I am using DESeq2 to perform LRT analysis. I have 4 conditions, and each group named Group1, Group2, Group3, and Group4. Each group has 80 to 100 samples. I use only p-adj value to determine DEGs.
Whenever I use DESeq2 with LRT function, the last group (like group4) has distinctive characteristics. When I changed the group name (group3 to group4, group4 to group3), DEGs were changed. (common DEGs were only 1/4 or 1/5)
Should I use other values to determine DEGs except for p-adjust value? Or should I change the design or other codes?
DESeq2Table <- DESeqDataSetFromMatrix(countData = countData, colData = colData, design = ~ Group_Name) DESeq2Table <- DESeq(DESeq2Table, test="LRT", reduced=~1)
DESeq2Table <- estimateSizeFactors(DESeq2Table) DESeq2Table <- estimateDispersions(DESeq2Table)
DESeq2Table <- nbinomWaldTest(DESeq2Table) DESeq2Res <- results(DESeq2Table, pAdjustMethod = "BH")