Hello,
I am currently using Deseq2 to perform differential analysis on my data. I have feature count data for 24 samples and 4 comparisons to do ( for each comparison I have 3 control samples and 3 knockout samples). The samples in each of these 4 groups are from different tissues and cell types.
The issue is: when I run Deseq2 for all of the samples, specifying all 4 comparisons, I get very different foldchanges and p-values then when I split the feature count data into 4 files, one per comparison, and run Deseq2 for each comparison separately.
One thing I noticed is that the p-value distribution peaks at around 0 when running with data for a single comparison, but peaks at around 1 when using all of the samples data ( 4 comparisons).
Why does this cause such a big difference and what is the best practice I should follow for these analysis? Could the fact that I am including samples from different cell types and tissues be an issue when estimating size factors?
Thank you
