I changed the cutoff to 0.01 for a more stringent selection of DE genes. Treat <- p.adjust(treatper.gene, method="BH") Treat per.gene <- do.call(scran::combinePValues, c(treatpval.list, list(method="simes"))) The correct way to do things would be to properly combine the p-values across contrasts for each gene, using Simes' method: pval.list <- lapply(1:ncol(pvalues), FUN=function(i) ) Eine der gebräuchlichsten Methoden ist die Verwendung der Bonferroni-Korrektur bei der Berechnung der p-Werte für jeden der paarweisen t-Tests. When I run the above code, I get an actual FDR of ~30%, which is much greater than my nominal FDR of 5% - not good. There is no guarantee that the FDR across genes is controlled at the nominal threshold, or even close. This is because the BH method is applied globally to the set of gene- and coefficient-specific tests. I should also add that the rowSums approach is not-quite-right in terms of FDR control of the resulting set of genes. I hope you did a contrasts.fit somewhere before calling treat, otherwise your tests won't make any sense.
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