Hello,

I have posted this question to the biostar forum but I couldn't get a thorough explanation. Sorry for repetition.

I have 4 different groups (species) that I want to look into their differential gene expression. I call them A, B, C and D. I have 5 - 8 replicates for each group and I am using DESeq2 for the analysis.

Which of the following ways is a recommended setting to continue with the analysis:

1. Create one dataset for each pairwise comparison, create the respective dds data matrix and run the DESeq function.
2. Create one dataset with all groups, create the dds data matrix, run the DESeq function and extract comparison of interest by contrast.

In the first approach, I get 215 significant genes for the comparison of A vs B while in the second approach, I get 49 significant genes. Of the top 50 genes with the lowest p-value in the two sets, 27 are common. How can I explain this difference and which approach is correct?

Thank you!

See the FAQ in the vignette, this is addressed directly.