I have a somewhat fundamental question about differential expression detection in limma. Assume, I want to analyze a micro-array experiment where gene-expression is measured for a limited set of genes (say 1000) in 8 different conditions. The main aim is to identify which of these genes are different for each pair-wise comparison (AvsB, AvsC, ..., AvsH, BvsC, et cetera).
In a "traditional" setting, one would apply first an F-test, and whenever this F-test has a pvalue below the significance level, one would start with the pair-wise comparisons. However, it seems that in limma, when using topTable() after eBayes(), that the moderated F-test and the moderated t-tests for the pair-wise comparisons are done at the same time, which means that the correction for multiple testing is done for all genes, regardless if the pvalue of the F-test is below the significance level, which could lead to having more false negatives.
Is it possible to mimick the "traditional" approach in limma, where the t-tests are done after selecting genes based on the F-test? Would this require to refit the model and hence apply the empirical Bayes approach on a smaller set of genes (which can lead to less precise estimation of the variances)?
Thank you, Jürgen