For a while now, I've been looking at the intersect of the significant results from DESeq2 and EdgeR as my standard for determining differentially expressed genes, treating EdgeR as a filter over the DESeq2 results. I usually report fold-changes from DESeq2 as the foldchange shrinkage when counts are low or highly variable is nice for not putting too much weight on results that are significant but low-confidence. I made the assumption that my considering of the p-values of both methods would result in a lower false-positive rate without sacrificing too much in terms of false negatives.
However, I was recently challenged on this assumption by a statistician, and must now consider the possibility that this approach increases the rate of false negatives to an unacceptable level, or conversely doesn't reduce the false positive rate enough to justify its application. We talked about the possibility of using the SeqQC dataset to actually run an analysis to figure this out. Before I get into doing that though, has anyone tackled this idea before? Is there any statistical treatment out there on the effect of stacking/intersecting different differential expression methods? I haven't come across anything of the sort, but that of course doesn't mean that I didn't miss something relevant.