Sorry for yet another question on pre-filtering and DESeq2, but I didn't find anything related in the support pages... Now to my question:
I know that DESeq2 does a wonderful job of automatic pre-filtering, just, in my case it did remove a miRNA which, with 260 counts on average is not that low expressed, and in which differential expression I really believe. So, basically, I would like to do the pre-filtering myself, also accepting that I loose quite some power due to the stronger adjustment of multiple hypothesis testing.
My question now however is whether this pre-filtering, i.e. removing of low count features, interferes or violates the assumptions of the dispersion estimation (or any other assumption) in the DESeq2 model (Also considering that the pre-filtering in DESeq2 takes place after calculation or the raw p-values). My concern comes from the (ancient) field of microarrays were a variance based pre-filtering was thought to violate assumptions of the moderated t-test in limma.
Is the situation similar for DESeq2 and manual pre-filtering?
Thanks in advance!