Hi, I am analyzing an RNAseq experiment that had two different treatments (treated and control) and two time points per treatment (week 2 and week 3), with 3 biological replicates for each treatment/time point. I am interested in determining which genes are differentially expressed between the two treatments at each time point (in other words, comparing week 2 treated samples with week 2 control samples and week 3 treated samples to week 3 control samples).
I have used Galaxy to run DESeq2 separately for each contrast and have kept independent filtering on for both comparisons. For Week 2 data, no genes were filtered out by independent filtering, while for Week 3, all genes with a mean count <~45 across the 6 Week 3 samples have been filtered out, making it so that there are thousands more comparisons for Week 2. It doesn't look like week 2 has a substantial amount of low count genes among those with low p values, so I'm not sure why more genes aren't being filtered out, though I do understand that the independent filtering is supposed to optimize the number of genes that have an adjusted p-value below a given FDR cutoff, so for some reason further filtering is not increasing this number of genes.
Ideally, I'd like to implement a single filtering criterion across all samples so that I can look at whether similar genes are differentially expressed across the two time points. I know it has been recommended in the past to find the minimum filtering threshold across all contrasts and apply that to everything, but in this case I worry that would mean have no filtering which would negatively affect my results for Week 3.
Please advise on whether it would be acceptable to apply a more stringent filtering criterion by either going with the Week 3 filtering threshold for all samples, or using a more arbitrary threshold like a minimum of 10 reads for at least 3 of the samples.