plotCellTypeMeanVar: Plot cell type counts means versus variances

In Oshlack/speckle: Statistical methods for analysing single cell RNA-seq data

Plot cell type counts means versus variances

Description

This function returns a plot of the log10(mean) versus log10(variance) of the cell type counts. The function takes a matrix of cell type counts as input. The rows are the clusters/cell types and the columns are the samples.

Usage

plotCellTypeMeanVar(x)

Arguments

x	a matrix or table of counts

Details

The expected variance under a binomial distribution is shown in the solid line, and the points represent the observed variance for each cell type/row in the counts table. The expected variance under different model assumptions are shown in the different coloured lines.

The mean and variance for each cell type is calculated across all samples.

Value

a base R plot

Author(s)

Belinda Phipson

Examples

library(edgeR)
# Generate some data
# Total number of samples
nsamp <- 10
# True cell type proportions
p <- c(0.05, 0.15, 0.35, 0.45)

# Parameters for beta distribution
a <- 40
b <- a*(1-p)/p

# Sample total cell counts per sample from negative binomial distribution
numcells <- rnbinom(nsamp,size=20,mu=5000)
true.p <- matrix(c(rbeta(nsamp,a,b[1]),rbeta(nsamp,a,b[2]),
          rbeta(nsamp,a,b[3]),rbeta(nsamp,a,b[4])),byrow=TRUE, ncol=nsamp)

counts <- matrix(NA,ncol=nsamp, nrow=nrow(true.p))
rownames(counts) <- paste("c",0:(nrow(true.p)-1), sep="")
for(j in 1:length(p)){
    counts[j,] <- rbinom(nsamp, size=numcells, prob=true.p[j,])
}

plotCellTypeMeanVar(counts)

Oshlack/speckle documentation built on Oct. 16, 2022, 9:39 a.m.