I am interested in testing for differentially expressed genes with
edgeR across multiple groups, using the ANOVA-like approach.
For example, in the mouse mammary gland experiment in the
edgeR user's guide, contrasts are defined and top differentially expressed genes obtained as follows:
con <- makeContrasts( L.PvsL = L.pregnant - L.lactate, L.VvsL = L.virgin - L.lactate, L.VvsP = L.virgin - L.pregnant, levels = design ) anov <- glmQLFTest(fit, contrast = con) topTags(anov)
How should one best approach the question of which of these genes are significantly different for the individual comparisons?
If instead the samples had been processed using
voom from the
limma package, the
decideTests function has two methods,
"nestedF", which could be appropriate for this purpose; however,
edgeR does not have a similar argument.
One can use
glmQLFTest repeatedly, selecting the individual comparisons:
qlf.L.PvsL <- glmQLFTest(fit, contrast = con[, "L.PvsL"]) qlf.L.VvsL <- glmQLFTest(fit, contrast = con[, "L.VvsL"]) qlf.L.VvsP <- glmQLFTest(fit, contrast = con[, "L.VvsP"])
Should one then take the unadjusted p-values from these three tests and then adjust them all together, something like the