Hello Simon, Thanks a lot for your answer. In fact from a previous email about VST that you answered, I suspected that maybe that might help me. I'll take a deeper look to that and to those papers, once again thank you very much!
On Mon, Mar 28, 2011 at 4:48 PM, Simon Anders <and...@embl.de> wrote: > Hi João > > > I'm studying the correlation of genes using RNAseq. I'm wondering if you >> know some literature that could help me in that direction.. In lack of >> better solutions, do you think non parametric things like spearman would >> do >> the job? >> > > In the DESeq package, we provide a function to perform a variance > stabilizing transformation, which then gives you the possibility to use > standard statistical techniques that require homoskedasticity. However, I do > not have much experience on how well this works in practice for applications > such as your. > > This is because calculating correlations per gene does not make much sense > unless you have many samples, and there are not that many published RNA-Seq > experiments with more than a few samples. Two of the very few such > large-scale RNA-Seq studies are the eQTL papers by Montomery et al. [Nature > 464 (2010) 773] and by Pickrell et al. [Nature 464 (2010) 768]. These papers > in fact use Spearman correlations. > > Simon > > _______________________________________________ > Bioc-sig-sequencing mailing list > Bioc-sig-sequencing@r-project.org > https://stat.ethz.ch/mailman/listinfo/bioc-sig-sequencing > -- João Moura [[alternative HTML version deleted]]
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