fixed spelling, cleaned up files, added new example data for testing

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,

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"

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% 

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"

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.

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
 

inst/CITATION

@@ -1,13 +1,18 @@
 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,
+  entry = "Article",
+  title = "scRepertoire: An R-based toolkit for single-cell immune receptor analysis",
+  author = personList(
+      as.person("Nicholas Borcherding"),
+      as.person("Nicholas L Bormann"),
+      as.person("Gloria Kraus")
+  ),
+  journal = "F1000Research",
+  volume = "9",
+  year = "2022",
+  publisher = "Faculty of 1000 Ltd",
+  doi = "10.12688/f1000research.22139.2",
+  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)"
 )

inst/WORDLIST

@@ -65,11 +65,8 @@ aa
 airr
 al
 alluvialClonotypes
-analyis
-autencoding
 barcode
 barcodes
-betwwen
 changeNames
 circlize
 clonalNetwork
@@ -134,15 +131,10 @@ rda
 registerDoParallel
 removeMulti
 removeNA
-reprtoire
-runing
-scRepetoire
 scater
 seurat
 startrac
 stripBarcode
-subseted
-subseting
 subtype
 sys
 tcr
@@ -152,5 +144,6 @@ transcriptomic
 tsv
 unqiue
 unreturned
-warpper
-wheter
+CMD
+Codecov
+coords

scRepertoire.Rproj

@@ -17,3 +17,4 @@ AutoAppendNewline: Yes
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
+PackageCheckArgs: --as-cran --use-valgrind

tests/testthat/test-viz.R

@@ -11,9 +11,10 @@ combined <- combineTCR(
 
 single_contig <- combineTCR(contig_list[[1]])
 
-single_contig_with__sample <- combineTCR(
+single_contig_with_sample <- combineTCR(
 	contig_list[[1]], samples = "PX", ID = "P"
 )
+# TODO test more cases with single_contig
 
 test_that("quantContig works", {
 	expect_doppelganger(

vignettes/vignette.Rmd

@@ -16,22 +16,21 @@ vignette: >
 ---
 
 ```{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