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Old 01-27-2014, 09:47 AM   #13
Devon Ryan
Location: Freiburg, Germany

Join Date: Jul 2011
Posts: 3,480

@rskr: Given that this is in the context of DESeq2 (I realize that the thread is titled with edgeR...), low-count genes are automatically dropped and power maximized (I have to admit that it's handy to not have to do this myself anymore). So, the low-coverage genes screwing the p-values critique doesn't apply.

@sindrle: The informed decision is basically short-hand for what you want to do downstream (at least that's what I would mean had I written that...perhaps Simon means something else). If you're just interested in generally describing broad changes (e.g. in enriched GO terms) then you can be a bit more lax with the adjusted p-value cutoff. If, on the other hand, you're going to generate a bunch of transgenic mice or start a large-scale drug screen (i.e., your next step involves large amounts of time/money), then you really really need to be positive that you're not following up a spurious result. In those cases, you'd use a much lower adjusted p-value threshold. A bit of understanding of the underlying biology can also help make an informed decision here.

Other considerations could be:
1) How many hits did you find at a given threshold and how many did you expect (given preliminary data or published literature)?
2) If there are known changes, how many of those did you get at a given threshold?
3) Do you lack ethics and just want to make a nice, but likely false, story to publish in Science/Cell/Nature? Then just use raw p-values (or "better" yet, fold-changes!) and request reviewers who only understand Western blots.
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