Hi, While analyzing a set of DEGs resulting from DESeq2, I noticed for some genes that were I 'see' differences they are not significant, but then another gene with similar behaviour, the difference is significant. They both come from different comparisons, the former with 6 replicates and the later with 3, so I guess the number of replicates in the comparison make a difference. However I decided to look at the p-value distribution and noticed that almost all pvalues fall in the 1 bin. From what I read this could mean that the differential test would be assuming that the data has a distribution it doesn't have. But then I am all confused whether I should consider the p-values, the adj p-values or do another test alltogether and ignore DESeq2.
These are the genes: gene A, DE when testing for genotype 1 vs genotype 2 (6 replicates) adj-pvalue=0.006, but it is not DE in the genotype 2 T vs C comparison (adj p-value = 0.8, red and blue dots), while it has the same profile/behaviour as gene B -> with DE in genotype 2 T vs C (adj p-value=0.016) but not DE when testing for genotype 1 vs genotype 2 (adj p-value=0.77). If I have to interpret these two genes as a biologist, I would not say they are differentially expressed between genotypes but both induced at treatment in genotype 2. I am wondering how I can support this in a report. while justifying the statistical results.  http://hpics.li/66db722
This is the pvalues histogram for one of the comparisons but the other one looks the same  http://hpics.li/5d0bdf2