Plot Selected Variable Genes

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.

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

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)
}
#> Count row names are empty, naming them now ... 
#> Count column names are empty, naming them now ...