I know this is probably beaten to death, but I can't seem to find a satisfactory answer. How can the p-values/B-statistics from limma be properly interpreted? With assumptions satisfied, an FDR corrected p-value cutoff should produce a list of induced/repressed genes that includes a given percentage of false positives. However, we all know that we cannot assume independence with arrays. So, how does one rationalize a p-value or B-statistic cutoff to get beyond just a list of the top X genes? Does this dependence render the p-values completely meaningless? My "problem" is that my FDR corrected p-values are incredibly low (<1x10-4) and a moderate p-value cutoff produces a list of over 10% of the genes on my array. Now, I can obviously lower the cutoff, but how does one decide where to draw the line? Is it just empirically by how many genes I *do* expect to see induced/repressed. Since I don't know this answer, this places me in a untoward position of justifying the rationale. Thanks in advance. -- Mike