[BioC] Variance stabilization of m-values

[Gustavo Fernández Bayón]

Hi Wolfgang,

First of all, I apologize for the late reply. As I have answered in a previous mail, there have been major reasons that have kept me away from the e-mail.

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El sábado 4 de agosto de 2012 a las 00:32, Wolfgang Huber escribió:

> Dear Gustavo
>  
> the two issues:
> - whether filtering of probes by overall variance is admissible and  
> helpful for your analysis
> - whether the variance depends on the mean
> are unrelated. If I understand your question correctly (and I am not  
> sure I do), then you should filter on the overall variance of the M  
> values, and need not worry about the mean-variance relationship.

I was thinking about that, when I noticed that the curve showing that relationship really had nearly no influence on a filtering of that kind. I.e., if I want to get rid of those probes whose variance is low, those are quite homogenous in the graph behavior. Well, I should have to re-think this, as I currently have to re-create the pipeline.
>  
> Can you check the paper on this topic ("Independent filtering increases  
> detection power for high-throughput experiments",  
> http://www.pnas.org/content/107/21/9546.long) and get back if it is  
> still unclear?
>  
I'll give it a read. Thank you very much for the link.
  
>  
> Best wishes
> Wolfgang

Thank you, as always, for your interesting hints and references.

Regards,
Gus
  
>  
>  
> Aug/3/12 1:19 AM, Gordon K Smyth scripsit::
> > Use eBayes with trend=TRUE later in the pipeline, then variance
> > stabilization may not be needed.
> >  
> > Gordon
> >  
> > > Date: Wed, 1 Aug 2012 15:20:56 +0200
> > > From: Gustavo Fern?ndez Bay?n <gbayon at gmail.com (mailto:gbayon at gmail.com)>
> > > To: bioconductor at r-project.org (mailto:bioconductor at r-project.org)
> > > Subject: [BioC] Variance stabilization of m-values
> > >  
> > > Hi everybody.
> > >  
> > > I am working with Illumina 450k methylation data. I am currently
> > > cleaning a data set, getting rid of XY probes, etc? and I would like
> > > to do a non-specific filtering and preserve only 20% of the probes,
> > > those with the higher variability (as seen in Chapter 7 of the
> > > Bioconductor Case Studies book).
> > >  
> > > In the book, they create a meanSdPlot() and proceed as the variance is
> > > not dependent on the mean (to a significant degree).
> > >  
> > > Trying to follow that procedure, I have converted my beta values to
> > > M-values, and then called meanSdPlot(). It shows, for my data, that
> > > there is a relationship between mean and variance, i.e. the line with
> > > the median is not horizontal. Of course, if I create a meanSdPlot with
> > > the beta values, the effect is greater, due to their heteroscedasticity.
> > >  
> > > Question: Is it correct to use a variance stabilization transformation
> > > (as the one in justvsn) on the M-values in order to discard
> > > low-variance probes?
> > >  
> > > Any hint will be much appreciated.
> > >  
> > > Regards,
> > > Gus
> >  
> >  
> >  
> > ______________________________________________________________________
> > The information in this email is confidential and inte...{{dropped:21}}
>  
>  
>  
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