I was just wondering whether it would make sense to combine the upperquartile normalization with limma-voom analysis of RNAseq data.
this is just out of interest, not driven by the data or so. At first, I did
uq <- DGEList(counts=initDGE, group=responseStatus)
uq <- calcNormFactors(uq, method="upperquartile")
uq <- estimateDisp(uq, design)
uq.v <- voom(uq, design, normalize.method="none")
cat("Limma::voom & eBayes after UpperQuartile\n")
fit <- lmFit(uq.v, design)
v.UQ <- contrasts.fit(fit, contrasts=contrastMat.R)
v.UQ <- eBayes(v.UQ)
then extracting a toptable from this object.
Ofcourse I get a toptable, but is this a correct approach. first, I am generating upperquartile normalized counts, then I feed this to 'voom' to log-transform the data without further normalization by voom, then I do the modelfitting etc.
Thank you for your advice.