > 2 productive contigs #' @import dplyr +#' @keywords internal .filteringMulti <- function(x) { x <- x %>% group_by(barcode, chain) %>% @@ -197,6 +212,7 @@ is_seurat_or_se_object <- function(obj) { #Filtering NA contigs out of single-cell expression object #' @import dplyr #' @importFrom SingleCellExperiment colData +#' @keywords internal .filteringNA <- function(sc) { meta <- .grabMeta(sc) evalNA <- data.frame(meta[,"cloneSize"]) @@ -216,6 +232,7 @@ is_seurat_or_se_object <- function(obj) { } #Organizing list of contigs for visualization +#' @keywords internal .parseContigs <- function(df, i, names, cloneCall) { data <- df[[i]] data1 <- data %>% @@ -227,6 +244,7 @@ is_seurat_or_se_object <- function(obj) { } # This suppressing outputs for using dput() +#' @keywords internal .quiet <- function(x) { sink(tempfile()) on.exit(sink()) @@ -235,6 +253,7 @@ is_seurat_or_se_object <- function(obj) { # This is to help sort the type of clonotype data to use +#' @keywords internal .theCall <- function(x) { x <- switch(x, "gene" = "CTgene", @@ -254,6 +273,7 @@ is_seurat_or_se_object <- function(obj) { # Assigning positions for TCR contig data #' @author Gloria Kraus, Nick Bormann, Nicky de Vrij, Nick Borcherding +#' @keywords internal .parseTCR <- function(Con.df, unique_df, data2) { data2 <- data2 %>% arrange(., chain, cdr3_nt) for (y in seq_along(unique_df)){ @@ -291,6 +311,7 @@ is_seurat_or_se_object <- function(obj) { #Assigning positions for BCR contig data #Now assumes lambda over kappa in the context of only 2 light chains #' @author Gloria Kraus, Nick Bormann, Nick Borcherding +#' @keywords internal .parseBCR <- function (Con.df, unique_df, data2) { for (y in seq_along(unique_df)) { barcode.i <- Con.df$barcode[y] @@ -330,6 +351,7 @@ is_seurat_or_se_object <- function(obj) { #Producing a data frame to visualize for lengthContig() +#' @keywords internal .lengthDF <- function(df, cloneCall, chain, group, c1, c2){ Con.df <- NULL names <- names(df) @@ -371,6 +393,7 @@ is_seurat_or_se_object <- function(obj) { } #General combination of nucleotide, aa, and gene sequences for T/B cells +#' @keywords internal .assignCT <- function(cellType, Con.df) { if (cellType == "T") { Con.df$CTgene <- paste(Con.df$TCR1, Con.df$TCR2, sep="_") @@ -390,6 +413,7 @@ is_seurat_or_se_object <- function(obj) { #Sorting the V/D/J/C gene sequences for T and B cells #' @importFrom stringr str_c str_replace_na #' @importFrom dplyr bind_rows +#' @keywords internal .makeGenes <- function(cellType, data2, chain1, chain2) { if(cellType %in% c("T")) { data2 <- data2 %>% @@ -411,6 +435,7 @@ is_seurat_or_se_object <- function(obj) { } #Getting the minimum repertoire size for diversity boots +#' @keywords internal .short.check <- function(df, cloneCall) { min <- c() for (x in seq_along(df)) { @@ -422,6 +447,7 @@ is_seurat_or_se_object <- function(obj) { } # check if object is a dataframe or list of dataframes +#' @keywords internal is_df_or_list_of_df <- function(x) { if (is.data.frame(x)) { return(TRUE) @@ -436,6 +462,7 @@ is_df_or_list_of_df <- function(x) { } #Pulling meta data +#' @keywords internal .expression2List <- function(sc, split.by) { if (!inherits(x=sc, what ="Seurat") & !inherits(x=sc, what ="SummarizedExperiment")) { @@ -457,6 +484,7 @@ is_df_or_list_of_df <- function(x) { } #Making lists for single-cell object, check blanks and apply chain filter +#' @keywords internal .data.wrangle <- function(df, split.by, cloneCall, chain) { df <- .list.input.return(df, split.by) df <- .checkBlanks(df, cloneCall) @@ -473,6 +501,7 @@ is_df_or_list_of_df <- function(x) { #' @importFrom stringdist stringdist #' @importFrom igraph graph_from_data_frame components graph_from_edgelist #' @importFrom dplyr bind_rows +#' @keywords internal .lvCompare <- function(dictionary, gene, chain, threshold, exportGraph = FALSE) { overlap <- NULL out <- NULL @@ -483,7 +512,7 @@ is_df_or_list_of_df <- function(x) { unique.v <- na.omit(unique(tmp$v.gene)) edge.list <- lapply(unique.v, function(y) { - #for(y in unique.v) { + #for(y in unique.v) { secondary.list <- list() filtered_df <- tmp %>% filter(v.gene == y) filtered_df <- filtered_df[!is.na(filtered_df[,chain]),] @@ -497,14 +526,14 @@ is_df_or_list_of_df <- function(x) { for (i in 1:(length(nucleotides) - 1)) { temp_list <- vector("list", length = length(nucleotides) - i) - idx_i <- tmp[, chain_col_number] == nucleotides[i] + idx_i <- tmp[, chain_col_number] == nucleotides[i] & tmp[,2] == y len_i <- nucleotide_lengths[i] for (j in (i + 1):length(nucleotides)) { distance <- stringdist::stringdist(nucleotides[i], nucleotides[j], method = "lv") distance_norm <- 1 - distance / ((len_i + nucleotide_lengths[j]) / 2) - if (!is.na(distance_norm) & distance_norm > threshold) { + if (!is.na(distance_norm) & distance_norm >= threshold) { idx_j <- tmp[, chain_col_number] == nucleotides[j] stored_positions <- idx_j & !idx_i diff --git a/R/vizGenes.R b/R/vizGenes.R index 2569c005..8d1221b9 100644 --- a/R/vizGenes.R +++ b/R/vizGenes.R @@ -34,6 +34,7 @@ #' @importFrom stats sd #' @importFrom dplyr bind_rows #' @export +#' @concept Visualizing_Clones #' @return ggplot bar diagram or heatmap of gene usage vizGenes <- function(df, diff --git a/README.md b/README.md index 17769ce2..78aa6fcf 100644 --- a/README.md +++ b/README.md @@ -44,11 +44,26 @@ Unfortunately, Github limits the size of individual files. In order to access th ## Learning To Use scRepertoire -Vignette available [here](https://www.borch.dev/uploads/vignette/vignette), includes almost 13,000 T cells across 3 patients with renal clear cell carcinoma. +Vignette available [here](https://www.borch.dev/uploads/vignette/scRepertoire), includes almost 13,000 T cells across 4 patients with acute respiratory distress syndrome. ## Please Cite Feel free to use, edit, modify scRepertoire, but if you do, please cite the [manuscript](https://f1000research.com/articles/9-47/v1). If you are building your own tool based on scRepertoire, reach out, I am happy to help and make things compatible. +## Bug Reports/New Features + +#### If you run into any issues or bugs please submit a [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) with details of the issue. + +- If possible please include a [reproducible example](https://reprex.tidyverse.org/). +Alternatively, an example with the internal **scRep_example** and **contig_list** would +be extremely helpful. + +#### Any requests for new features or enhancements can also be submitted as [GitHub issues](https://github.com/ncborcherding/scRepertoire/issues). + +#### [Pull Requests](https://github.com/ncborcherding/scRepertoire/pulls) are welcome for bug fixes, new features, or enhancements. + +- Please set PR to merge with “dev” branch and provide description of what the PR +contains (referencing existing issue(s) if appropriate). + ## Contact Questions, comments, suggestions, please feel free to contact Nick Borcherding via this repository, [email](mailto:ncborch@gmail.com), or using [twitter](https://twitter.com/theHumanBorch). diff --git a/_pkgdown.yml b/_pkgdown.yml new file mode 100644 index 00000000..867709fd --- /dev/null +++ b/_pkgdown.yml @@ -0,0 +1,116 @@ +url: https://ncborcherding.github.io/scRepertoire/ +Template: + params: + bootswatch: flatly + +home: + sidebar: + structure: [links, license, custom, authors, dev] + components: + custom: + title: Citation + text: "[Citing scRepertoire](https://f1000research.com/articles/9-47)" + +navbar: + left: + - text: "Getting Started" + href: index.html + - text: "Install" + href: articles/Installation.html + - text: "Vignettes" + menu: + - text: "Loading Data" + href: articles/Loading.html + - text: "Combining Contigs into Clones" + href: articles/Combining_Contigs.html + - text: "Additional Processing" + href: articles/Processing.html + - text: ------- + - text: "Basic Clonal Analysis" + href: articles/Clonal_Visualizations.html + - text: "Visualizing Clonal Dynamics" + href: articles/Clonal_Dynamics.html + - text: "Summarizing Repertoires" + href: articles/Repertoire_Summary.html + - text: "Clonal Diversity and Overlap" + href: articles/Clonal_Diversity.html + - text: "Clustering Sequences by Edit Distance" + href: articles/Clonal_Cluster.html + - text: ------- + - text: "Combining Clones and Single-Cell Objects" + href: articles/Attaching_SC.html + - text: "Visualizations for Single-Cell Objects" + href: articles/SC_Visualizations.html + - text: "Clonal Bias" + href: articles/Clonal_Bias.html + - text: ------- + - text: "Using Trex for TCRs" + href: articles/Trex.html + - text: "Using Ibex for BCRs" + href: articles/Ibex.html + - text: "News/Changelog" + href: news/index.html + - text: "Reference" + href: reference/index.html + - text: "FAQ" + href: articles/FAQ.html + +reference: +- title: "Loading and Processing Contigs" + desc: "Functions that load, combine, and process the single-cell contig information." +- contents: + - addVariable + - combineBCR + - combineTCR + - createHTOContigList + - exportClones + - loadContigs + - subsetClones + +- title: "Visualizing Clones" + desc: "Functions plotting clonal data." +- contents: + - clonalAbundance + - clonalCluster + - clonalCompare + - clonalDiversity + - clonalHomeostasis + - clonalLength + - clonalOverlap + - clonalProportion + - clonalQuant + - clonalRarefaction + - clonalScatter + - clonalSizeDistribution + - vizGenes + +- title: "Summarizing Repertoire" + desc: "Functions to summarize clonal sequences across the repertoire" +- contents: + - percentAA + - percentGenes + - percentKmer + - percentVJ + +- title: "Single-Cell Object Processing and Visualizations" + desc: "Functions to add or visualize clonal information along a single-cell object" +- contents: + - alluvialClones + - clonalBias + - clonalNetwork + - clonalOccupy + - clonalOverlay + - combineExpression + - getCirclize + - highlightClones + - StartracDiversity + +- title: "Data" + desc: "Reference data for package functions." +- contents: + - contig_list + - mini_contig_list + - scRep_example + +deploy: + install_metadata: true \ No newline at end of file diff --git a/index.md b/index.md new file mode 100644 index 00000000..17574b63 --- /dev/null +++ b/index.md @@ -0,0 +1,65 @@ +# scRepertoire + +[![BioC status](http://www.bioconductor.org/shields/build/release/bioc/scRepertoire.svg)](https://bioconductor.org/checkResults/release/bioc-LATEST/scRepertoire) +[![R-CMD-check](https://github.com/ncborcherding/scRepertoire/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ncborcherding/scRepertoire/actions/workflows/R-CMD-check.yaml) +[![Codecov test coverage](https://codecov.io/gh/ncborcherding/scRepertoire/branch/master/graph/badge.svg)](https://app.codecov.io/gh/ncborcherding/scRepertoire?branch=master) +[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://www.borch.dev/uploads/vignette/vignette) + + +### Introduction +Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine RNA and immune profiling, scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular single-cell R packages. + +### Applying Deep Learning to VDJ data +scRepertoire is compatible and integrated with the R packages [Trex](https://github.com/ncborcherding/Trex) for convolutional-neural-network-based autoencoding of the T cell receptor and [Ibex](https://github.com/ncborcherding/Ibex) for the B cell receptor. + +## Installation + +#### Installation of Master Branch +``` +devtools::install_github("ncborcherding/scRepertoire") +``` +#### Most up-to-date version +``` +devtools::install_github("ncborcherding/scRepertoire@dev") +``` + +#### Bioconductor + +[scRepertoire](https://www.bioconductor.org/packages/release/bioc/html/scRepertoire.html) is on Bioconductor. + +``` +if (!requireNamespace("BiocManager", quietly = TRUE)) + install.packages("BiocManager") + +BiocManager::install("scRepertoire") +``` + +## Getting Data + +Unfortunately, Github limits the size of individual files. In order to access the Seurat object paired with scRepertoire please download the .rda from [here]("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link). + +## Please Cite + +We are working on submitting the scRepertoire as a peer review article, for now: + +***Borcherding, N. & Wang, Q. 2023. scRepertoire v2: Expanded toolkit for single-cell immune repertoire analysis*** + +## Bug Reports/New Features + +#### [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) + +- If possible please include a [reproducible example](https://reprex.tidyverse.org/). +Alternatively, an example with the internal **scRep_example** and **contig_list** would +be extremely helpful. + +#### [Features or Enhancements Requests](https://github.com/ncborcherding/scRepertoire/issues) + +#### [Pull Requests](https://github.com/ncborcherding/scRepertoire/pulls) +All are welcome for bug fixes, new features, or enhancements. + +- Please set PR to merge with “dev” branch and provide description of what the PR +contains (referencing existing issue(s) if appropriate). + +## Contact +Questions, comments, suggestions, please feel free to contact Nick Borcherding via this repository, [email](mailto:ncborch@gmail.com), or using [twitter](https://twitter.com/theHumanBorch). + diff --git a/inst/pkgdown.yml b/inst/pkgdown.yml new file mode 100644 index 00000000..116d43d4 --- /dev/null +++ b/inst/pkgdown.yml @@ -0,0 +1,26 @@ +pandoc: 3.1.1 +pkgdown: 2.0.7 +pkgdown_sha: ~ +articles: + Attaching_SC: Attaching_SC.html + Clonal_Bias: Clonal_Bias.html + Clonal_Cluster: Clonal_Cluster.html + Clonal_Diversity: Clonal_Diversity.html + Clonal_Dynamics: Clonal_Dynamics.html + Clonal_Rarefaction: Clonal_Rarefaction.html + Clonal_Visualizations: Clonal_Visualizations.html + Combining_Contigs: Combining_Contigs.html + FAQ: FAQ.html + Ibex: Ibex.html + Installation: Installation.html + Loading: Loading.html + Processing: Processing.html + Repertoire_Summary: Repertoire_Summary.html + SC_Visualizations: SC_Visualizations.html + Trex: Trex.html + vignette: vignette.html +last_built: 2023-10-22T17:28Z +urls: + reference: https://ncborcherding.github.io/scRepertoire/reference + article: https://ncborcherding.github.io/scRepertoire/articles + diff --git a/man/StartracDiversity.Rd b/man/StartracDiversity.Rd index b2234539..775ef8ea 100644 --- a/man/StartracDiversity.Rd +++ b/man/StartracDiversity.Rd @@ -69,3 +69,4 @@ StartracDiversity(scRep_example, \author{ Liangtao Zheng } +\concept{SC_Functions} diff --git a/man/addVariable.Rd b/man/addVariable.Rd index 0119b1b4..88bf42fb 100644 --- a/man/addVariable.Rd +++ b/man/addVariable.Rd @@ -32,3 +32,4 @@ combined <- addVariable(combined, variables = rep(c("B", "L"), 4)) } +\concept{Loading_and_Processing_Contigs} diff --git a/man/alluvialClones.Rd b/man/alluvialClones.Rd index 659ed06d..f36492d9 100644 --- a/man/alluvialClones.Rd +++ b/man/alluvialClones.Rd @@ -71,3 +71,4 @@ alluvialClones(scRep_example, color = "ident") } +\concept{SC_Functions} diff --git a/man/clonalAbundance.Rd b/man/clonalAbundance.Rd index 905558b8..47a705d7 100644 --- a/man/clonalAbundance.Rd +++ b/man/clonalAbundance.Rd @@ -58,3 +58,4 @@ combined <- combineTCR(contig_list, clonalAbundance(combined, cloneCall = "gene", scale = FALSE) } +\concept{Visualizing_Clones} diff --git a/man/clonalBias.Rd b/man/clonalBias.Rd index 4a943193..a0ae13b1 100644 --- a/man/clonalBias.Rd +++ b/man/clonalBias.Rd @@ -70,3 +70,4 @@ clonalBias(scRep_example, } +\concept{SC_Functions} diff --git a/man/clonalCluster.Rd b/man/clonalCluster.Rd index 5489856a..e0064925 100644 --- a/man/clonalCluster.Rd +++ b/man/clonalCluster.Rd @@ -54,3 +54,4 @@ combined <- combineTCR(contig_list, sub_combined <- clonalCluster(combined[[2]], chain = "TRA", sequence = "aa") } +\concept{Visualizing_Clones} diff --git a/man/clonalCompare.Rd b/man/clonalCompare.Rd index 0d25dae1..ad95d7c8 100644 --- a/man/clonalCompare.Rd +++ b/man/clonalCompare.Rd @@ -73,3 +73,4 @@ clonalCompare(combined, cloneCall="aa") } +\concept{Visualizing_Clones} diff --git a/man/clonalDiversity.Rd b/man/clonalDiversity.Rd index 6087c82c..51d5295a 100644 --- a/man/clonalDiversity.Rd +++ b/man/clonalDiversity.Rd @@ -99,3 +99,4 @@ clonalDiversity(combined, cloneCall = "gene") \author{ Andrew Malone, Nick Borcherding } +\concept{Visualizing_Clones} diff --git a/man/clonalHomeostasis.Rd b/man/clonalHomeostasis.Rd index 6d2daeb8..9b7adaa3 100644 --- a/man/clonalHomeostasis.Rd +++ b/man/clonalHomeostasis.Rd @@ -54,3 +54,4 @@ combined <- combineTCR(contig_list, clonalHomeostasis(combined, cloneCall = "gene") } +\concept{Visualizing_Clones} diff --git a/man/clonalLength.Rd b/man/clonalLength.Rd index 4b62b70f..b22dd7a3 100644 --- a/man/clonalLength.Rd +++ b/man/clonalLength.Rd @@ -57,3 +57,4 @@ combined <- combineTCR(contig_list, clonalLength(combined, cloneCall="aa", chain = "both") } +\concept{Visualizing_Clones} diff --git a/man/clonalNetwork.Rd b/man/clonalNetwork.Rd index cef57e9f..8729becf 100644 --- a/man/clonalNetwork.Rd +++ b/man/clonalNetwork.Rd @@ -81,3 +81,4 @@ clonalNetwork(scRep_example, } } +\concept{SC_Functions} diff --git a/man/clonalOccupy.Rd b/man/clonalOccupy.Rd index 6781b647..251bbd23 100644 --- a/man/clonalOccupy.Rd +++ b/man/clonalOccupy.Rd @@ -61,3 +61,4 @@ clonalOccupy(scRep_example, x.axis = "ident") table <- clonalOccupy(scRep_example, x.axis = "ident", exportTable = TRUE) } +\concept{SC_Functions} diff --git a/man/clonalOverlap.Rd b/man/clonalOverlap.Rd index 28272987..57de979b 100644 --- a/man/clonalOverlap.Rd +++ b/man/clonalOverlap.Rd @@ -75,3 +75,4 @@ clonalOverlap(combined, method = "jaccard") } +\concept{Visualizing_Clones} diff --git a/man/clonalOverlay.Rd b/man/clonalOverlay.Rd index 836da39e..05480d03 100644 --- a/man/clonalOverlay.Rd +++ b/man/clonalOverlay.Rd @@ -55,3 +55,4 @@ clonalOverlay(scRep_example, \author{ Francesco Mazziotta, Nick Borcherding } +\concept{SC_Functions} diff --git a/man/clonalProportion.Rd b/man/clonalProportion.Rd index ff627394..7b940864 100644 --- a/man/clonalProportion.Rd +++ b/man/clonalProportion.Rd @@ -6,7 +6,7 @@ \usage{ clonalProportion( df, - clonal.split = c(10, 100, 1000, 10000, 30000, 1e+05), + clonalSplit = c(10, 100, 1000, 10000, 30000, 1e+05), cloneCall = "strict", chain = "both", group.by = NULL, @@ -18,7 +18,7 @@ clonalProportion( \item{df}{The product of \code{\link{combineTCR}}, \code{\link{combineBCR}}, or \code{\link{combineExpression}}.} -\item{clonal.split}{The cut points for the specific clonotypes.} +\item{clonalSplit}{The cut points for the specific clonotypes.} \item{cloneCall}{How to call the clonotype - VDJC gene (gene), CDR3 nucleotide (nt), CDR3 amino acid (aa), or @@ -40,7 +40,7 @@ ggplot of the space occupied by the specific rank of clonotypes \description{ This function calculates the relative clonal space occupied by the clonotypes. The grouping of these clonotypes is based on the parameter -**clonal.split**, at default, **clonal.split** will group the clonotypes +**clonalSplit**, at default, **clonalSplit** will group the clonotypes into bins of 1:10, 11:100, 101:1001, etc. To adjust the clonotypes selected, change the numbers in the variable split. If a matrix output for the data is preferred, set exportTable = TRUE. @@ -53,3 +53,4 @@ combined <- combineTCR(contig_list, clonalProportion(combined, cloneCall = "gene") } +\concept{Visualizing_Clones} diff --git a/man/clonalQuant.Rd b/man/clonalQuant.Rd index fb3e23de..1bc97e7e 100644 --- a/man/clonalQuant.Rd +++ b/man/clonalQuant.Rd @@ -52,3 +52,4 @@ combined <- combineTCR(contig_list, clonalQuant(combined, cloneCall="strict", scale = TRUE) } +\concept{Visualizing_Clones} diff --git a/man/clonalRarefaction.Rd b/man/clonalRarefaction.Rd index 852d96a8..b06c543b 100644 --- a/man/clonalRarefaction.Rd +++ b/man/clonalRarefaction.Rd @@ -59,3 +59,4 @@ clonalRarefaction(combined[c(1,2)], cloneCall = "gene", n.boots = 3) } +\concept{Visualizing_Clones} diff --git a/man/clonalScatter.Rd b/man/clonalScatter.Rd index 8b808f48..2999175d 100644 --- a/man/clonalScatter.Rd +++ b/man/clonalScatter.Rd @@ -62,3 +62,4 @@ clonalScatter(combined, graph = "proportion") } +\concept{Visualizing_Clones} diff --git a/man/clonalSizeDistribution.Rd b/man/clonalSizeDistribution.Rd index e0d7bec9..db6c6730 100644 --- a/man/clonalSizeDistribution.Rd +++ b/man/clonalSizeDistribution.Rd @@ -59,7 +59,7 @@ Where: \item{\eqn{\xi} is a shape parameter} \item{\eqn{x \ge \mu} if \eqn{\xi \ge 0} and \eqn{\mu \le x \le \mu - \sigma/\xi} if \eqn{\xi < 0}} } - + The probability density function (pdf) for the Gamma Distribution is given by: \deqn{f(x|\alpha, \beta) = \frac{x^{\alpha-1} e^{-x/\beta}}{\beta^\alpha \Gamma(\alpha)}}{ f(x|alpha, beta) = (x^(alpha-1) * exp(-x/beta)) / (beta^alpha * Gamma(alpha)) @@ -84,3 +84,4 @@ clonalSizeDistribution(combined, cloneCall = "strict", method="ward.D2") \author{ Hillary Koch } +\concept{Visualizing_Clones} diff --git a/man/combineBCR.Rd b/man/combineBCR.Rd index 510d9372..b1fd8c19 100644 --- a/man/combineBCR.Rd +++ b/man/combineBCR.Rd @@ -58,7 +58,7 @@ with "LD" in the CTstrict header. BCR <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv") combined <- combineBCR(BCR, samples = "Patient1", - ID = "Time1", threshold = 0.85) } +\concept{Loading_and_Processing_Contigs} diff --git a/man/combineExpression.Rd b/man/combineExpression.Rd index a34c8d5b..d51c858d 100644 --- a/man/combineExpression.Rd +++ b/man/combineExpression.Rd @@ -77,3 +77,4 @@ sce <- Seurat::as.SingleCellExperiment(sce) sce <- combineExpression(combined, sce) } +\concept{SC_Functions} diff --git a/man/combineTCR.Rd b/man/combineTCR.Rd index da0b240c..9792296d 100644 --- a/man/combineTCR.Rd +++ b/man/combineTCR.Rd @@ -46,3 +46,4 @@ combined <- combineTCR(contig_list, "P19B","P19L", "P20B", "P20L")) } +\concept{Loading_and_Processing_Contigs} diff --git a/man/contig_list.Rd b/man/contig_list.Rd index b2440c3f..79532b51 100644 --- a/man/contig_list.Rd +++ b/man/contig_list.Rd @@ -9,3 +9,4 @@ filter_contig_annotation files.} A data set of T cell contigs as a list outputed from the filter_contig_annotation files. } +\concept{Data} diff --git a/man/createHTOContigList.Rd b/man/createHTOContigList.Rd index 2f3d3a8c..a6dbd570 100644 --- a/man/createHTOContigList.Rd +++ b/man/createHTOContigList.Rd @@ -40,3 +40,4 @@ contig.list <- createHTOContigList(contig = filtered.contig, } } +\concept{Loading_and_Processing_Contigs} diff --git a/man/exportClones.Rd b/man/exportClones.Rd index 15ba0c6f..d4fe4030 100644 --- a/man/exportClones.Rd +++ b/man/exportClones.Rd @@ -44,3 +44,4 @@ exportClones(combined) \author{ Jonathan Noonan, Nick Borcherding } +\concept{Loading_and_Processing_Contigs} diff --git a/man/figures/screpertoire_hex.png b/man/figures/screpertoire_hex.png new file mode 100644 index 0000000000000000000000000000000000000000..fca2897f4904879fd9e06414c1e6921e12374f2d GIT binary patch literal 210069 zcmb@uWmr|w7A{PubV>`-9n#VW(jXlQA|>4+NQac5AkryHN~g3)NFynYbf*e}cdos` zbIyJ4^Zoi1W$(>eGsYb8j(3d33k@{|94rbf1Ox<}`-*Z}2nZ;g;NLI?8u)~2#?A(O zLv+r(dI>B#Iy!NeCzhgGa`&!o 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T@y1Gt8vOY3*Q~7Z6lMJl$oYYO literal 0 HcmV?d00001 diff --git a/man/getCirclize.Rd b/man/getCirclize.Rd index 86d5baed..2f31f2e1 100644 --- a/man/getCirclize.Rd +++ b/man/getCirclize.Rd @@ -54,3 +54,4 @@ circles <- getCirclize(scRep_example, \author{ Dillon Corvino, Nick Borcherding } +\concept{SC_Functions} diff --git a/man/highlightClones.Rd b/man/highlightClones.Rd index 1c209975..81f3d022 100644 --- a/man/highlightClones.Rd +++ b/man/highlightClones.Rd @@ -43,3 +43,4 @@ scRep_example <- highlightClones(scRep_example , cloneCall= "aa", sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF")) } +\concept{SC_Functions} diff --git a/man/loadContigs.Rd b/man/loadContigs.Rd index 65ddd472..c88643e7 100644 --- a/man/loadContigs.Rd +++ b/man/loadContigs.Rd @@ -19,9 +19,7 @@ List of contigs for further processing in scRepertoire This function generates a contig list and formats the data to allow for function with \code{\link{combineTCR}} or \code{\link{combineBCR}}. If using data derived from filtered outputs of 10X Genomics, there is no -need to use this function as the data is already compatible. The function -assumes if listing multiple directories, there are distinct outputs with -unmodified file names in them. +need to use this function as the data is already compatible. } \details{ The files that this function parses includes: @@ -30,7 +28,7 @@ The files that this function parses includes: \item AIRR = "airr_rearrangement.tsv" \item BD = "Contigs_AIRR.tsv" \item JSON = ".json" - \item MiXCR = "clones.txt" + \item MiXCR = "clones.tsv" \item Omniscope = ".csv" \item TRUST4 = "barcode_report.tsv" \item WAT3R = "barcode_results.csv" @@ -48,3 +46,4 @@ WAT3R <- read.csv("https://www.borch.dev/uploads/contigs/WAT3R_contigs.csv") contig.list <- loadContigs(WAT3R, format = "WAT3R") } +\concept{Loading_and_Processing_Contigs} diff --git a/man/mini_contig_list.Rd b/man/mini_contig_list.Rd index 7e33a720..c754c39a 100644 --- a/man/mini_contig_list.Rd +++ b/man/mini_contig_list.Rd @@ -18,4 +18,5 @@ which correspond to the same barcodes found in `scRep_example` \seealso{ \code{\link{contig_list}} } +\concept{Data} \keyword{datasets} diff --git a/man/percentAA.Rd b/man/percentAA.Rd index a7cdf2d4..cf481fc5 100644 --- a/man/percentAA.Rd +++ b/man/percentAA.Rd @@ -41,3 +41,4 @@ combined <- combineTCR(contig_list, "P19B","P19L", "P20B", "P20L")) percentAA(combined, chain = "TRB", aa.length = 20) } +\concept{Summarize_Repertoire} diff --git a/man/percentGenes.Rd b/man/percentGenes.Rd index 75b069b9..d2b9a61b 100644 --- a/man/percentGenes.Rd +++ b/man/percentGenes.Rd @@ -42,3 +42,4 @@ combined <- combineTCR(contig_list, percentGenes(combined, chain = "TRB", gene = "Vgene") } +\concept{Summarize_Repertoire} diff --git a/man/percentKmer.Rd b/man/percentKmer.Rd index e48215ed..6f91ce90 100644 --- a/man/percentKmer.Rd +++ b/man/percentKmer.Rd @@ -49,3 +49,4 @@ combined <- combineTCR(contig_list, "P19B","P19L", "P20B", "P20L")) percentKmer(combined, chain = "TRB", motif.length = 3) } +\concept{Summarize_Repertoire} diff --git a/man/percentVJ.Rd b/man/percentVJ.Rd index 56a9310d..7e765ef1 100644 --- a/man/percentVJ.Rd +++ b/man/percentVJ.Rd @@ -39,3 +39,4 @@ combined <- combineTCR(contig_list, percentVJ(combined, chain = "TRB") } +\concept{Summarize_Repertoire} diff --git a/man/scRepertoire-package.Rd b/man/scRepertoire-package.Rd index 01804a81..c4a5d36d 100644 --- a/man/scRepertoire-package.Rd +++ b/man/scRepertoire-package.Rd @@ -7,6 +7,13 @@ \title{scRepertoire: A toolkit for single-cell immune receptor profiling} \description{ scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. +} +\seealso{ +Useful links: +\itemize{ + \item \url{https://ncborcherding.github.io/scRepertoire/} +} + } \author{ \strong{Maintainer}: Nick Borcherding \email{ncborch@gmail.com} diff --git a/man/screp_example.Rd b/man/screp_example.Rd index 636e648a..d0923146 100644 --- a/man/screp_example.Rd +++ b/man/screp_example.Rd @@ -9,3 +9,4 @@ from 4 patients with acute respiratory distress (ARDS).