I was wondering if it's possible to incorporate the weights calculated by voomWithQualityWeights() in glmQLFit in edgeR. My code is as follows:

v <- voomWithQualityWeights(dge, design, plot=FALSE)
qlfit_weigthed <- glmQLFit(dge$counts, design, weights = v$weights, robust=TRUE, dispersion = dge$trended.dispersion)
lrt_weighted <- glmQLFTest(qlfit_weigthed, contrast=cont)

Adding the weights to glmQLFit gives me several more significant genes, so I want to make sure what I am doing is correct. My hesitation is with which counts to feed into glmQLFit. Should I be entering normalized counts or raw counts? I've read through the reference manual and I couldn't find a specification.

Additionally, is this worth pursuing, or are the weights provided by voomWQW and the weights exprected by glmQLFit not the same type?

Any help would be greatly appreciated. Thanks!

No, it doesn't make sense to use the voom weights in QL edgeR. The former describes the precision of the log-CPMs, while the weights in GLM scale the variances of the raw counts. They're totally different things.

You should be supplying the DGEList object to glmQLFit. You can supply the raw counts if you want, but then you need to pass the normalization factors via offsets. edgeR doesn't have a concept of normalized counts.