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plotReducedDimLR.R	plotReducedDimLR.R

Plot ligand-receptor expression on UMAP and t-SNE projection

I-Hsuan Lin

University of Manchester

March 15, 2022

Introduction

This is a reproducible demo that shows readers how to use the function plotReducedDimLR which make use of the ggnewscale R package to ovelay the expressions of ligand and receptor genes on a single plot of UMAP or t-SNE projection.

About demo datasets

We use the TENxPBMCData R package to load the pbmc4k single-cell RNA-seq dataset on PBMC generated by 10X Genomics.

Loading required libraries

library(scales)
library(cowplot)
library(ggrepel)
library(ggnewscale)

# Run PBMC scRNA-seq demo
library(scater)
library(scran)
library(bluster)
library(celldex)
library(SingleR)
library(TENxPBMCData)

Load functions from file

We use source to load the the plotReducedDimLR and add_label functions from the file plotReducedDimLR.R into the workspace.

Specifically, plotReducedDimLR uses the new_scale_colour function from the ggnewscale R package to add an additional layer where a second geoms will use another color scale.

source("plotReducedDimLR.R")

Function 1: plotReducedDimLR

The plotReducedDimLR function allows users to create UMAP and t-SNE plots that show expression of a ligand-receptor pair. The required arguments to use the function are object, dimred and lr_pair.

object: A SingleCellExperiment object.
dimred: A string or integer scalar indicating the reduced dimension result in reducedDims(object) to plot.
lr_pair: A character vector of length 2 containing the ligand and receptor gene symbol.
lr_desc: A character vector of length 2 containing short description to change legend title. (default: c("Ligand","Receptor"))
lr_color: A character vector of length 2 containing colour aesthetics. (default: c("blue","red"))
lr_sep: A character string to define how the 2 genes terms are separated. (default: “-”)
oneplot: Logical scalar indicating whether to overlay expressions in a single plot or generate 2 side-by-side plots. (default: TRUE)
by_exprs_values: A string or integer scalar specifying which assay to obtain expression values from, for use in point aesthetics. (default: “logcounts”)
point_size: A numeric scalar specifying the size of the points. (default: 2)
point_alpha: A numeric scalar (between 0 and 1) specifying the transparency. (default: 0.4)
point_shape: An integer scalar (between 0 and 25) specifying the shape aesthetics. (default: 16)
text_by: A string specifying the column metadata field with which to add text labels on the plot. (default: NULL)
text_size: A numeric scalar specifying the size of added text. (default: 8)
text_color: A string specifying the color of the added text. (default: “black”)
theme_size: A numeric scalar specifying the base font size. (default: 14)

Function 2: add_label

The add_label function is a fix of the plotReducedDim function from the scater package (wll be fixed in scater v1.23.5). It allows the added labels to be placed centrally in each cluster. The required arguments to use the function are object and dimred.

object: A SingleCellExperiment object.
dimred: A string or integer scalar indicating the reduced dimension result in reducedDims(object) to plot.
text_by: A string specifying the column metadata field with which to add text labels on the plot. (default: “label”)
text_size: A numeric scalar specifying the size of added text. (default: 8)
text_color: A string specifying the colour of the added text. (default: “black”)

Prepare the `pbmc4k` dataset

#--- data-loading ---#
sce.pbmc <- TENxPBMCData("pbmc4k")
sce.pbmc

## class: SingleCellExperiment 
## dim: 33694 4340 
## metadata(0):
## assays(1): counts
## rownames(33694): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
## rowData names(3): ENSEMBL_ID Symbol_TENx Symbol
## colnames: NULL
## colData names(11): Sample Barcode ... Individual Date_published
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):

#--- gene-annotation ---#
rownames(sce.pbmc) <- uniquifyFeatureNames(rowData(sce.pbmc)$ENSEMBL_ID, 
                       rowData(sce.pbmc)$Symbol)
is.mito <- grep("^MT-", rowData(sce.pbmc)$Symbol_TENx)

#--- quality-control ---#
stats <- perCellQCMetrics(sce.pbmc, subsets = list(Mito = is.mito))
high.mito <- isOutlier(stats$subsets_Mito_percent, type = "higher")
sce.pbmc <- sce.pbmc[,!high.mito]

#--- normalization ---#
set.seed(10001)
clusters <- quickCluster(sce.pbmc)
sce.pbmc <- computeSumFactors(sce.pbmc, cluster = clusters)
sce.pbmc <- logNormCounts(sce.pbmc)

#--- variance-modelling ---#
set.seed(10001)
dec.pbmc <- modelGeneVarByPoisson(sce.pbmc)
top.pbmc <- getTopHVGs(dec.pbmc, prop = 0.1)

