I have seen many posts about batch effect correction on RNA-seq data. But I'm little confused in using the code for differential analysis. So, asking it here.

I have been using 8 rna-seq samples, 4 NOTCH silenced samples and 4 Controls. The setup is like this

Samples   type      days
1        Control    day1
2        Control    day1
3        Control    day2
4        Control    day2
5        shNotch    day2
6        shNotch    day2
7        shNotch    day1
8        shNotch    day1

For my data I did the batch correction and then used the data for an MDS plot to look how they clustered.

library(edgeR)
group <- factor(paste0(columndata$type))
dge <- DGEList(counts=countdata, group=group)

keep <- rowSums(cpm(dge) > 1) >= 3
dge <- dge[keep, , keep.lib.sizes=FALSE]

dge <- calcNormFactors(dge, method = "TMM")

logCPM <- cpm(dge, log=TRUE, prior.count=2)
logCPM_bc <- removeBatchEffect(logCPM, batch=columndata$days)
plotMDS(logCPM_bc)

For differential analysis first I created design matrix like following:

design <- model.matrix(~ 0 + group + columndata$days)
colnames(design) <- c("Control","shNotch", "Days")
contrast.matrix <- makeContrasts(shNotch-Control, levels=design)

And now I was wondering whether to use DGElist object dge or logCPM_bc (Batch corrected logCPM) for estimating dispersion estimateDisp, for glmQLFit.

Any help? thanks a lot

Use the DGEList object and include the batch effect in your model.