} The object is compatible with `contig_list` and the TCR sequencing data can be added with `combineExpression`. } +\concept{Data} diff --git a/man/subsetContig.Rd b/man/subsetClones.Rd similarity index 71% rename from man/subsetContig.Rd rename to man/subsetClones.Rd index eb645fa7..05f7bb31 100644 --- a/man/subsetContig.Rd +++ b/man/subsetClones.Rd @@ -1,10 +1,10 @@ % Generated by roxygen2: do not edit by hand -% Please edit documentation in R/subsetContig.R -\name{subsetContig} -\alias{subsetContig} -\title{Subset the product of combineTCR() combineBCR()} +% Please edit documentation in R/subsetClone.R +\name{subsetClones} +\alias{subsetClones} +\title{Subset the product of combineTCR() or combineBCR()} \usage{ -subsetContig(df, name, variables = NULL) +subsetClones(df, name, variables = NULL) } \arguments{ \item{df}{The product of \code{\link{combineTCR}} or \code{\link{combineBCR}}.} @@ -26,6 +26,7 @@ x <- contig_list combined <- combineTCR(contig_list, samples = c("P17B", "P17L", "P18B", "P18L", "P19B","P19L", "P20B", "P20L")) -subset <- subsetContig(combined, name = "sample", variables = c("P17B")) +subset <- subsetClones(combined, name = "sample", variables = c("P17B")) } +\concept{Loading_and_Processing_Contigs} diff --git a/man/vizGenes.Rd b/man/vizGenes.Rd index 075625b8..b555119d 100644 --- a/man/vizGenes.Rd +++ b/man/vizGenes.Rd @@ -60,3 +60,4 @@ vizGenes(combined, plot = "heatmap") } +\concept{Visualizing_Clones} diff --git a/vignettes/articles/Attaching_SC.Rmd b/vignettes/articles/Attaching_SC.Rmd new file mode 100644 index 00000000..4aa69491 --- /dev/null +++ b/vignettes/articles/Attaching_SC.Rmd @@ -0,0 +1,122 @@ +--- +title: "Combining Contigs into Clones" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Combining Contigs into Clones} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +suppressMessages(library(scater)) +suppressMessages(library(Seurat)) + +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + +googledrive::drive_download("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link", overwrite = TRUE) +``` + +The data in the scRepertoire package is derived from a [study](https://pubmed.ncbi.nlm.nih.gov/33622974/) of acute respiratory stress disorder in the context of bacterial and COVID-19 infections. The internal single cell data (`scRep_example()`) built in to scRepertoire is randomly sampled 500 cells from the fully integrated Seurat object to minimize the package size. However for the purpose of the vignette we will use the full single-cell object with almost 13,000 cells. We will use both Seurat and Single Cell Experiment (SCE) with scater to perform further visualizations in tandem. + +## Quick Pre-processing of the Single-Cell Object + +```{r} +scRep_example <- readRDS("scRep_example_full.rds") + +#Making a Single-Cell Experiment object +sce <- Seurat::as.SingleCellExperiment(scRep_example) +``` + + +## combineExpression + +After processing the contig data into clones via `combineBCR()` or `combineTCR()`, we can add the clonal information to the single-cell object using `combineExpression()`. + +**Importantly**, the major requirement for the attachment is matching contig cell barcodes and barcodes in the row names of the meta data of the Seurat or Single-Cell Experiment object. If these do not match, the attachment will fail. Based on ease, we suggest making changes to the single-cell object barcodes. + +### Calculating cloneSize + +Part of `combineExpression()` is calculating the clonal frequency and proportion, placing each clone into groups called **cloneSize**. The default **cloneSize** argument uses the following bins: c(Rare = 1e-4, Small = 0.001, Medium = 0.01, Large = 0.1, Hyperexpanded = 1), which can be modified to include more/less bins or different names. Clonal frequency and proportion is dependent on the repertoires being compared, which we can modify the calculation using the **group.by** parameter, such as grouping by the *Patient* variable from above. If **group.by** is not set, `combineExpression()` will calculate clonal frequency, proportion, and **cloneSize** as a function of individual sequencing runs. In addition, **cloneSize** can use the frequency of clones when **proportion** = FALSE. + +We can check the default cloneSize groupings using the Single-Cell Experiment object we just created above with using **group.by** set to the *sample* variable used in ```combineTCR()``` : + +```{r} +sce <- combineExpression(combined.TCR, + sce, + cloneCall="gene", + group.by = "sample", + proportion = TRUE) + +#Define color palette +colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) + +plotUMAP(sce, colour_by = "cloneSize") + + scale_color_manual(values=rev(colorblind_vector[c(1,3,5,7)])) +``` + +Alternatively, if we want **cloneSize** to be based on the frequency of the clone, we can set **proportion** = FALSE and we will need to change the **cloneSize** bins to integers. If we have not inspected our clone data, setting the upper limit of the clonal frequency might be difficult - ```combineExpression()``` will automatically adjust the upper limit to fit the distribution of the frequencies. To demonstrate this, check out the Seurat object below: + +```{r} +scRep_example <- combineExpression(combined.TCR, + scRep_example, + cloneCall="gene", + group.by = "sample", + proportion = FALSE, + cloneSize=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=500)) + +Seurat::DimPlot(scRep_example, group.by = "cloneSize") + + scale_color_manual(values=rev(colorblind_vector[c(1,3,4,5,7)])) +``` + +### Combining both TCR and BCR + +If we have TCR/BCR enrichment or want to add info for gamma-delta and alpha-beta T cells, we can make a single list and use ```combineExpression()```. + +**Major note** if there are duplicate barcodes (if a cell has both Ig and TCR), the immune receptor information will not be added. It might be worth checking cluster identities and removing incongruent barcodes in the products of ```combineTCR()``` and ```combineBCR()```. As an anecdote, the [testing data](https://support.10xgenomics.com/single-cell-vdj/datasets/6.0.1/SC5v2_Melanoma_5Kcells_Connect_single_channel_SC5v2_Melanoma_5Kcells_Connect_single_channel) we used to improve this function had 5-6% of barcode overlap. + +```{r, eval=FALSE} +#This is an example of the process, which will not be evaluated during knit +TCR <- combineTCR(...) +BCR <- combineBCR(...) +list.receptors <- c(TCR, BCR) + + +seurat <- combineExpression(list.receptors, + seurat, + cloneCall="gene", + proportion = TRUE) +``` diff --git a/vignettes/articles/Clonal_Bias.Rmd b/vignettes/articles/Clonal_Bias.Rmd new file mode 100644 index 00000000..85f908c8 --- /dev/null +++ b/vignettes/articles/Clonal_Bias.Rmd @@ -0,0 +1,100 @@ +--- +title: "Clonal Bias" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Clonal Bias} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +suppressMessages(library(Seurat)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + +scRep_example <- readRDS("scRep_example_full.rds") + +scRep_example <- combineExpression(combined.TCR, + scRep_example, + cloneCall="gene", + group.by = "sample") + +#Adding patient information +scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) + +#Adding type information +scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) + +#Defining colors +colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) +``` + + +## StartracDiversity + +From the excellent work by Lei Zhang, et al. in [Lineage tracking reveals dynamic relationships of T cells in colorectal cancer](https://www.nature.com/articles/s41586-018-0694-x), the authors introduce new methods for looking at clonotypes by cellular origins and cluster identification. Their [startrac](https://github.com/Japrin/STARTRAC) software has been adapted to work with scRepertoire and please read and cite their excellent work. + +In order to use the ```StartracDiversity()``` function, you will need to include the product of the ```combinedExpression()``` function. The second requirement is a column header in the meta data of the Seurat object that has tissue of origin. In the example data, **type** corresponds to the column "Type", which includes the "P" and "T" classifiers. The indices can be subsetted for a specific patient or examined overall using the **by** variable. Importantly, the function uses only the strict definition of a clonotype of the VDJC genes and the CDR3 nucleotide sequence. + +**The indices output includes:** ++ expa - Clonal Expansion ++ migr - Cross-tissue Migration ++ tran - State Transition + +```{r} +StartracDiversity(scRep_example, + type = "Type", + group.by = "Patient") +``` + +## clonalBias + +A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/35829695/), ```clonalBias()```, like Startrac is a clonal metric that seeks to quantify how individual clones are skewed towards a specific cellular compartment or cluster. + +**split.by** +* Variable used to calculate the baseline frequencies + +**group.by** +* The compartment/variable used for the purpose of the comparison + +```{r, message = FALSE} +clonalBias(scRep_example, + cloneCall = "aa", + split.by = "Patient", + group.by = "seurat_clusters", + n.boots = 10, + min.expand =5) +``` diff --git a/vignettes/articles/Clonal_Cluster.Rmd b/vignettes/articles/Clonal_Cluster.Rmd new file mode 100644 index 00000000..8ba8723a --- /dev/null +++ b/vignettes/articles/Clonal_Cluster.Rmd @@ -0,0 +1,133 @@ +--- +title: "Clustering by Edit Distance" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Clustering by Edit Distance} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +suppressMessages(library(Seurat)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + +googledrive::drive_download("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link", overwrite = TRUE) + +scRep_example <- readRDS("scRep_example_full.rds") + +scRep_example <- combineExpression(combined.TCR, + scRep_example, + cloneCall="gene", + group.by = "sample") +``` + +## clonalCluster + +The nucleotide or amino acid sequences of the chains can be used to cluster clonotypes by examining the edit distance of the sequences. This approach is underlying the `combineBCR()` function but now can be applied to the B or T cell receptors at the level of nucleotides (**sequence** = "nt) or amino acids (**sequence** = "aa). It will add a cluster to the end of each list element by generating a network connected by the similarity in sequence. This network is directed by the threshold variable, where 0.85 is the normalized mean edit distance. + +Clonotype cluster by distance: + +* TCRA:Cluster.1 [chain:] + :Cluster + [number] +* TCRA.123 [chain:] + [number] + +Cluster denotes if the cluster was called using the normalized Levenshtein distance, which takes the edit distance calculated between 2 sequences and divides that by the mean of the sequence lengths. + +### Basic use: + +```{r} +sub_combined <- clonalCluster(combined.TCR[[2]], + chain = "TRA", + sequence = "aa", + threshold = 0.85, + group.by = NULL) + +head(sub_combined) +``` + +If performed over the number of samples, such as the list elements, **group.by** can used to calculate only the clusters on the setting of patient sample (**group.by** = "Patient") or tissue type (**group.by** = "Type"). This will add the selected group to the beginning of the cluster designation. We can also call ```clonalCluster()``` directly on a Single-Cell Object. + +### Clustering with attached data: + +```{r} +#Adding patient information +scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) + +#Adding type information +scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) + +#Define color palette +colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) + +scRep_example <- clonalCluster(scRep_example, + chain = "TRA", + sequence = "aa", + threshold = 0.85, + group.by = "Patient") + +DimPlot(scRep_example, group.by = "TRA_cluster") + + scale_color_manual(values = hcl.colors(n=length(unique(scRep_example@meta.data[,"TRA_cluster"])), "inferno")) + + NoLegend() +``` + +Using ```clonalCluster()```, we can also return an igraph object of all the related sequences using **exportGraph** = TRUE. The returned igraph object contains only the sequences that have at least one connection with another sequence. The igraph can then be directly visualized (below) or used for downstream analysis (see the igraph [website](https://r.igraph.org/). + +### Returning an igraph object: + +```{r} +#Clustering Patient 19 samples +igraph.object <- clonalCluster(combined.TCR[c(5,6)], + chain = "TRB", + sequence = "aa", + group.by = "sample", + threshold = 0.85, + exportGraph = TRUE) + +#Setting color scheme +col_legend <- factor(igraph::V(igraph.object)$group) +col_samples <- hcl.colors(3,"inferno")[as.numeric(col_legend)] +color.legend <- factor(unique(igraph::V(igraph.object)$group)) + +#Plotting +plot( + igraph.object, + vertex.size = sqrt(igraph::V(igraph.object)$size), + vertex.label = NA, + edge.arrow.size = .25, + vertex.color = col_samples +) +legend("topleft", legend = levels(color.legend), pch = 16, col = unique(col_samples), bty = "n") +``` \ No newline at end of file diff --git a/vignettes/articles/Clonal_Diversity.Rmd b/vignettes/articles/Clonal_Diversity.Rmd new file mode 100644 index 00000000..25d2f268 --- /dev/null +++ b/vignettes/articles/Clonal_Diversity.Rmd @@ -0,0 +1,123 @@ +--- +title: "Comparing Clonal Diversity" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Comparing Clonal Diversity} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +``` + +## clonalDiversity + +Diversity can also be measured for samples or by other variables. Diversity **metrics** calculated, include: **"shannon"**, **"inv.simpson"**, **"norm.entropy"**, **"gini.simpson"**, **"chao1"**, and **"ACE"**. Please see the manual for more information on each metric and the underlying calculations. + +Inherent in diversity calculations is a bias increasing diversity with increasing repertoire size. ```clonalDiversity()``` will automatically downsample to the smallest repertoire size and perform boostapping to return the mean diversity estimates. If the output of diversity values are strange or minimally variable, it is likely due to a sample with small repertoire size. + +**n.boots** +The number of calculations to perform **(default = 100)**. + +**return.boots** +- TRUE: Return all the calculations. +- FALSE: Return only the mean values **(default)**. + +**skip.boots** +Skip the bootstrapping calculations. + + +```{r} +clonalDiversity(combined.TCR, + cloneCall = "gene", + n.boots = 20) + +#Return only a subset of metrics +clonalDiversity(combined.TCR, + metrics = c("shannon", "ACE"), + cloneCall = "gene", + n.boots = 20) +``` + +## clonalSizeDistribution + +Another method for modeling the repertoire distribution is a discrete gamma-GPD spliced threshold model, proposed by [Koch et al.](https://pubmed.ncbi.nlm.nih.gov/30485278/) The spliced model models the repertoire and allows for the application of a power law distribution for larger clonal-expanded sequences and a Poisson distribution for smaller clones. After fitting the models, repertoires can be compared using Euclidean distance. + +If using this function, please read/cite [Koch et al.](https://pubmed.ncbi.nlm.nih.gov/30485278/) and check out the [powerTCR](https://bioconductor.org/packages/release/bioc/html/powerTCR.html) R package. + +```{r} +clonalSizeDistribution(combined.TCR, + cloneCall = "aa", + method= "ward.D2") +``` + +## clonalOverlap + +If you are interested in measures of similarity between the samples loaded into scRepertoire, using `clonalOverlap()` can assist in the visualization. + +The methods for calculating overlap are described below: + +Overlap Coefficient: +\deqn{overlap = \frac{\sum \min(a_i, b_i)}{\min(\sum a_i, \sum b_i)}} + +Raw Count Overlap: +\deqn{raw = \sum \min(a_i, b_i)} + +Morisita Index: +\deqn{morisita = \frac{\sum a_i b_i}{(\sum a_i)(\sum b_i)}} + +Jaccard Index: +\deqn{jaccard = \frac{\sum \min(a_i, b_i)}{\sum a_i + \sum b_i - \sum \min(a_i, b_i)}} + +Cosine Similarity: +\deqn{cosine = \frac{\sum a_i b_i}{\sqrt{(\sum a_i^2)(\sum b_i^2)}}} + +Where: +\itemize{ +\item{\eqn{a_i} and \eqn{b_i} are the abundances of species \eqn{i} in groups A and B, respectively.} +} + +```{r} +clonalOverlap(combined.TCR, + cloneCall = "strict", + method = "morisita") + +clonalOverlap(combined.TCR, + cloneCall = "strict", + method = "raw") +``` \ No newline at end of file diff --git a/vignettes/articles/Clonal_Dynamics.Rmd b/vignettes/articles/Clonal_Dynamics.Rmd new file mode 100644 index 00000000..af5a2e6f --- /dev/null +++ b/vignettes/articles/Clonal_Dynamics.Rmd @@ -0,0 +1,93 @@ +--- +title: "Visualizing Clonal Dynamics" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Visualizing Clonal Dynamics} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +``` + + +After completing the basic processing and summary functions in scRepertoire, we can explore the clonotypes of the single-cell data in more detail. + +## clonalHomeostasis + +By examining the clonal space, we effectively look at the relative space occupied by clones at specific proportions. Another way to think about this would be to think of the total immune receptor sequencing run as a measuring cup. In this cup, we will fill liquids of different viscosity - or different numbers of clonal proportions. Clonal space homeostasis asks what percentage of the cup is filled by clones in distinct proportions (or liquids of different viscosity, to extend the analogy). The proportional cut points are set under the **cloneSize** variable in the function and can be adjusted. + +**cloneSize** +* Rare = 0.0001 +* Small = 0.001 +* Medium = 0.01 +* Large = 0.1 +* Hyperexpanded = 1 + +```{r} +clonalHomeostasis(combined.TCR, + cloneCall = "gene") + +clonalHomeostasis(combined.TCR, + cloneCall = "gene", + cloneSize = c(Rare = 0.001, Small = 0.01, Medium = 0.1, Large = 0.3, Hyperexpanded = + 1)) +``` + +## clonalProportion + +Like clonal space homeostasis above, clonal proportion places clones into separate bins. The key difference is that instead of looking at the relative proportion of the clone to the total, the `clonalProportion()` function will rank the clones by total number and place them into bins. + +The **clonalSplit** represents the ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **clonalSplit** variable in the function and can be adjusted, but they are as follows at baseline. + +**clonalSplit** +* 10 +* 100 +* 1000 +* 10000 +* 30000 +* 100000 + +```{r} +clonalProportion(combined.TCR, + cloneCall = "gene") + +clonalProportion(combined.TCR, + cloneCall = "nt", + clonalSplit = c(1, 5, 10, 100, 1000, 10000)) +``` \ No newline at end of file diff --git a/vignettes/articles/Clonal_Rarefaction.Rmd b/vignettes/articles/Clonal_Rarefaction.Rmd new file mode 100644 index 00000000..18a9762f --- /dev/null +++ b/vignettes/articles/Clonal_Rarefaction.Rmd @@ -0,0 +1,120 @@ +--- +title: "Clonal Rarefaction and Extrapolation" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Clonal Rarefaction and Extrapolation} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +``` + +## clonaRarefaction + +We can also use Hill numbers to estimate the rarefaction, or estimating species richness, using the the abundance of clones across groupings. Underlying the rarefaction calculation is the use of observed receptor of abundance to compute diversity. + +**hill.numbers** ++ 0 - species-richness ++ 1 - Shannon ++ 2 - Simpson + +**plot.type** ++ 1 - sample-size-based rarefaction/extrapolation ++ 2 - sample completeness curve ++ 3 - coverage-based rarefaction/extrapolation curve + +This relies on the [iNEXT](https://cran.r-project.org/web/packages/iNEXT/index.html) with the accompanying [manuscript](http://chao.stat.nthu.edu.tw/wordpress/paper/120_pdf_appendix.pdf). Like the other wrapping functions in scRepetoire, please cite the original work. + +### Rarefaction using Shannon Diversity (q = 0) + +```{r, message=FALSE} +clonalRarefaction(combined.TCR, + plot.type = 1, + hill.numbers = 0, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 2, + hill.numbers = 0, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 3, + hill.numbers = 0, + n.boots = 2) +``` + + +### Rarefaction using Shannon Diversity (q = 1) + +```{r} +clonalRarefaction(combined.TCR, + plot.type = 1, + hill.numbers = 1, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 2, + hill.numbers = 1, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 3, + hill.numbers = 1, + n.boots = 2) +``` + +### Rarefaction using Simpson Diversity (q = 2) + +```{r} +clonalRarefaction(combined.TCR, + plot.type = 1, + hill.numbers = 2, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 2, + hill.numbers = 2, + n.boots = 2) + +clonalRarefaction(combined.TCR, + plot.type = 3, + hill.numbers = 2, + n.boots = 2) +``` diff --git a/vignettes/articles/Clonal_Visualizations.Rmd b/vignettes/articles/Clonal_Visualizations.Rmd new file mode 100644 index 00000000..0c621144 --- /dev/null +++ b/vignettes/articles/Clonal_Visualizations.Rmd @@ -0,0 +1,205 @@ +--- +title: "Basic Clonal Visualizations" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Basic Clonal Visualizations} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +combined.TCR <- addVariable(combined.TCR, + name = "Type", + variables = rep(c("B", "L"), 4)) +``` + +## clonalQuant + +The first function to explore the clonotypes is `clonalQuant()` to return the total or relative numbers of unique clonotypes. + +**scale** +* TRUE - relative percent of unique clonotypes scaled by the total size of the clonal repertoire +* FALSE - Report the total number of unique clonotypes + +**chain** ++ "both" for combined chain visualization ++ "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" to select single chain + +```{r} +clonalQuant(combined.TCR, + cloneCall="strict", + chain = "both", + scale = TRUE) +``` + +For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme: ++ Change the **palette** parameter to the desired color scheme. This approach uses the built-in palettes of grDevices and we can access the list of color schemes using ``hcl.pals()``. ++ Add a ggplot layer with a new color scheme. + +```{r} +clonalQuant(combined.TCR, + cloneCall="strict", + chain = "both", + scale = TRUE, + palette = "Zissou 1") + +clonalQuant(combined.TCR, + cloneCall="strict", + chain = "both", + scale = TRUE) + + scale_fill_manual(values = hcl.colors(8,"geyser")) +``` + +Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph instead of the visual output. + +```{r} +clonalQuant_output <- clonalQuant(combined.TCR, + cloneCall="strict", + scale = TRUE, + exportTable = TRUE) +clonalQuant_output +``` + +Another option here is to be able to define the visualization by data classes. Here, we used the `combineTCR()` to define the **Type** variable as part of the naming structure. We can use the **group.by** to specifically use a column in the data set to organize the visualization. + +```{r} +clonalQuant(combined.TCR, + cloneCall="gene", + group.by = "Type", + scale = TRUE) +``` + +## clonalAbundance + +We can also examine the relative distribution of clonotypes by abundance. Here `clonalAbundance()` will produce a line graph with a total number of clonotypes by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the contig object using the **group.by** variable in the function. + +```{r} +clonalAbundance(combined.TCR, + cloneCall = "gene", + scale = FALSE) + +clonalAbundance(combined.TCR, + group.by = "Type", + scale = TRUE) +``` + +## clonalLength + +We can look at the length distribution of the CDR3 sequences by calling the `lengtheContig()` function. Importantly, unlike the other basic visualizations, the **cloneCall** can only be "nt" or "aa". Due to the method of calling clonotypes as outlined above, the length should reveal a multimodal curve, this is a product of using the **NA** for the unreturned chain sequence and multiple chains within a single barcode. + +*chain* ++ "both" for combined chain visualization ++ "TRA", "TRB", "TRD", "TRG", "IGH" or "IGL" to select single chain + +```{r} +clonalLength(combined.TCR, + cloneCall="aa", + chain = "both") + +clonalLength(combined.TCR, + cloneCall="aa", + chain = "TRA", + scale = TRUE) +``` + +## clonalCompare + +We can also look at clonotypes between samples and changes in dynamics by using the `clonalCompare()` function. + +**samples** +* Can be used to isolate specific samples based on the name of the list element + +**graph** +* "alluvial" - graph imaged below +* "area" - graph by area of the respective clonotype + +**top.clones** +* The top number of clonotypes to graph, this will be calculated based on the frequency of the individual sample. This can also be left blank. + +**clonotypes** +* Can be used to isolate specific clonotype sequences, ensure the call matches the sequences you would like to visualize. + +**highlight.clones** +* Specifically color certain clonotypes, other clones will be greyed out. + +**relabel.clones** +* Simplify the isolated clonotypes to numerical designations to allow for a tidier visualization + +```{r} +clonalCompare(combined.TCR, + top.clones = 10, + samples = c("P17B", "P17L"), + cloneCall="aa", + graph = "alluvial") +``` + +We can also choose to highlight specific clonotypes, such as in the case of *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*: + +```{r} +clonalCompare(combined.TCR, + top.clones = 10, + highlight.clones = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF"), + relabel.clones = TRUE, + samples = c("P17B", "P17L"), + cloneCall="aa", + graph = "alluvial") +``` + +## clonalScatter + +```clonalScatter()``` will organize two repertoires, quantify the relative clone sizes, and produce a scatter plot comparing the two samples. + +**x.axis** and **y.axis** +* Names of the list element to place on the x-axis and y-axis - so for example *"P18L"* and *"P18B" * + +**dot.size** +* "total" to display the total number of clones between the x- and y-axis +* Name of the list element to use for size calculation + +**graph** +* "proportion" for the relative proportion the clonotype represents across all clonotypes +* "count" for the total count of clonotypes by sample + +```{r} +clonalScatter(combined.TCR, + cloneCall ="gene", + x.axis = "P18B", + y.axis = "P18L", + dot.size = "total", + graph = "proportion") +``` \ No newline at end of