Davide Cittaro | 5 Feb 07:07 2013

limma-voom and y$E

Hi all, 
Although limma-voom seems to give us really good results in our RNA-seq experiments, we still have some
doubt about y$E values (i.e. expression values after voom). looking at function implementation it seems
that voom performs quantile normalization using normalizeBetweenArrays function (default option). I
need a clarification here: it has been explained many times that limma (and edgeR too) do not need to take
into account gene length as they perform gene-wise statistical tests: feature length does not change and
so it is good to take into account library size. It has also been explained that this method is good for DGE
but does not necessarily works for "absolute quantification" of a transcript. But. AFAIK (but I may be
wrong) quantile normalization works by considering all features in e
 ach expression quantile together; in the same quantile there could be low expressed long genes and highly
expressed short genes (if we only consider CPM), and I'm missing the rationale behi!
 nd. If the normalization process is correct I should be able to use y$E values as "absolute" quantification
(or absolute-like) of a transcript. If the y$E cannot be used as absolute quantification, is quantile
normalization the proper way to do this?
Sorry for the confused mail, it's early morning and I really need some sleep...

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