#--- dimensionality-reduction ---#
set.seed(10001)
sce.pbmc <- denoisePCA(sce.pbmc, subset.row = top.pbmc, technical = dec.pbmc)

set.seed(10001)
sce.pbmc <- runTSNE(sce.pbmc, dimred = "PCA")

set.seed(10001)
sce.pbmc <- runUMAP(sce.pbmc, dimred = "PCA")

sce.pbmc

## class: SingleCellExperiment 
## dim: 33694 4175 
## metadata(0):
## assays(2): counts logcounts
## rownames(33694): ENSG00000243485 FAM138A ... ENSG00000277475 FAM231C
## rowData names(3): ENSEMBL_ID Symbol_TENx Symbol
## colnames: NULL
## colData names(12): Sample Barcode ... Date_published sizeFactor
## reducedDimNames(3): PCA TSNE UMAP
## mainExpName: NULL
## altExpNames(0):

#--- walktrap clustering ---#
clu.walktrap <- clusterCells(sce.pbmc, use.dimred = "PCA", 
                 BLUSPARAM = NNGraphParam(cluster.fun = "walktrap"))
sce.pbmc$walktrap <- clu.walktrap
table(clu.walktrap)

## clu.walktrap
##    1    2    3    4    5    6    7    8    9   10   11 
##  187  563  526  811  605  128 1011   46  197   84   17

#--- louvain clustering ---#
clu.louvain <- clusterCells(sce.pbmc, use.dimred = "PCA", 
                BLUSPARAM = NNGraphParam(cluster.fun = "louvain"))
sce.pbmc$louvain <- clu.louvain
table(clu.louvain)

## clu.louvain
##   1   2   3   4   5   6   7   8   9  10  11  12  13  14 
## 536 371 583 127 605 292 429 152  46 188  85 366  37 358

table(clu.walktrap, clu.louvain)

##             clu.louvain
## clu.walktrap   1   2   3   4   5   6   7   8   9  10  11  12  13  14
##           1    0   0   0   0   0   0   0   0   0 187   0   0   0   0
##           2    0 361   6   0   0   0   9   3   0   0   0   1  37 146
##           3    0  10   0   0   0   0   0 149   0   1   0 365   0   1
##           4  535   0   0   0   0 275   0   0   0   0   1   0   0   0
##           5    0   0   0   0 605   0   0   0   0   0   0   0   0   0
##           6    0   0   0 127   0   1   0   0   0   0   0   0   0   0
##           7    0   0 577   0   0   0 420   0   0   0   0   0   0  14
##           8    0   0   0   0   0   0   0   0  46   0   0   0   0   0
##           9    0   0   0   0   0   0   0   0   0   0   0   0   0 197
##           10   0   0   0   0   0   0   0   0   0   0  84   0   0   0
##           11   1   0   0   0   0  16   0   0   0   0   0   0   0   0

#--- cell type annotation ---#
ref <- celldex::BlueprintEncodeData()
pred <- SingleR(test = sce.pbmc, ref = ref, labels = ref$label.main)
table(pred$labels)

## 
##      B-cells CD4+ T-cells CD8+ T-cells           DC          HSC    Monocytes     NK cells 
##          609          867         1386            1           12         1060          240

Create plots

Show cells coloured by cluster assignment

p1 <- plotReducedDim(sce.pbmc, "TSNE", colour_by = "walktrap", theme_size = 14) +
    guides(color = guide_legend(title = "walktrap", ncol = 1, 
                    override.aes = list(size = 4, alpha = 1))) +
    add_label(sce.pbmc, "TSNE", text_by = "walktrap") + 
    ggtitle("TSNE", subtitle = "labelled by walktrap")

p2 <- plotReducedDim(sce.pbmc, "UMAP", colour_by = "walktrap", theme_size = 14) +
    guides(color = guide_legend(title = "walktrap", ncol = 1, 
                    override.aes = list(size = 4, alpha = 1))) +
    add_label(sce.pbmc, "UMAP", text_by = "walktrap") + 
    ggtitle("UMAP", subtitle = "labelled by walktrap")

p3 <- plotReducedDim(sce.pbmc, "TSNE", colour_by = "louvain", theme_size = 14) +
    guides(color = guide_legend(title = "louvain", ncol = 1, 
                    override.aes = list(size = 4, alpha = 1))) +
    add_label(sce.pbmc, "TSNE", text_by = "louvain") + 
    ggtitle("TSNE", subtitle = "labelled by louvain")

p4 <- plotReducedDim(sce.pbmc, "UMAP", colour_by = "louvain", theme_size = 14) +
    guides(color = guide_legend(title = "louvain", ncol = 1, 
                    override.aes = list(size = 4, alpha = 1))) +
    add_label(sce.pbmc, "UMAP", text_by = "louvain") + 
    ggtitle("UMAP", subtitle = "labelled by louvain")

