I have the raw counts for RNA-Seq data. I converted counts data to logCPM using edgeR package.
Lets say I have a dataframe
A with 15000 genes as rows and 100 samples as columns with counts data. I have used following code from edgeR tutorial.
logCPM <- cpm(A, prior.count=2, log=TRUE) head(logCPM)[1:5,1:5] Sample1 Sample2 Sample3 Sample4 A1BG -1.019429 0.8333739 -1.192960 -0.6717287 A2M 7.622280 7.8057767 7.932570 8.6409077 A2ML1 1.004541 1.0909119 5.462613 6.4149348 A4GALT 2.126502 1.8403954 5.322601 2.9469345 AAAS 5.785888 5.1639501 5.259789 5.3248026 Sample5 A1BG 0.3121975 A2M 9.1949911 A2ML1 9.4740594 A4GALT 2.9214979 AAAS 4.3708063
After converting counts to
logCPM I see there is another command for scaling which is used for heatmaps.
logCPM <- t(scale(t(logCPM)))
It is given there that
scaling each row (each gene) to have mean zero and standard deviation one in page 15 edgeR
Is this nothing but z-score? or Is there any otherway to calculate z-score from logCPM data?