Hi, I guess the simple question is would you expect or have you seen a 'standard' distribution of p-values for a treated-untreated comparison (3 reps) after the eBayes procedure in Limma? Expressed in my usual 'comprehensive' style ;-) Following on from a previous thread I've started to look more into the p.value distributions to get an idea of false +ve and -ve rates. I understand that p-values should be approx. uniformly distributed as they approach 1. The following paper uses a mixture of beta and uniform distributions to model false/true +ve and -ve rates. http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&l is t_uids=12835267&dopt=Abstract Code that can be pasted into R is found here - http://www.stjuderesearch.org/depts/biostats/BUM/index.html Has anyone done similar? And is this approach valid using the eBayes moderated stats of limma? This approach 'appears' ok if you have alot of replicates (my 9 genotypes x 2 treatments x 3 reps example again) ie: the p-values show the expected distribution. However, if you drop down to a single genotype and therefore a 3 x 3 comparison the p-values aren't well distributed (slightly more 0.55-0.8, slightly less 0.85-1). I'm worried this means that maybe the tests assumptions aren't met, but is there a way of formally testing this? At the same time I suppose it's not too suprising that with low replications the distribution is not ideal - hence my question of other peoples experience with p-value distributions from limma). Incidently limma p-values have a better distribution than a paired or equal varience t-test which I guess is a good sign. I had a quick look at someones elses data for a 3 x 3 comparison they had an even 'worse' p-value distribution. Theirs had a large secondary peak from 0.8-1. Again I assume this could mean assumptions were not met - but can anyone explain any possible causes? The reasons I can think of are that their experiment had more similar biological replica and their treatment could cause a large number of co-regulated changes. However, not being a statistician, I can't relate this to the p-value distribution. Thanks in advance for any feedback. Matt