file diff --git a/vignettes/articles/Combining_Contigs.Rmd b/vignettes/articles/Combining_Contigs.Rmd new file mode 100644 index 00000000..a356b19c --- /dev/null +++ b/vignettes/articles/Combining_Contigs.Rmd @@ -0,0 +1,107 @@ +--- +title: "Combining Contigs into Clones" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Combining Contigs into Clones} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +``` + +There are varying definitions of clones or clonotypes in the literature. For the purposes of scRepertoire, we will use **clone** and define this as the cells with shared/trackable complementarity-determining region 3 (CDR3) sequences. Within this definition, one might use amino acid (**aa**) sequences of one or both chains to define a clone. Alternatively, we could use nucleotide (**nt**) or the V(D)JC genes (**genes**) to define a clone. The latter **genes** would be a more permissive definition of *"clones"*, as multiple amino acid or nucleotide sequences can results from the same gene combination. Another option to define clone is use of the V(D)JC and nucleotide sequence (**strict**). scRepertoire allows for the use of all these definitions of clones and allows for users to select both or individual chains to examine. + +The first step in getting clones is to use the single-cell barcodes to organize cells into paired sequences. This is accomplished using `combineTCR()` and `combineBCR()`. + +## combineTCR + +**df** +* List of *filtered_contig_annotations.csv* data frames from the 10x Cell Ranger. +* List of data processed using `loadContigs()`. + +**samples** and **ID** +* Grouping variables for downstream analysis and will be added as prefixes to +prevent issues with duplicate barcodes **(optional)**. + +**removeNA** +* TRUE - Filter to remove any cell barcode with an NA value in at least one of the chains. +* FALSE - Include and incorporate cells with 1 NA value **(default)**. + +**removeMulti** +* TRUE - Filter to remove any cell barcode with more than 2 immune receptor chains. +* FALSE - Include and incorporate cells with > 2 chains **(default)**. + +**filterMulti** +* TRUE - Isolate the top 2 expressed chains in cell barcodes with multiple chains. +* FALSE - Include and incorporate cells with > 2 chains **(default)**. + +The output of `combineTCR()` will be a list of contig data frames that will be reduced to the reads associated with a single cell barcode. It will also combine the multiple reads into clonotype calls by either the nucleotide sequence (**CTnt**), amino acid sequence (**CTaa**), the VDJC gene sequence (**CTgene**), or the combination of the nucleotide and gene sequence (**CTstrict**). + +```{r} +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L"), + removeNA = FALSE, + removeMulti = FALSE, + filterMulti = FALSE) + +head(combined.TCR[[1]]) +``` + +## combineBCR + +`combineBCR()` is analogous to `combineTCR()` with 2 major changes: **1)** Each barcode can only have a maximum of 2 sequences, if greater exists, the 2 with the highest reads are selected; **2)** The **strict** definition of a clone is based on the normalized Levenshtein edit distance of CDR3 nucleotide sequences and V-gene usage. For more information on this approach, please see the respective [citation](https://pubmed.ncbi.nlm.nih.gov/34161770/). This definition allows for the grouping of BCRs derived from the same progenitor that have undergone mutation as part of somatic hypermutation and affinity maturation. + +**threshold** +The level of similarity in sequences to group together. **Default** is 0.85. + +\[ +\text{threshold}(s, t) = 1-\frac{\text{Levenshtein}(s, t)}{\frac{\text{length}(s) + \text{length}(t)}{2}} +\] + +**call.related.clones** +Calculate the normalized edit distance (**TRUE**) or skip the calculation (**FALSE**). Skipping the edit distance calculation may save time, especially in the context of large data sets, but is not recommended. + + +```{r} +BCR.contigs <- read.csv("https://www.borch.dev/uploads/contigs/b_contigs.csv") +combined.BCR <- combineBCR(BCR.contigs, + samples = "P1", + threshold = 0.85) + +head(combined.BCR[[1]]) +``` + diff --git a/vignettes/articles/FAQ.Rmd b/vignettes/articles/FAQ.Rmd new file mode 100644 index 00000000..fdf45ab4 --- /dev/null +++ b/vignettes/articles/FAQ.Rmd @@ -0,0 +1,80 @@ +--- +title: "Frequently Asked Questions" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Frequently Asked Questions} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) +``` + +### Barcode Mismatch + +Depending on the pipeline used to generate the single-cell object, there may be inherent mismatches in the barcodes in the single-cell object and the output of ```combineBCR()``` or ```combineTCR()```. In particular, by default, Seurat will amend the suffix of the barcodes with _X, so the barcodes change like: + +``` +original: ACGTACGTACGTACGT-1 +seurat-modified: ACGTACGTACGTACGT-1_1 +``` + +scReprtoire uses the samples in combineTCR() to add a prefix to the barcodes: + +``` +original: ACGTACGTACGTACGT-1 +scRepertoire-modified: Sample1_ACGTACGTACGTACGT-1 +``` + +The easiest way to make these compatible is to rename the cell barcodes in the Seurat object by using the RenameCells(). + +```{r, eval=FALSE} +cell.barcodes <- rownames(seuratObj[[]]) +cell.barcodes <- stringr::str_split(cell.barcodes, "_", simplify = TRUE)[,1] #This is removing the _1 at the end of the barcodes) +cell.barcodes <- paste0(seuratObj$orig.ident, "_", cell.barcodes) #This is adding the prefix of the orig.ident to the barcodes, assuming that is the sample ids +seuratObj <- RenameCells(seuratObj, new.names = cell.barcodes) +``` + +### Citing scRepertoire + +***Borcherding, N. & Wang, Q. scRepertoire v2: Expanded toolkit for single-cell immune repertoire analysis*** + +### Bug Reports/New Features + +##### [GitHub issue](https://github.com/ncborcherding/scRepertoire/issues) + +If possible please include a [reproducible example](https://reprex.tidyverse.org/). +Alternatively, an example with the internal **scRep_example** and **contig_list** would +be extremely helpful. + +### Features or Enhancements Requests +Feature request can also be submitted as [GitHub issues](https://github.com/ncborcherding/scRepertoire/issues). \ No newline at end of file diff --git a/vignettes/articles/Ibex.Rmd b/vignettes/articles/Ibex.Rmd new file mode 100644 index 00000000..db62e955 --- /dev/null +++ b/vignettes/articles/Ibex.Rmd @@ -0,0 +1,214 @@ +--- +title: "Using Ibex for BCRs" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Using Ibex for BCRs} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) +suppressMessages(library(reticulate)) +use_condaenv(condaenv = "r-reticulate", required = TRUE) +suppressMessages(library(Ibex)) +suppressMessages(library(Seurat)) +suppressMessages(library(ggplot2)) +suppressMessages(library(viridis)) +suppressMessages(library(dplyr)) +``` + + +The idea behind Ibex is to combine BCR cdr3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using BCR and RNA. Ibex was designed to make a customizable approach to this combined approach using R. + +More infromation is available at the [Ibex GitHub Repo](https://github.com/ncborcherding/Ibex). + +## Installation + +```{r, eval = FALSE} +devtools::install_github("ncborcherding/Ibex") +``` + +## The Data Set + +To show the multiple options of Ibex, the example data is derived from [this manuscript](https://pubmed.ncbi.nlm.nih.gov/33891889/) - multimodal single-cell characterization of COVID19-associated multisystem inflammatory syndrome in children. The data example built into the package (ibex_example) is derived from randomly sampling cells from Patient 1. + +```{r} +SeuratObj <- readRDS(url("https://www.borch.dev/uploads/data/Ibex_FullExample.rds")) +``` + + +## Ibex.matrix Function + +Ibex has 2 major functions - the first being ```Ibex.matrix()```, which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike ```runIbex()``` below, ```Ibex.matrix()``` does not filter the input for only B cells with attached BCR data. In addition, ```Ibex.matrix()``` is compatible with the list output from the ```combineBCR()``` function from the [scRepertoire](https://github.com/ncborcherding/scRepertoire) R package, while ```runIbex()``` must be performed on a single-cell object. + +**chains** +* "Heavy" for Ig Heavy Chain +* "Light" for Ig Light Chain + +**method** +* "encoder" for a convolution neural network (CNN) based encoding. +* "geometric" for a geometric transformation + +**encoder.model** +* "VAE" for a variational autoencoder +* "AE" for a traditional autoencoder + +**encoder.input** +* "AF" to use Atchley factors +* "KF" to use Kidera factors +* "both" to use both +* "OHE" for a One Hot Autoencoder + +**theta** +If choosing the geometric transformation, what value of theta to use (default is pi) + + +```{r} +ibex_vectors <- Ibex.matrix(SeuratObj, + chains = "Light", + encoder.input = "OHE") + +qplot(data = as.data.frame(ibex_vectors), Ibex_2, Ibex_3) + theme_classic() +``` + +## runIbex + +Additionally, ```runIbex()``` can be used to append the Seurat or Single-cell Experiment object with the Ibex vectors and allow for further analysis. Importantly, ```runIbex()``` will remove single cells that do not have recovered BCR data in the metadata of the object. + +```{r} +SeuratObj <- runIbex(SeuratObj, + chains = "Heavy", + encoder.input = "KF", + reduction.name = "ibex.KF") +``` + + +## Using Ibex Vectors + +After ```runIbex()```, we have the encoded values stored under **"Ibex..."**. Using the Ibex reduction stored in Seurat, we can calculate the nearest neighbor and shared nearest neighbor indexes and generate a UMAP. + +```{r} +#Generating UMAP from ibex Neighbors +SeuratObj <- RunUMAP(SeuratObj, + reduction = "ibex.KF", + dims = 1:30, + reduction.name = 'ibex.umap', + reduction.key = 'ibexUMAP_') + +#ibex UMAP +plot1 <- DimPlot(SeuratObj, reduction = "ibex.umap") + NoLegend() +plot2 <- DimPlot(SeuratObj, group.by = "CTaa", reduction = "ibex.umap") + + scale_color_viridis(discrete = TRUE, option = "B") + + NoLegend() + +plot1 + plot2 +``` + +We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija's group [paper](https://pubmed.ncbi.nlm.nih.gov/34062119/). We will use the RNA, ADT protein levels, and ibex vectors for the WNN calculations. + +```{r} +SeuratObj <- FindMultiModalNeighbors( + SeuratObj, + reduction.list = list("pca", "apca", "ibex.KF"), + dims.list = list(1:30, 1:20, 1:30), + modality.weight.name = "RNA.weight" + ) +SeuratObj <- RunUMAP(SeuratObj, + nn.name = "weighted.nn", + reduction.name = "wnn.umap", + reduction.key = "wnnUMAP_") +SeuratObj <- FindClusters(SeuratObj, + graph.name = "wsnn", + resolution = 0.6, + algorithm = 3, verbose = FALSE) + +#WNN UMAP +plot3 <- DimPlot(SeuratObj, reduction = "wnn.umap") +plot4 <- DimPlot(SeuratObj, reduction = "wnn.umap", group.by = "CTaa") + + scale_color_viridis(discrete = TRUE, option = "B") + + NoLegend() + +plot3 + plot4 +``` + +## Comparing the outcome to just one modality + +We can also look at the differences in the UMAP generated from RNA, ADT, or Ibex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above. + +```{r} +SeuratObj <- RunUMAP(SeuratObj, + reduction = 'pca', + dims = 1:30, + assay = 'RNA', + reduction.name = 'rna.umap', + reduction.key = 'rnaUMAP_') + +SeuratObj <- RunUMAP(SeuratObj, + reduction = 'apca', + dims = 1:20, + assay = 'ADT', + reduction.name = 'adt.umap', + reduction.key = 'adtUMAP_') + +plot5 <- DimPlot(SeuratObj, reduction = "rna.umap") + NoLegend() +plot6 <- DimPlot(SeuratObj, reduction = "adt.umap") + NoLegend() +plot7 <- DimPlot(SeuratObj, reduction = "ibex.umap") + NoLegend() + +plot5 + plot6 + plot7 +``` + +# CoNGA Reduction + +Recent [work](https://pubmed.ncbi.nlm.nih.gov/34426704/) has proposed using representative cells for the characterization of clonotype and gene expression relationships. In order to generate these representative cells, either a mean expression across a clone or using the PCA dimensional space to identify a single cell that has the minimum euclidean distance across a clone. + +In order to generate a single-cell object based on the CoNGA approach, Ibex offers the function ```CoNGAfy()```. For **method**, select either "mean" or "dist" as described above. After performing ```CoNGAfy()```, the user can use any of the above reduction strategies. + +```{r} +CoNGA.seurat <- CoNGAfy(SeuratObj, + method = "dist") + +CoNGA.seurat <- runIbex(CoNGA.seurat, + encoder.input = "KF", + reduction.name = "ibex.KF") + +CoNGA.seurat <- CoNGA.seurat %>% + FindNeighbors(reduction = "ibex.KF") %>% + FindClusters(algorithm = 3) + +CoNGA.seurat <- RunUMAP(CoNGA.seurat, + reduction = "ibex.KF", + dims = 1:20, + reduction.name = 'ibex.umap', + reduction.key = 'ibexUMAP_') + +DimPlot(CoNGA.seurat, reduction = "ibex.umap") + NoLegend() +``` \ No newline at end of file diff --git a/vignettes/articles/Installation.Rmd b/vignettes/articles/Installation.Rmd new file mode 100644 index 00000000..c84a6089 --- /dev/null +++ b/vignettes/articles/Installation.Rmd @@ -0,0 +1,64 @@ +--- +title: "Installation Instructions for scRepertoire" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Installation Instructions for scRepertoire} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) +``` + +sscRepertoire requires [R](https://www.r-project.org/) version 4.0 or greater. Highly recommend