**0**wrote:

I'm trying to understand how the mean-variance plot is calculated with the meanVarPlot() of EDASeq package. I tried to reproduce it manually with the code below but the result is very different.

Given the expression matrix (row=genes, columns=samples), I used the logarithm to transform the counts before computing mean and variance. Since there are zero counts, I considered the maximum between 0 and the log of the counts. After that I calculated the row means and the row variances (the mean and variance of each gene across all samples).

Here's the code I used:

rm(list=ls())

library(yeastRNASeq)

library(EDASeq)

data(geneLevelData)

head(geneLevelData)

Data=newSeqExpressionSet(as.matrix(geneLevelData))

meanVarPlot(Data,log=T)

geneLevelDataM=as.matrix(geneLevelData)

means=apply(pmax(log(geneLevelDataM),0),1,mean)

variances=apply(pmax(log(geneLevelDataM),0),1,var)

plot(means,variances)

means=apply(pmax(log2(geneLevelDataM),0),1,mean)

variances=apply(pmax(log2(geneLevelDataM),0),1,var)

plot(means,variances)

**13k**• written 18 months ago by neverstop •

**0**