Hi,
I am trying to determine differentially expressed genes between different statuses using edgeR with the ANOVA-like approach. Our experiment is very similar to the mouse mammary gland experiment described in the edgeR user's guide. I can compare the three statuses for either the L cell type or the B cell type separately, as shown below:

> targets
CellType    Status
B   virgin
B   virgin
B   pregnant
B   pregnant
B   lactate
B   lactate
L   virgin
L   virgin
L   pregnant
L   pregnant
L   lactate
L   lactate

> group <- factor(paste0(targets$CellType, ".", targets$Status))
> design <- model.matrix(~ 0 + group)
> fit <- glmQLFit(y, design, robust=TRUE)
> contrast <- makeContrasts(L.PvsL = L.pregnant - L.lactate, 
                          L.VvsL = L.virgin - L.lactate, 
                          L.VvsP = L.virgin - L.pregnant, levels=design)
> anova <- glmQLFTest(fit, contrast=contrast)
> topTags(anova, n=Inf, adjust.method = 'BH', sort.by = 'PValue')

However, I also want to find genes that are differentially expressed between the three statuses regardless of cell type. To do this, I would like to compare the 4 virgin, 4 pregnant, and 4 lactate samples together. How can I achieve this using the design above?

I have tried the following design, but it sets one of the statuses as a reference, and none of the statuses should serve as a reference or control:

> design <- model.matrix(~cell_type + status)
> fit <- glmQLFit(y, design)
> anova <- glmQLFTest(fit, coef=3:4)
> topTags(anova, n=Inf, adjust.method = 'BH', sort.by='PValue')

You could try the followings:

> design <- model.matrix(~ 0 + group)
> contrast <- makeContrasts(PvsL = 0.5*(L.pregnant + B.pregnant) - 0.5*(L.lactate + B.lactate), 
                            VvsL = 0.5*(L.virgin + B.virgin) - 0.5*(L.lactate + B.lactate), levels=design)
> anova <- glmQLFTest(fit, contrast=contrast)