# Use plot_grid to join plots
plot_grid(p1, p2, nrow = 1)
plot_grid(p3, p4, nrow = 1)

Show cells coloured by cell type assignment

p1 <- plotReducedDim(sce.pbmc, "TSNE", colour_by = I(pred$labels), theme_size = 14) +
    guides(color = guide_legend(title = "CellType", ncol = 1, 
                    override.aes = list(size = 4, alpha = 1))) +
    add_label(sce.pbmc, "TSNE", text_by = "walktrap") + 
    ggtitle("TSNE", subtitle = "labelled by walktrap")

p2 <- plotReducedDim(sce.pbmc, "UMAP", colour_by = I(pred$labels), theme_size = 14) +
    guides(color = guide_legend(title = "CellType", ncol = 1, 
                    override.aes = list(size = 4, alpha = 1))) +
    add_label(sce.pbmc, "UMAP", text_by = "walktrap") + 
    ggtitle("UMAP", subtitle = "labelled by walktrap")

# Use plot_grid to join plots
plot_grid(p1, p2, nrow = 1)

Show cells coloured by CD40LG and CD40 expression

The CD40LG-CD40 is one of the well-characterised ligand-receptor pairs between T cells and B cells (ref).

The CD40LG (also known as CD154 or CD40L) encodes the CD40 ligand that is predominantly expressed in activated T cells. [walktrap clusters 2, 3, 7 and 9]
The CD40 molecule is a costimulatory receptor expressed by B cells and antigen-presenting cells (APCs). [walktrap cluster 5]

Minimal arguments

A SingleCellExperiment object.
A string or integer scalar indicating the reduced dimension result in reducedDims(object) to plot.
A character vector of length 2 containing the ligand and receptor gene symbol.

p1 <- plotReducedDimLR(sce.pbmc, "TSNE", c("CD40LG","CD40"))
p2 <- plotReducedDimLR(sce.pbmc, "UMAP", c("CD40LG","CD40"))

# Use plot_grid to join plots
plot_grid(p1, p2, nrow = 1)

Labelled by cluster assignment

Use text_by to add walktrap cluster labels.

plotReducedDimLR(sce.pbmc, "TSNE", c("CD40LG","CD40"), text_by = "walktrap")

Change expression colours

Use lr_color to change colours.

plotReducedDimLR(sce.pbmc, "TSNE", c("CD40LG","CD40"), text_by = "walktrap", 
         lr_color = c("cyan","magenta"))

Show expression of ligand-receptor pairs separately

Set oneplot = FALSE to show 2 separate plots.

plotReducedDimLR(sce.pbmc, "TSNE", c("CD40LG","CD40"), text_by = "walktrap", 
         oneplot = FALSE)

Show expression of 2 genes (not necessary a LR pair)

Use lr_desc to change legend titles.
Use lr_sep to change how the genes are separated in plot title.

plotReducedDimLR(sce.pbmc, "TSNE", c("MS4A1","NKG7"), lr_desc = c("B cell","NK cell"), 
         lr_sep = " and ", text_by = "walktrap")

Session Info

sessionInfo()

