[BioC] MDS in edgeR

[Gordon K Smyth]

Dear Susanne,

Please leave top at the default value unless you have a good reason to 
change it.

Setting top to the whole genome would mean that you would be trying to 
distinguish your samples using a collection genes that are mostly either 
not differentially expressed between the samples or are not expressed at 
all.  This would increase the noise in your comparison and risk marking 
real patterns.

There is a large literature on unsupervised clustering, of which MDS is a 
type, and filtering the genes to those which contain real information for 
distinguishing the samples is pretty much universally recommended.  The 
exact number that are used is not important, but the fact that it is 
limited to more variable genes is.

Best wishes
Gordon

> Date: Wed, 7 Dec 2011 19:11:05 +0100
> From: Susanne Franssen <s.franssen at uni-muenster.de>
> To: bioconductor at r-project.org
> Subject: [BioC] MDS in edgeR
>
> Dear all / authors of the edgeR package,
>
> I have a question concerning the use of the multidimensional scaling
> plot provided by the edgeR package.
>
> I have RNA-seq data for 8 libraries from a 2x2 factorial design and I
> want produce a mds plot (plotMDS.DGEList) to get a better idea of the
> distances between the libraries.
> The function plotMDS.DGEList now offers me the option top. Here, I can
> choose the x (top=x) genes that show the highest tagwise dispersion
> looking at all libraries.
>
> My question is now, what I should consider for the choice of x?
> As I have a 2x2 factorial design I was going to choose x=number of all
> genes as I don't see a rational for choosing a specific number smaller
> number, which would seem somehow arbitrary to me.
>
> Are there any opinions on that?
>
> Thanks a lot,
> Susanne

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