installing [R Studio](https://posit.co/downloads/). + +## Install from CRAN +scRepertoire can be installed from [Bioconductor](https://www.bioconductor.org/packages/release/bioc/html/scRepertoire.html) for all platforms using standard installation methods: +``` +if (!require("BiocManager", quietly = TRUE)) + install.packages("BiocManager") + +BiocManager::install("scRepertoire") +``` + +## Release Notes +A full copy of the changes in each version can be found in the [NEWS/ChangeLog](https://ncborcherding.github.io/scRepertoire/news/index.html). + + +## GitHub Branches +scRepertoire is an active project, that recieves a number of changes based on user feedback, the most up-to-date versions are available via the GitHub repositories. These versions can be installed using ```devtools::install_github()``` or ```remotes::install_github()```. The **dev** version is the active development branch and might be less stable than the main branch or bioconductor versions. + +``` +devtools::install_github(repo = "ncborcherding/scRepertoire") + +remotes::install_github(repo = "ncborcherding/scRepertoire", ref = "dev") +``` \ No newline at end of file diff --git a/vignettes/articles/Loading.Rmd b/vignettes/articles/Loading.Rmd new file mode 100644 index 00000000..ca7c7568 --- /dev/null +++ b/vignettes/articles/Loading.Rmd @@ -0,0 +1,113 @@ +--- +title: "Loading Data into scRepertoire" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Loading Data into scRepertoire} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +``` + +## Loading and Processing Contig Data + +### What data to load into scRepertoire? + +scRepertoire functions using the **filtered_contig_annotations.csv** output from the 10x Genomics Cell Ranger. This file is located in the ./outs/ directory of the VDJ alignment folder. To generate a list of contigs to use for scRepertoire, just 1) load the **filtered_contig_annotations.csv** for each of the samples and then 2) make a list. For example: + +```{r, eval=FALSE} +S1 <- read.csv(".../Sample1/outs/filtered_contig_annotations.csv") +S2 <- read.csv(".../Sample2/outs/filtered_contig_annotations.csv") +S3 <- read.csv(".../Sample3/outs/filtered_contig_annotations.csv") +S4 <- read.csv(".../Sample4/outs/filtered_contig_annotations.csv") + +contig_list <- list(S1, S2, S3, S4) +``` + +## Other alignment workflows + +Beyond the default 10x Genomic Cell Ranger pipeline outputs, scRepertoire supports the following **single-cell** formats: + +* AIRR +* BD Rhapsody Multiomic Immune Profiling +* JSON-formatted contig data +* [MiXCR](https://pubmed.ncbi.nlm.nih.gov/25924071/) +* [Omniscope](https://www.omniscope.ai/) +* [TRUST4](https://pubmed.ncbi.nlm.nih.gov/33986545/) +* [WAT3R](https://pubmed.ncbi.nlm.nih.gov/35674381/) + +```loadContigs()``` can be given a directory where the sequencing experiments are located and it will recursively load and process the contig data based on the file names. Alternatively, ```loadContigs()``` can be given a list of data frames and process the contig data + + +```{r, eval=FALSE} +#Directory example +contig.output <- c("~/Documents/MyExperiment") +contig.list <- loadContigs(dir = contig.output, + format = "TRUST4") + +#List of data frames example +S1 <- read.csv("~/Documents/MyExperiment/Sample1/outs/barcode_results.csv") +S2 <- read.csv("~/Documents/MyExperiment/Sample2/outs/barcode_results.csv") +S3 <- read.csv("~/Documents/MyExperiment/Sample3/outs/barcode_results.csv") +S4 <- read.csv("~/Documents/MyExperiment/Sample4/outs/barcode_results.csv") + +contig_list <- list(S1, S2, S3, S4) +contig.list <- loadContigs(dir = contig.output, + format = "WAT3R") +``` + +## Multiplexed Experiment + +It is now easy to create the contig list from a multiplexed experiment by first generating a single-cell RNA object (either Seurat or Single Cell Experiment), loading the filtered contig file and then using `createHTOContigList()`. This function will return a list separated by the **group.by** variable(s). + +This function depends on the match of barcodes between the single-cell object and contigs. If there is a prefix or different suffix added to the barcode, this will result in no contigs recovered. Currently, it is recommended you do this step before the integration, as integration workflows commonly alter the barcodes. There is a **multi.run** variable that can be used on the integrated object. However, it assumes you have modified the barcodes with the Seurat pipeline (automatic addition of _# to end), and your contig list is in the same order. + +```{r, eval = F} +contigs <- read.csv(".../outs/filtered_contig_annotations.csv") + +contig.list <- createHTOContigList(contigs, Seurat.Obj, group.by = "HTO_maxID") +``` + +## Example Data in scRepertoire + +scRepertoire comes with a data set from T cells derived from four patients with acute respiratory distress to demonstrate the functionality of the R package. More information on the data set can be found in the corresponding [manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/). The samples consist of paired peripheral-blood (B) and bronchoalveolar lavage (L), effectively creating 8 distinct runs for T cell receptor (TCR) enrichment. We can preview the elements in the list by using the head function and looking at the first contig annotation. + +The built-in example data is derived from the 10x Cell Ranger pipeline, so it is ready to go for downstream processing and analysis. + +```{r} +data("contig_list") #the data built into scRepertoire + +head(contig_list[[1]]) +``` diff --git a/vignettes/articles/Processing.Rmd b/vignettes/articles/Processing.Rmd new file mode 100644 index 00000000..344596f7 --- /dev/null +++ b/vignettes/articles/Processing.Rmd @@ -0,0 +1,99 @@ +--- +title: "Additional Processing Steps" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Additional Processing Steps} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +``` + +## addVariable + +What if there are more variables to add than just sample and ID? We can add them by using the `addVariable()` function. All we need is the **name** of the variable you'd like to add and the specific character or numeric values (**variables**). As an example, here we add the **Type** in which the samples were processed and sequenced. + +```{r} +combined <- addVariable(combined.TCR, + name = "Type", + variables = rep(c("B", "L"), 4)) + +combined[[1]][1:5,ncol(combined.TCR[[1]])] # This is showing the first 5 values of the new column added +``` + +## subsetClones + +Likewise, we can remove specific list elements after `combineTCR()` using the `subsetClones()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and the variable values to subset (**variables**). Below, we isolate just the 2 sequencing results from P18L and P18B. + +Alternatively, we can also just select the list elements after `combineTCR()` or `combineBCR()`. + +```{r} +subset1 <- subsetClones(combined.TCR, + name = "sample", + variables = c("P18L", "P18B")) + +#Subset using list elements +subset2 <- combined.TCR[c(3,4)] + +head(subset1) +head(subset2) +``` + + +## exportClones + +After assigning the cloneby barcode, we can export the paired clonotypes using `exportClones()` to save for later use or to use in other pipelines. + +**write.file** +* TRUE, save the file. +* FALSE, return a data.frame. + +**dir** +directory location to save the csv + +**file.name** +the csv file name + +```{r, eval = FALSE} +exportClones(combined, + write.file = TRUE, + dir = "~/Documents/MyExperiment/Sample1/" + file.name = "clones.csv" +``` + diff --git a/vignettes/articles/Repertoire_Summary.Rmd b/vignettes/articles/Repertoire_Summary.Rmd new file mode 100644 index 00000000..d62fa562 --- /dev/null +++ b/vignettes/articles/Repertoire_Summary.Rmd @@ -0,0 +1,201 @@ +--- +title: "Basic Clonal Visualizations" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Basic Clonal Visualizations} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +``` + +## vizGenes + +A visualization of the relative usage of genes of the TCR or BCR, using `vizGenes()`. There is some functional crossover between `vizGenes()` and two functions below called `percentGenes()` and `percentVJ()`. But `vizGenes()` is more adaptable to allow for comparisons across chains, scaling, etc. + +**x.axis** ++ Gene segment to separate the x-axis, such as "TRAV", "TRBD", "IGKJ" + +**y.axis** ++ Gene segment or categorical variable to visualize along the y-axis + +**plot** ++ "barplot" for a bar chart ++ "heatmap" for a heatmap + +**scale** ++ TRUE to scale the graph by number of genes per sample ++ FALSE to report raw numbers + +**order** ++ "gene" to order by gene name ++ "variance" to order by variance between the separate variable categories + +```{r} +vizGenes(combined.TCR, + x.axis = "TRBV", + y.axis = NULL, + plot = "barplot", + scale = TRUE) +``` + +`vizGenes()` can also be used to look at the usage of genes in a single chain. For example, say we are interested in the difference in TRB V and J usage between lung and peripheral blood samples - we can easily take a look at this using the following code: + +```{r} +#Peripheral Blood +vizGenes(combined.TCR[c(1,3,5,7)], + x.axis = "TRBV", + y.axis = "TRBJ", + plot = "heatmap", + scale = TRUE) + +#Lung +vizGenes(combined.TCR[c(2,4,6,8)], + x.axis = "TRBV", + y.axis = "TRBJ", + plot = "heatmap", + scale = TRUE) +``` + +For the P17 patient samples, what if we are interested in chain pairings, we can look at TRBV and TRAV at the same time using them as inputs to **x.axis** and **y.axis**. + +```{r} +vizGenes(combined.TCR[c(1,2)], + x.axis = "TRBV", + y.axis = "TRAV", + plot = "heatmap", + scale = FALSE) +``` + + +## percentAA + +Quantify the proportion of amino acids along the cdr3 sequence with `percentAA()`. By default, the function will pad the sequences with NAs up to the maximum of **aa.length**. Sequences longer than **aa.length** will be removed before visualization. + +```{r, message = FALSE} +percentAA(combined.TCR, + chain = "TRB", + aa.length = 20) +``` + +## percentGenes + +Quantify the proportion of V or J gene usage with `percentGenes()`. Like `percentAA()`, we select the chain of interest and then indicate the gene of interest with the **gene** parameter. Two major limitations of `percentGenes()` are, 1) the function quantifies only V or J genes, and 2) the quantification of the genes are limited to all the V or J genes seen across the samples, not all possible V or J genes. + +```{r} +percentGenes(combined.TCR, + chain = "TRB", + gene = "Vgene") +``` + +We can also use the output `percentGenes()` for dimensional reduction to summarise the gene usage by sample. This can be done with a simple principal component analysis (below) or even more complex reductions. + +```{r} +df.genes <- percentGenes(combined.TCR, + chain = "TRB", + gene = "Vgene", + exportTable = TRUE) + +#Performing PCA +pc <- prcomp(df.genes) + +#Getting data frame to plot from +df <- as.data.frame(cbind(pc$x[,1:2], rownames(df.genes))) +df$PC1 <- as.numeric(df$PC1) +df$PC2 <- as.numeric(df$PC2) + +#Plotting +ggplot(df, aes(x = PC1, y = PC2)) + + geom_point(aes(fill =df[,3]), shape = 21, size = 5) + + guides(fill=guide_legend(title="Samples")) + + scale_fill_manual(values = hcl.colors(nrow(df), "inferno")) + + theme_classic() +``` + +## percentVJ + +Quantify the proportion of V and J gene usage with `percentVJ()`. Like `percentGenes()`, this function will quantify the percentage of V and J paired together across individual repertoires. The output can be visualized using a heatmap or as input for further dimensional reduction. + +```{r} +percentVJ(combined.TCR, + chain = "TRB") + +df.genes <- percentVJ(combined.TCR, + chain = "TRB", + exportTable = TRUE) + +#Performing PCA +pc <- prcomp(df.genes) + +#Getting data frame to plot from +df <- as.data.frame(cbind(pc$x[,1:2], rownames(df.genes))) +df$PC1 <- as.numeric(df$PC1) +df$PC2 <- as.numeric(df$PC2) + +#Plotting +ggplot(df, aes(x = PC1, y = PC2)) + + geom_point(aes(fill =df[,3]), shape = 21, size = 5) + + guides(fill=guide_legend(title="Samples")) + + scale_fill_manual(values = hcl.colors(nrow(df), "inferno")) + + theme_classic() +``` + +## percentKmer + +Another quantification of the composition of the CDR3 sequence is to define motifs by sliding across the amino acid or nucleotide sequences at set intervals resulting in substrings or kmers. + +**motif.length** +Numerical value for the length of the kmer. + +**top.motifs** +Display the most variable genes determined by mean absolute deviation. + +```{r} +percentKmer(combined.TCR, + cloneCall = "aa", + chain = "TRB", + motif.length = 3, + top.motifs = 25) + +percentKmer(combined.TCR, + cloneCall = "nt", + chain = "TRB", + motif.length = 3, + top.motifs = 25) +``` \ No newline at end of file diff --git a/vignettes/articles/SC_Visualizations.Rmd b/vignettes/articles/SC_Visualizations.Rmd new file mode 100644 index 00000000..ee0999c1 --- /dev/null +++ b/vignettes/articles/SC_Visualizations.Rmd @@ -0,0 +1,202 @@ +--- +title: "Visualizations for Single-Cell Objects" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Visualizations for Single-Cell Objects} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +suppressMessages(library(Seurat)) +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) +googledrive::drive_download("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link", overwrite = TRUE) + +scRep_example <- readRDS("scRep_example_full.rds") + +scRep_example <- combineExpression(combined.TCR, + scRep_example, + cloneCall="gene", + group.by = "sample") + +#Adding patient information +scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) + +#Adding type information +scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) + +#Defining colors +colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) +``` + +## clonalOverlay + +Using the dimensional reduction graphs as a reference, we can also generate an overlay of the position of clonally-expanded cells using `clonalOverlay()`. Select the **reduction** for the visualization, default is "PCA" and the **freq.cutpoint** or lowest clonal frequency or proportion to generate the contour plot. + +We can modify the contours by selecting the number of **bins** or the number of contours drawn. `clonalOverlay()` can be used to look across all cells or faceted by a meta data variable using **facet.by**. The overall dimensional reduction will be maintained as we facet, while the contour plots will adjust based on the **facet.by** variable. The coloring of the dot plot is based on the active identity of the single-cell object. + +This visualization was authored by Dr. Francesco Mazziotta from Johns Hopkins and inspired by Drs. Carmona and Andreatta and their work with [ProjectTIL](https://github.com/carmonalab/ProjecTILs), which is a great pipeline to annotated T cell subtypes. + +```{r} +clonalOverlay(scRep_example, + reduction = "umap", + freq.cutpoint = 1, + bins = 10, + facet.by = "Patient") + + guides(color = "none") +``` + +## clonalNetwork + +Similar to `clonalOverlay()`, we can look at the network interaction of clonotypes shared between clusters along the single-cell dimensional reduction using `clonalNetwork()`. This function shows the relative proportion of clones from the starting node, with the ending node indicated by the arrow. + +Filtering for clones can be accomplished using 3 methods: + +**filter.clones** +* Select a number to isolate the clones comprising the top X number of cells (filter.clones = 2000) +* Select "min" to make sure all groups are scaled to the size of the minimum group + +**filter.identity** +* For the identity chosen to visualize, show the to and from network connections for a single group + +**filter.proportion** +* Remove clones that comprise less than a certain proportion of clones in groups. + + +```{r} +#ggraph needs to be loaded due to issues with ggplot +library(ggraph) + +#No Identity filter +clonalNetwork(scRep_example, + reduction = "umap", + group.by = "seurat_clusters", + filter.clones = NULL, + filter.identity = NULL, + cloneCall = "aa") + +#Examining Cluster 2 only +clonalNetwork(scRep_example, + reduction = "umap", + group.by = "seurat_clusters", + filter.identity = 3, + cloneCall = "aa") +``` + +We can also use **exportClones** to quickly get clones that are shared across multiple identity groups, along with the total number of clones in the data set. + +```{r} +shared.clones <- clonalNetwork(scRep_example, + reduction = "umap", + group.by = "seurat_clusters", + cloneCall = "aa", + exportClones = TRUE) +head(shared.clones) +``` + +## highlightClones + +We can also look at the clonotypes by calling specific sequences in the `highlightClonotypes()` below. In order to highlight the clonotypes, we first need to use the **cloneCall**, the type of sequence we will be using, and then the specific sequences themselves using **sequence**. Below, we can see the steps to highlight the two most prominent sequences *"CVVSDNTGGFKTIF_CASSVRRERANTGELFF"* and *"NA_CASSVRRERANTGELFF"*. + +```{r} +scRep_example <- highlightClones(scRep_example, + cloneCall= "aa", + sequence = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", + "NA_CASSVRRERANTGELFF")) + +Seurat::DimPlot(scRep_example, group.by = "highlight") +``` + +## clonalOccupy + +We can also look at the count of cells by cluster assigned into specific frequency ranges by using the `clonalOccupy()` function and selecting the **x.axis** to display cluster or other variables in the meta data of the single cell object. + ++ **proportion** can be used to look at relative level groupings ++ **label** will still return the absolute number of clonotypes + +```{r} +clonalOccupy(scRep_example, + x.axis = "seurat_clusters") + +clonalOccupy(scRep_example, + x.axis = "ident", + proportion = TRUE, + label = FALSE) +``` + +## alluvialClones + +After the metadata has been modified, we can look at clones across multiple categories using the `alluvialClones()` function. We are able to use the plots to examine the interchange of categorical variables. Because this function will produce a graph with each clonotype arranged by called stratification, this will take some time depending on the size of the repertoire. + +To understand the basic concepts of this graphing method, I'd highly recommend reading [this post](https://cran.r-project.org/web/packages/ggalluvial/vignettes/ggalluvial.html). + +```{r} +alluvialClones(scRep_example, + cloneCall = "aa", + y.axes = c("Patient", "ident", "Type"), + color = c("CVVSDNTGGFKTIF_CASSVRRERANTGELFF", "NA_CASSVRRERANTGELFF")) + + scale_fill_manual(values = c("grey", colorblind_vector[3])) + + +alluvialClones(scRep_example, + cloneCall = "gene", + y.axes = c("Patient", "ident", "Type"), + color = "ident") +``` + +## getCirclize + +Like alluvial graphs, we can also visualize the interconnection of clusters using the chord diagrams from the [circlize R package](https://jokergoo.github.io/circlize_book/book/). + +The first step is getting the data frame output to feed into the `chordDiagram()` function in circlize, which can be done using `getCirclize()`. + +This will calculate the relative number of clonotypes shared based on the **group.by** variable using the product of `combineExpression()`. It is important to note `getCirclize()` will create a matrix the size of the **group.by** variable and then simplify into instructions to be read by the circlize R package. The output is the total number of unique and shared clones by the **group.by** variable. + +```{r} +library(circlize) +library(scales) + +circles <- getCirclize(scRep_example, + group.by = "seurat_clusters") + +#Just assigning the normal colors to each cluster +grid.cols <- hue_pal()(length(unique(scRep_example$seurat_clusters))) +names(grid.cols) <- unique(scRep_example$seurat_clusters) + +#Graphing the chord diagram +chordDiagram(circles, self.link = 1, grid.col = grid.cols) +``` \ No newline at end of file diff --git a/vignettes/articles/Trex.Rmd b/vignettes/articles/Trex.Rmd new file mode 100644 index 00000000..581cc55b --- /dev/null +++ b/vignettes/articles/Trex.Rmd @@ -0,0 +1,291 @@ +--- +title: "Using Trex for TCRs" +date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`' +output: rmarkdown::html_vignette +theme: united +df_print: kable +vignette: > + %\VignetteIndexEntry{Using Trex for TCRs} + %\VignetteEngine{knitr::rmarkdown} + %\VignetteEncoding{UTF-8} +--- +*** + + + +```{r setup, include=FALSE} +all_times <- list() # store the time for each chunk +knitr::knit_hooks$set(time_it = local({ + now <- NULL + function(before, options) { + if (before) { + now <<- Sys.time() + } else { + res <- difftime(Sys.time(), now, units = "secs") + all_times[[options$label]] <<- res + } + } +})) +knitr::opts_chunk$set( + tidy = TRUE, + tidy.opts = list(width.cutoff = 95), + message = FALSE, + warning = FALSE, + time_it = TRUE +) + +suppressMessages(library(scRepertoire)) +suppressMessages(library(Trex)) +suppressMessages(library(Seurat)) +suppressMessages(library(ggplot2)) +suppressMessages(library(viridis)) +suppressMessages(library(dplyr)) +suppressMessages(library(reticulate)) +use_condaenv(condaenv = "r-reticulate", required = TRUE) + +data("contig_list") +combined.TCR <- combineTCR(contig_list, + samples = c("P17B", "P17L", "P18B", "P18L", + "P19B","P19L", "P20B", "P20L")) + +googledrive::drive_download("https://drive.google.com/file/d/1_YuRraDyg8UgF3oasjF0-jgPnwox-B24/view?usp=share_link", overwrite = TRUE) + +scRep_example <- readRDS("scRep_example_full.rds") +scRep_example <- scRep_example %>% + ScaleData(verbose = FALSE) %>% + FindVariableFeatures(verbose = FALSE) %>% + RunPCA(verbose = FALSE) + + +scRep_example <- combineExpression(combined.TCR, + scRep_example, + cloneCall="gene", + group.by = "sample") + +#Adding patient information +scRep_example$Patient <- substr(scRep_example$orig.ident, 1,3) + +#Adding type information +scRep_example$Type <- substr(scRep_example$orig.ident, 4,4) + +#Defining colors +colorblind_vector <- hcl.colors(n=7, palette = "inferno", fixup = TRUE) +``` + + +The idea behind Trex is to combine TCR CDR3 amino acid information with phenotypic RNA/protein data to direct the use of single-cell sequencing towards antigen-specific discoveries. This is a growing field - specifically [TESSA](https://github.com/jcao89757/TESSA) uses amino acid characteristics and autoencoder as a means to get a dimensional reduction. Another option is [CoNGA](https://github.com/phbradley/conga), which produces an embedding using TCR and RNA with python. Trex was designed to make a customizable approach to this combined approach using R. + +More infromation is available at the [Trex GitHub Repo](https://github.com/ncborcherding/Trex). + +## Installation + +```{r, eval = FALSE} +devtools::install_github("ncborcherding/Trex") +``` + + +## maTrex Function + +Trex has 2 major functions - the first being ```maTrex()```, which is the backbone of the algorithm and returns the encoded values based on the selection of variables. Unlike ```runTrex()``` below, ```maTrex()``` does not filter the input for only T cells with attached TCR data. In addition, ```maTrex()``` is compatible with the list output from the ```combineTCR()``` function from the [scRepertoire](https://github.com/ncborcherding/scRepertoire) R package, while ```runTrex()``` must be performed on a single-cell object. + +**chains** +* "TRA" for TCR Alpha Chain +* "TRB" for TCR Beta Chain + +**method** +* "encoder" for a convolution neural network (CNN) based encoding. +* "geometric" for a geometric transformation + +**encoder.model** +* "VAE" for a variational autoencoder +* "AE" for a traditional autoencoder + +**encoder.input** +* "AF" to use Atchley factors +* "KF" to use Kidera factors +* "both" to use both +* "OHE" for a One Hot Autoencoder + +**theta** +If choosing the geometric transformation, what value of theta to use (default is pi) + +```{r} +library(Trex) +Trex_vectors <- maTrex(scRep_example, + chains = "TRA", + encoder.model = "VAE", + encoder.input = "AF") + +ggplot(data = as.data.frame(Trex_vectors), aes(Trex_1, Trex_2)) + + geom_point() + + theme_classic() + +Trex_vectors2 <- maTrex(scRep_example, + chains = "TRB", + method = "geometric", + theta = pi) + +ggplot(as.data.frame(Trex_vectors2), aes(x = Trex_1, y = Trex_2)) + + geom_point() + + theme_classic() +``` + +## runTrex + +Additionally, ```runTrex()``` can be used to append the Seurat or Single-cell Experiment object with the Trex vectors and allow for further analysis. Importantly, ```runTrex()``` will remove single cells that do not have recovered TCR data in the metadata of the object. + +```{r} +scRep_example <- runTrex(scRep_example, + chains = "TRB", + encoder.model = "VAE", + encoder.input = "KF", + reduction.name = "Trex.KF") +``` + + +# Using Trex Vectors + +After ```runTrex()``` we have the encoded values stored under **"Trex..."