Dear Gordon and list, I have a question about p-value adjustment for multiple testing across contrasts. Let's say we have three groups, as in the Limma user guide, section 8.6, and this contrasts are closely related one another, so we want to apply method="global" to decideTests(). The code in section 8.6: > f <- factor(targets$Target, levels=c("RNA1","RNA2","RNA3")) > design <- model.matrix(~0+f) > colnames(design) <- c("RNA1","RNA2","RNA3") > fit <- lmFit(eset, design) > contrast.matrix <- makeContrasts(RNA2-RNA1, RNA3-RNA2, RNA3-RNA1, + levels=design) > fit2 <- contrasts.fit(fit, contrast.matrix) > fit2 <- eBayes(fit2) Here we have 3 contrasts on 3 levels, so only the first two are orthogonal (or at least so it says in "The R book"). Is it justified here to run >decideTests(fit2, method="global") or having non totally independent contrasts makes it impossible to correct the p-values "globally"? Best, paolo -- Paolo Innocenti Department of Animal Ecology, EBC Uppsala University Norbyv?gen 18D 75236 Uppsala, Sweden