Hi,

  I want to use edgeR for DEGs between two groups, but I find two batches in samples. So I intend to use Combat to adjust for differences between batches. I know edgeR is designed to work with actual read counts, not rpkm or fpkm. The question is can i use Combat on read counts straightly? Or Combat only be used on expression data, for example rpkm?

It would usually be preferable to skip any pre-adjustment and to instead include a batch effect as part of the edgeR linear model analysis. See case study 4.6 in the edgeR User's Guide for an example of this sort of analysis.

    Hi Gordon, thank you for your advice. I have checked on  case study 4.6 in the edgeR User's Guide, I don't think our case is similar with it. 

    I have 40 pairs Normal-Tumor samples. And there ane two batches. Can I use edgeR to adjust for batch effects in such a case?

    I have used Combat on log(rpkm+1) data to adjust for batch effects, and then just use it's result to compute DEG by edgeR, is this OK? (I mean I do not use read counts but adjusted rpkm for DEG by edgeR.) 

   Yes，we have different pairs in different batches. We use featureCounts in Rsubread package to get read counts. You mean that we just use the read counts for DEG by edgeR without removing the batch effect? We did before and got more than 3,000 different expression genes. My codes are as follows:

library(Rsubread)
library(limma)
library(edgeR)
library(splines)
targets <- readTargets("targets.txt")
Counts <- read.table("readscount.txt")
x <- DGEList(counts=Counts,group=targets$CellType)
keep <- rowSums(cpm(x)>10) >= 80
x <- x[keep,]
x$samples$lib.size <- colSums(x$counts)
x <- calcNormFactors(x)
Patient <- factor(rep(15:54,each=2)) ###the sample are CH15N, CH15T,CH16N,CH16T...CH54N,CH54T
Tissue <- factor(targets$CellType)
data.frame(Sample=colnames(x),Patient,Tissue)
design <- model.matrix(~Patient+Tissue)
rownames(design) <- colnames(x)
x <- estimateGLMCommonDisp(x, design, verbose=TRUE)
x <- estimateGLMTrendedDisp(x, design)
x <- estimateGLMTagwiseDisp(x, design)
fit <- glmFit(x, design)
lrt <- glmLRT(fit)
write.table(topTags(lrt, n=dim(lrt$table)[1]),file="result.txt",row.names=TRUE,sep="\t")

    Are the codes OK? Thank you very much for your help! 

The code looks ok. Yes, any batch effect associated with groups of patients is already being removed.

One would expect to get 3000 or more DE genes for tumor vs normal for most cancers.

OK, thank you very much!