**. Using the Trex reduction stored in Seurat, we can calculate the nearest neighbor and shared nearest neighbor indexes and generate a UMAP. + +```{r} +#Generating UMAP from Trex Neighbors +scRep_example <- RunUMAP(scRep_example, + reduction = "Trex.KF", + dims = 1:30, + reduction.name = 'Trex.umap', + reduction.key = 'trexUMAP_') + +#Trex UMAP +plot1 <- DimPlot(scRep_example, + reduction = "Trex.umap") + + NoLegend() +plot2 <- DimPlot(scRep_example, + group.by = "CTaa", + reduction = "Trex.umap") + + scale_color_viridis(discrete = TRUE, option = "B") + + NoLegend() + +plot1 + plot2 +``` + +We now can use this in a similar way as other single-cell modalities and calculate weighted nearest neighbor (WNN). To check out more on WNN, please read the Satija's group [paper](https://pubmed.ncbi.nlm.nih.gov/34062119/). We will use the RNA and Trex vectors for the WNN calculations. + +```{r} +scRep_example <- FindMultiModalNeighbors( + scRep_example, + reduction.list = list("pca", "Trex.KF"), + dims.list = list(1:30, 1:30), + modality.weight.name = "RNA.weight") + +scRep_example <- RunUMAP(scRep_example, + nn.name = "weighted.nn", + reduction.name = "wnn.umap", + reduction.key = "wnnUMAP_") + +scRep_example <- FindClusters(scRep_example, + graph.name = "wsnn", + resolution = 0.6, + algorithm = 3, verbose = FALSE) + +#WNN UMAP +plot3 <- DimPlot(scRep_example, + reduction = "wnn.umap") +plot4 <- DimPlot(scRep_example, + reduction = "wnn.umap", + group.by = "CTaa") + + scale_color_viridis(discrete = TRUE, option = "B") + + NoLegend() + +plot3 + plot4 +``` + +## Comparing the outcome to just one modality + +We can also look at the differences in the UMAP generated from RNA or Trex as individual components. Remember, the clusters that we are displaying in UMAP are based on clusters defined by the weighted nearest neighbors calculated above. + +```{r} +scRep_example <- RunUMAP(scRep_example, + reduction = 'pca', + dims = 1:30, + assay = 'RNA', + reduction.name = 'rna.umap', + reduction.key = 'rnaUMAP_') + + +plot5 <- DimPlot(scRep_example, reduction = "rna.umap") + NoLegend() +plot6 <- DimPlot(scRep_example, reduction = "Trex.umap") + NoLegend() + +plot5 + plot6 +``` + + +# CoNGA Reduction + +Recent [work](https://pubmed.ncbi.nlm.nih.gov/34426704/) has proposed using representative cells for the characterization of clonotype and gene expression relationships. In order to generate these representative cells, either a mean expression across a clone or using the PCA dimensional space to identify a single cell that has the minimum euclidean distance across a clone. + +In order to generate a single-cell object based on the CoNGA approach, Trex offers the function ```CoNGAfy()```. For **method**, select either "mean" or "dist" as described above. After performing ```CoNGAfy()```, the user can use any of the above reduction strategies. + +```{r} +CoNGA.seurat <- CoNGAfy(scRep_example, + method = "dist") + +CoNGA.seurat <- runTrex(CoNGA.seurat, + chains = "TRB", + encoder.model = "VAE", + encoder.input = "KF", + reduction.name = "Trex.KF") + +CoNGA.seurat <- CoNGA.seurat %>% + FindNeighbors(reduction = "Trex.KF") %>% + FindClusters(algorithm = 3) + +CoNGA.seurat <- RunUMAP(CoNGA.seurat, + reduction = "Trex.KF", + dims = 1:20, + reduction.name = 'Trex.umap', + reduction.key = 'trexUMAP_') + +DimPlot(CoNGA.seurat, reduction = "Trex.umap") + NoLegend() +``` + +# Annotating possible epitopes + +Towards find epitope relationships, Trex has a built in data base of TCRA and TCRB sequences associated with epitopes. To append the database to the single-cell object (either before or after ```CoNGAfy()```), you can use ```annotateDB()```. + +The database is collated from multiple sources and represents a great deal of effort from these sources: + +1. [VDJdb](https://vdjdb.cdr3.net/) - [citation](https://pubmed.ncbi.nlm.nih.gov/28977646/) +2. [McPAS-TCR](http://friedmanlab.weizmann.ac.il/McPAS-TCR/) - [citation](https://pubmed.ncbi.nlm.nih.gov/28481982/) +3. [iedb](https://www.iedb.org/) - [citation](https://pubmed.ncbi.nlm.nih.gov/30357391/) +4. [TBA](https://db.cngb.org/pird/home/) (now called PIRD) - [citation](https://pubmed.ncbi.nlm.nih.gov/31373607/) + + +```{r} +CoNGA.seurat <- annotateDB(CoNGA.seurat, + chains = "TRB") + +DimPlot(CoNGA.seurat, + reduction = "Trex.umap", + group.by = "TRB_Epitope.species") + +DimPlot(CoNGA.seurat, + reduction = "Trex.umap", + group.by = "TRB_Epitope.sequence") +``` + +Or ```annotateDB()``` can be used on the full single-cell object and examine the sequence information along the RNA-based UMAP. An added feature to the function allows the annotations to be extended to cdr3 sequences that are within defined edit distances from the reference using **edit.distance**. + +```{r} +scRep_example <- annotateDB(scRep_example, + chains = "TRB", + edit.distance = 2) + +DimPlot(scRep_example, + reduction = "rna.umap", + group.by = "TRB_Epitope.species") +``` + diff --git a/vignettes/vignette.Rmd b/vignettes/vignette.Rmd index c6f4634f..41adc173 100644 --- a/vignettes/vignette.Rmd +++ b/vignettes/vignette.Rmd @@ -76,7 +76,8 @@ contig.list <- createHTOContigList(contigs, Seurat.Obj, group.by = "HTO_maxID") scRepertoire comes with a data set from T cells derived from four patients with acute respiratory distress to demonstrate the functionality of the R package. More information on the data set can be found in the corresponding [manuscript](https://pubmed.ncbi.nlm.nih.gov/33622974/). The samples consist of paired peripheral-blood (B) and bronchoalveolar lavage (L), effectively creating 8 distinct runs for T cell receptor (TCR) enrichment. We can preview the elements in the list by using the head function and looking at the first contig annotation. ```{r} -data("contig_list") #the data built into scRepertoire +#built-in data for scRepertoire +data("contig_list") head(contig_list[[1]]) ``` @@ -102,7 +103,7 @@ contig.list <- loadContigs(dir = contig.output, # combineTCR -As the output of CellRanger are quantifications of both the TCRA and TCRB chains, the next step is to create a single list object with the TCR genes (comprised of the VDJC genes) and CDR3 sequences by cell barcode. This is performed using the `combineTCR()`, where the input is the stripped contig_list. There is also the relabeling of the barcodes by sample and ID information to prevent duplicates. +As the output of Cell Ranger are quantifications of both the TCRA and TCRB chains, the next step is to create a single list object with the TCR genes (comprised of the VDJC genes) and CDR3 sequences by cell barcode. This is performed using the `combineTCR()`, where the input is the stripped contig_list. There is also the relabeling of the barcodes by sample and ID information to prevent duplicates. **removeNA** * TRUE - this is a stringent filter to remove any cell barcode with an NA value in at least one of the chains @@ -137,14 +138,20 @@ combined <- addVariable(combined, combined[[1]][1:5,ncol(combined[[1]])] # This is showing the first 5 values of the new column added ``` -## subsetContig +## subsetClones -Likewise, we can remove specific list elements after `combineTCR()` using the `subsetContig()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and the variable values to subset (**variables**). Below, we isolate just the 2 sequencing results from P18L and P18B. +Likewise, we can remove specific list elements after `combineTCR()` using the `subsetClones()` function. In order to subset, we need to identify the vector we would like to use for subsetting (**name**) and the variable values to subset (**variables**). Below, we isolate just the 2 sequencing results from P18L and P18B. ```{r} -subset <- subsetContig(combined, - name = "sample", - variables = c("P18L", "P18B")) +subset1 <- subsetClones(combined, + name = "sample", + variables = c("P18L", "P18B")) + +#Subset using list elements +subset2 <- combined[c(3,4)] + +head(subset1) +head(subset2) ``` @@ -200,8 +207,7 @@ clonalQuant(combined, scale = TRUE) ``` -For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme/ - +For all visualizations in scRepertoire, there are 2 ways to adjust the color scheme: + Change the **palette** parameter to the desired color scheme. This approach uses the built-in palettes of grDevices and we can access the list of color schemes using ``hcl.pals()``. + Add a ggplot layer with a new color scheme. @@ -216,9 +222,8 @@ clonalQuant(combined, cloneCall="strict", chain = "both", scale = TRUE) + - scale_color_manual(values = hcl.colors(8,"geyser")) + scale_fill_manual(values = hcl.colors(8,"geyser")) ``` - Within each of the general analysis functions, there is the ability to export the data frame used to create the visualization. To get the exported values, use **exportTable** = TRUE. It will return the data frame used to make the graph instead of the visual output. ```{r} @@ -238,8 +243,6 @@ clonalQuant(combined, scale = TRUE) ``` - - ## clonalAbundance We can also examine the relative distribution of clonotypes by abundance. Here `clonalAbundance()` will produce a line graph with a total number of clonotypes by the number of instances within the sample or run. Like above, we can also group.by this by vectors within the contig object using the **group.by** variable in the function. @@ -253,7 +256,6 @@ clonalAbundance(combined, group.by = "Type", scale = TRUE) ``` - ## clonalLength We can look at the length distribution of the CDR3 sequences by calling the `lengtheContig()` function. Importantly, unlike the other basic visualizations, the **cloneCall** can only be "nt" or "aa". Due to the method of calling clonotypes as outlined above, the length should reveal a multimodal curve, this is a product of using the **NA** for the unreturned chain sequence and multiple chains within a single barcode. @@ -272,7 +274,6 @@ clonalLength(combined, chain = "TRA", scale = TRUE) ``` - ## clonalCompare We can also look at clonotypes between samples and changes in dynamics by using the `clonalCompare()` function. @@ -419,7 +420,7 @@ ggplot(df, aes(x = PC1, y = PC2)) + ## percentVJ -Quantify the proportion of V and J gene usage with `percentVJ()`. Like `percentGenes()`, this function will quantify the percentage of V and J paired together across individual repertoires. The output can be visualized using a heatmap or as input for futher dimensional reduction. +Quantify the proportion of V and J gene usage with `percentVJ()`. Like `percentGenes()`, this function will quantify the percentage of V and J paired together across individual repertoires. The output can be visualized using a heatmap or as input for further dimensional reduction. ```{r} percentVJ(combined, @@ -452,7 +453,7 @@ Another quantification of the composition of the CDR3 sequence is to define moti Numerical value for the length of the kmer. **top.motifs** -Display the *N* most variable genes determinied by absolute deviation. +Display the *N* most variable genes determined by absolute deviation. ```{r} percentKmer(combined, @@ -492,14 +493,13 @@ clonalHomeostasis(combined, cloneCall = "aa") ``` - ## clonalProportion Like clonal space homeostasis above, clonal proportion places clones into separate bins. The key difference is that instead of looking at the relative proportion of the clone to the total, the `clonalProportion()` function will rank the clones by total number and place them into bins. -The **clonal.split** represents the ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **clonal.split** variable in the function and can be adjusted, but they are as follows at baseline. +The **clonalSplit** represents the ranking of clonotypes by copy or frequency of occurrence, meaning 1:10 are the top 10 clonotypes in each sample. The default bins are under the **clonalSplit** variable in the function and can be adjusted, but they are as follows at baseline. -**clonal.split** +**clonalSplit** * 10 * 100 * 1000 @@ -591,10 +591,10 @@ clonalRarefaction(combined, ``` ## clonalScatter -A number of users requested a visualization from the work of [Wu, et al 2020, PMID: 32103181](https://pubmed.ncbi.nlm.nih.gov/32103181/) that allows for the direct comparison of clonotypes between 2 samples. ```clonalScatter()``` will organize two samples from the combineTCR/BCR product, count the relative clonotypes, and produce a scatter plot comparing the two samples. +```clonalScatter()``` will organize two repertoires, quantify the relative clone sizes, and produce a scatter plot comparing the two samples. **x.axis** and **y.axis** -* Names of the list element to place on the x-axis and y-axis - so for example "P18L" and "P18B" +* Names of the list element to place on the x-axis and y-axis - so for example *"P18L"* and *"P18B" * **dot.size** * "total" to display the total number of clones between the x- and y-axis @@ -604,6 +604,7 @@ A number of users requested a visualization from the work of [Wu, et al 2020, PM * "proportion" for the relative proportion the clonotype represents across all clonotypes * "count" for the total count of clonotypes by sample + ```{r} clonalScatter(combined, cloneCall ="gene", @@ -858,7 +859,7 @@ sub_combined <- clonalCluster(combined[[2]], ``` -Using ```clonalCluster()```, we can also return an igraph object of all the related sequences using **exportGraph** = TRUE. The returned igraph object contains only the sequences that have at least one conenction with another sequence. The igraph can then be directly visualized (below) or used for downstream analysis (see the igraph [website](https://r.igraph.org/). +Using ```clonalCluster()```, we can also return an igraph object of all the related sequences using **exportGraph** = TRUE. The returned igraph object contains only the sequences that have at least one connection with another sequence. The igraph can then be directly visualized (below) or used for downstream analysis (see the igraph [website](https://r.igraph.org/). ```{r} igraph.object <- clonalCluster(combined[c(1,2)], @@ -901,7 +902,7 @@ StartracDiversity(scRep_example, group.by = "Patient") ``` -## clonotypeBias +## clonalBias A new metric proposed by [Massimo et al](https://pubmed.ncbi.nlm.nih.gov/35829695/), clonal bias, like Startrac is a clonal metric that seeks to quantify how individual clones are skewed towards a specific cellular compartment or cluster. 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T@y1Gt8vOY3*Q~7Z6lMJl$oYYO From da438bae0e641913a3c9eb28ab98c9c0c68fafa2 Mon Sep 17 00:00:00 2001 From: theHumanBorch Date: Mon, 23 Oct 2023 15:45:01 -0500 Subject: [PATCH 097/142] Updated Trex and NEWS Trex and NEWS now working for website --- NEWS.html | 895 ++++++++++++++++++++++++++++++++++++ NEWS.md | 332 +++++++++++++ _pkgdown.yml | 4 +- inst/pkgdown.yml | 2 +- vignettes/articles/Ibex.Rmd | 6 +- vignettes/articles/Trex.Rmd | 143 +++--- 6 files changed, 1297 insertions(+), 85 deletions(-) create mode 100644 NEWS.html create mode 100644 NEWS.md diff --git a/NEWS.html b/NEWS.html new file mode 100644 index 00000000..95035920 --- /dev/null +++ b/NEWS.html @@ -0,0 +1,895 @@ + + + + + + + + + + + + + +NEWS + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +

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