Steve!

I was thinking along these same lines: estimating dispersions then using a
variance stabilizing transformation. However, I am not sure how proper this
approach is?

Can anyone confirm the validity of this approach?

Michael


On Mon, Jul 15, 2013 at 3:58 PM, Steve Lianoglou
<lianoglou.steve@gene.com>wrote:

> Hi,
>
> On Mon, Jul 15, 2013 at 2:42 PM, Michael Breen
> <breenbioinformatics@gmail.com> wrote:
> > Hi all,
> > We have a large RNAseq data set. Apart from identifying differentially
> > expressed genes with these data we are also interested in classification
> in
> > terms of developing a pronostic and diagnostic classifier.
> >
> > Normally, our approach would utilize a machine learning classifier, as
> SVM,
> > and typically proceed with a nested cross-validation approach.
> >
> >
> > The vast majority of these programs and packages have been designed
> > utilizing microarray data.
> >
> > Are there any reasonable biases which one should consider before using
> such
> > already published approaches on RNAseq data?
> >
> > Do the distributions of the different data types matter at all?
> >
> > If so, does an application exist using an SVM taking into consideration
> > RNAseq raw counts?
>
> One approach would be to take the output from one of the variance
> stabilizing transformations in DESeq2 as the input to your machine
> learning approach.
>
> See:
>
> R> library(DESeq2)
> R> ?varianceStabilizingTransformation
>
> and the Section 7 of the DESeq2 vignette (count data transformations):
>
>
> http://bioconductor.org/packages/release/bioc/vignettes/DESeq2/inst/doc/DESeq2.pdf
>
> HTH,
> -steve
>
> --
> Steve Lianoglou
> Computational Biologist
> Bioinformatics and Computational Biology
> Genentech
>

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