Click to expand

## R version 4.1.2 (2021-11-01)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /home/ihsuan/miniconda3/envs/jupyterlab/lib/libopenblasp-r0.3.18.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8       
##  [4] LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
## [10] LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] TENxPBMCData_1.12.0         HDF5Array_1.22.1            rhdf5_2.38.0               
##  [4] DelayedArray_0.20.0         Matrix_1.4-0                SingleR_1.8.1              
##  [7] celldex_1.4.0               bluster_1.4.0               scran_1.22.1               
## [10] scater_1.22.0               scuttle_1.4.0               SingleCellExperiment_1.16.0
## [13] SummarizedExperiment_1.24.0 Biobase_2.54.0              GenomicRanges_1.46.1       
## [16] GenomeInfoDb_1.30.1         IRanges_2.28.0              S4Vectors_0.32.3           
## [19] BiocGenerics_0.40.0         MatrixGenerics_1.6.0        matrixStats_0.61.0         
## [22] ggnewscale_0.4.6            ggrepel_0.9.1               ggplot2_3.3.5              
## [25] cowplot_1.1.1               scales_1.1.1                knitr_1.37                 
## 
## loaded via a namespace (and not attached):
##   [1] Rtsne_0.15                    ggbeeswarm_0.6.0              colorspace_2.0-3             
##   [4] ellipsis_0.3.2                XVector_0.34.0                BiocNeighbors_1.12.0         
##   [7] farver_2.1.0                  bit64_4.0.5                   RSpectra_0.16-0              
##  [10] interactiveDisplayBase_1.32.0 AnnotationDbi_1.56.2          fansi_1.0.2                  
##  [13] codetools_0.2-18              sparseMatrixStats_1.6.0       cachem_1.0.6                 
##  [16] cluster_2.1.2                 dbplyr_2.1.1                  png_0.1-7                    
##  [19] uwot_0.1.11                   shiny_1.7.1                   BiocManager_1.30.16          
##  [22] compiler_4.1.2                httr_1.4.2                    dqrng_0.3.0                  
##  [25] assertthat_0.2.1              fastmap_1.1.0                 limma_3.50.1                 
##  [28] cli_3.2.0                     later_1.3.0                   BiocSingular_1.10.0          
##  [31] htmltools_0.5.2               tools_4.1.2                   rsvd_1.0.5                   
##  [34] igraph_1.2.11                 gtable_0.3.0                  glue_1.6.2                   
##  [37] GenomeInfoDbData_1.2.7        dplyr_1.0.8                   rappdirs_0.3.3               
##  [40] Rcpp_1.0.8                    rhdf5filters_1.6.0            vctrs_0.3.8                  
##  [43] Biostrings_2.62.0             ExperimentHub_2.2.1           DelayedMatrixStats_1.16.0    
##  [46] xfun_0.29                     stringr_1.4.0                 beachmat_2.10.0              
##  [49] mime_0.12                     lifecycle_1.0.1               irlba_2.3.5                  
##  [52] statmod_1.4.36                AnnotationHub_3.2.1           edgeR_3.36.0                 
##  [55] zlibbioc_1.40.0               promises_1.2.0.1              parallel_4.1.2               
##  [58] yaml_2.3.5                    curl_4.3.2                    memoise_2.0.1                
##  [61] gridExtra_2.3                 stringi_1.7.6                 RSQLite_2.2.10               
##  [64] highr_0.9                     BiocVersion_3.14.0            ScaledMatrix_1.2.0           
##  [67] filelock_1.0.2                BiocParallel_1.28.3           rlang_1.0.1                  
##  [70] pkgconfig_2.0.3               bitops_1.0-7                  evaluate_0.15                
##  [73] lattice_0.20-45               Rhdf5lib_1.16.0               purrr_0.3.4                  
##  [76] labeling_0.4.2                bit_4.0.4                     tidyselect_1.1.2             
##  [79] RcppAnnoy_0.0.19              magrittr_2.0.2                R6_2.5.1                     
##  [82] generics_0.1.2                metapod_1.2.0                 DBI_1.1.2                    
##  [85] pillar_1.7.0                  withr_2.4.3                   KEGGREST_1.34.0              
##  [88] RCurl_1.98-1.6                tibble_3.1.6                  crayon_1.5.0                 
##  [91] utf8_1.2.2                    BiocFileCache_2.2.1           rmarkdown_2.11               
##  [94] viridis_0.6.2                 locfit_1.5-9.4                grid_4.1.2                   
##  [97] blob_1.2.2                    digest_0.6.29                 xtable_1.8-4                 
## [100] httpuv_1.6.5                  munsell_0.5.0                 beeswarm_0.4.0               
## [103] viridisLite_0.4.0             vipor_0.4.5

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plotReducedDimLR

plotReducedDimLR

README.md

Plot ligand-receptor expression on UMAP and t-SNE projection

Introduction

About demo datasets

Loading required libraries

Load functions from file

Prepare the `pbmc4k` dataset

Create plots

Show cells coloured by cluster assignment

Show cells coloured by cell type assignment

Show cells coloured by CD40LG and CD40 expression

Minimal arguments

Labelled by cluster assignment

Change expression colours

Show expression of ligand-receptor pairs separately

Show expression of 2 genes (not necessary a LR pair)

Session Info

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parent directory

README.md

Plot ligand-receptor expression on UMAP and t-SNE projection

Introduction

About demo datasets

Loading required libraries

Load functions from file

Prepare the pbmc4k dataset

Create plots

Show cells coloured by cluster assignment

Show cells coloured by cell type assignment

Show cells coloured by CD40LG and CD40 expression

Minimal arguments

Labelled by cluster assignment

Change expression colours

Show expression of ligand-receptor pairs separately

Show expression of 2 genes (not necessary a LR pair)

Session Info

Prepare the `pbmc4k` dataset