Hi

I would like to correct batch effect using deseq2, to analyze one hundred of RNA-Seq of tumors, without experimental design. 80 tumors were sequenced in 2018, and 20 in 2019; I can see a strong batch effect whent I plot PCA on reads count or on data after DESeq between 2018 and 2019 tumors.

Usualy I used:

coldata <- as.data.frame(rep(TRUE, each=100))
rownames(coldata)<- colnames(COUNT)
colnames(coldata)<- c("group")    
dds<-DESeqDataSetFromMatrix(COUNT, coldata,design=~1)

I read that DESeq can correct batch effect with this kind of command:

dds <- DESeqDataSet(COUNT, design = ~ batch + condition)

But in my case I have no condition, and I tried "design=~batch", but without effect. I can remove efficiently batch effect with ComBat, with very good result on PCA plot. But then I have negatives values in my matrix, which is a problem for further analysis.

Which solution can I try?

Thank you for any suggestion.

DESeq does not correct the batch effect, it incorporates it into its linear model making. And you do that just like above, by adding batch to the design along with the other elements of the experiment.