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 <anders@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]]

