plotSelVarGenes: Plot Selected Variable Genes

In fmicompbio/swissknife: Handy code shared in the FMI CompBio group

Plot Selected Variable Genes

Description

This function take the output from selVarGenes and plots the genes that have been selected to be highly variable across the cells. It plot the log2 coefficient of variation as a function of the log mean.

Usage

plotSelVarGenes(
  selVarGenes_list = NULL,
  xlab = "logMean",
  ylab = "logCV",
  main = "Selected Variable Genes",
  pch = 16,
  col = "#BEBEBE40",
  sel_col = "steelblue",
  colByBin = FALSE,
  asp = 1,
  ...
)

Arguments

selVarGenes_list	the output list from the selVarGenes function.
xlab	label for x-axis.
ylab	label for y-axis.
main	title for plot.
pch	point pch.
col	point color.
sel_col	point color of the selected variable genes.
colByBin	if TRUE, color the genes by the bin they've been assigned to.
asp	the y/x aspect ratio. Set to 1 when colByBin is TRUE.
...	additional parameters for the plot function.

Value

plot

Author(s)

Dania Machlab

Examples

if (requireNamespace("SingleCellExperiment", quietly = TRUE)) {
    # packages
    library(SingleCellExperiment)
   
    # create example count matrix
    # ... poisson distr per gene
    mu <- ceiling(runif(n = 2000, min = 0, max = 100))
    counts <- do.call(rbind, lapply(mu, function(x){rpois(1000, lambda = x)}))
    counts <- counts + 1
    # ... add signal to subset of genes (rows) and cells (columns)
    i <- sample(x = 1:nrow(counts), size = 500)
    j <- sample(x = 1:ncol(counts), size = 500)
    counts[i, j] <- counts[i, j] + sample(5:10, length(i), replace = TRUE)
 
    # create SCE
    sce <- SingleCellExperiment(list(counts = counts))

    # calculate sizeFactors
    libsizes <- colSums(counts)
    sizeFactors(sce) <- libsizes / mean(libsizes)

    # select variable genes
    varGenes <- selVarGenes(sce)
   
    # plot
    plotSelVarGenes(varGenes)
    plotSelVarGenes(varGenes, colByBin=TRUE)
}

fmicompbio/swissknife documentation built on June 11, 2025, 4:17 p.m.