As you answered yourself @Death Metal,
voom will by default not perform additional normalization.
However in virtually all cases you would want to do some kind of normalization at least to correct for differences in sequenced reads between the samples. This is why in the manual (page 71 right at the top) the
calcNormFactors function from
edgeR is used. This does a global
TMM normalization for differences in library size.
voom assumes that you did a normalization and will not do a second one because this could cause all kinds of problems.
In some cases you may prefer normalization other than
TMM, for example if the number of genes detected in your samples is widely different. In this case is could be better to do
quantile normalization that is available through the
normalizeBetweenArrays function in
However, this may over-correct your data and get rid of many interesting differences. In these cases I have found
qsmooth quite helpful. Here quantile normalization is only applied across groups if the local distributions are similar enough.