We have performed RNAseq on a lot of samples of individual flies (~ 20 per condition).
After running DESeq and then checking various contrasts (with ihw=TRUE), I find myself with quite a bit of genes that are detected as differentially expressed between 2 conditions. But when I plot their counts, I notice that for a lot of them, it comes from the fact that most individuals have 0 counts while 1 or 2 have a high counts, which ends up being responsible for the significance of the Wald test.
I have read that pre-filtering was recommended, something along the lines of
keep <- rowSums(counts(dds) >= x) >= y
However if you look at the example I am joining to this post : if I filtered out this gene based on the fact that it has more than 0 counts in only 10 individuals, I take the risk of filtering a gene which would have more than 0 counts in 10 individuals of one condition. This would represent half of the individuals of the given condition, which I would consider meaningful.
There are a few of these genes in my dataset too.
The user discusses filtering RNA-seq data post-DESeq analysis, noting that genes with counts in only a few individuals may still be biologically significant. They seek advice on appropriate filtering strategies.
