Dear all,

I'm using ht-seq raw counts RNA-seq data from TCGA. For Normalizing the data first I used voom() transformation and converted them to log-CPM values.

I have used this voom function from Lima package to normalise data. t_index is the samples. The below function I got with some google search.

vm <- function(x){
  cond <- factor(ifelse(seq(1,dim(x)[2],1) %in% t_index, 1,  0))
  d <- model.matrix(~1+cond)
  x <- t(apply(x,1,as.numeric))
  ex <- voom(x,d,plot=F)
  return(ex$E)
}​

I have some couple of questions regarding the above function. Need some explanation from any one of you please.

Why I see negative values after normalisation? And what type of normalisation is applied after voom? Is normalisation across samples?

Any help is appreciated. Thank you.

The voom function computes logCPM values using whatever normalization information you choose to provide. This is documented in the help page for voom. Generally, you should be running it on a DGEList object after running calcNormFactors on the DGEList. See the help page for calcNormFactors if you want to know what kind of normalization it is doing.

In addition, if you only want logCPM values, you should not be using voom at all. The purpose of voom is to compute the weights required to counteract the mean-variance trend in the data. If all you want is logCPM, then use the cpm function from edgeR with log=TRUE, again after running calcNormFactors. However, depending on what you intend to do with the transformed data, DESeq2's rlog transformation might be more useful to you.