Hi all, Several questions concerning intricacies of limma's decideTests() have emerged from a discussion with some colleagues here at the EBI. Perhaps someone can enlighten us. 1. The docs (and previously on this list) say that nestedF is especially powerful in identifying genes that are diff. expressed in many contrasts, and less powerful for ones diff. expressed in only one contrast. On the other hand we also read that with nestedF "at least one contrast will be classified as significant if and only if the overall F-statistic is significant" - meaning that it should pick up genes diff. expressed in only one contrast, shouldn't it? Why less powerful? 2. Say we have 5 contrasts adn we run decideTests() both with "separate" and with "nestedF" methods and are comparing the results. Suppose some gene is marked as differentially expressed in 2 out of 5 "separate" contrasts, but is significant in only 1 out of 5 "nestedF" ones. What's the best way to interpret such a result? Shall we say this gene is not sufficiently variable overall? And vice versa, if it's marked significant in 2 columns in "nestedF" but only in 1 in "separate" results, does "nestedF" overestimate its significance, or is it that "separate" failed to pick it up in some contrast? 3. Does it make sense to rank genes in order of significance of differential expression by looking at how many columns of "nestedF" results have non-zero values for each gene? If a gene is classified by "nestedF" as diff. expressed in only 1 contrast, is it still one of the most variable genes across all contrasts? 4. Say we have an experiment with treatment A, treatment B and a compound treatment A+B (not time course), is it legitimate to apply "nestedF" to all pair-wise contrasts to identify the most responsive overall genes, but then to look at the results matrix separately to say which treatments contributed most to this overall significance? Or is it more sensible to do it with the method "separate"? Many thanks in advance for any answers to these questions! --Misha K. and colleagues Microarray Informatics Team, EBI