I am using DESeq2 to search for highly variable genes. My experiment includes cells from 3 different human donors, they were planned as biological replicates and all treated under the same condition. As I saw in my PCA, donors differ quite a lot on in their genetic background. For now, I would like to see the effects of my treatment, so I used RUVs to normalize donors for batch effects. Afterwards I include the k's in my dds design.
ddsRUVs <- DESeqDataSetFromMatrix(countData = counts(setRUVs), colData = pData(setRUVs), design = ~ W_1 + W_2 + W_3 + W_4 + W_5 + W_6 + treatment)
For analysis of highly variable genes, I have the problem that my rlog transformation does not include the batch correction and I find only genes specific to my donors as highly variable. Also with counts(ddsRUVs, normalize = TRUE) they are not included. Any ideas if it would be possible to do my highly variable gene analysis on RUVs batch corrected data?
Thanks a lot!