The mean-variance plot should be far "more" horizontal with M-values than
beta-values; have you plotted it against total intensity?  You end up going
down the rabbit hole eventually due to copy number variation, but plotting
m-value variance against the mean, the line of best fit is nearly flat
across the range of values.  The variance is more U-shaped (as opposed to
the "n" shape with beta values).

You could try an arcsin transform

asin(sqrt(beta)))

if your primary goal is to stabilize the variance, though Dr. Smyth's
suggestion will probably be better for sensitivity in the end.

Just a thought.  There are many ways to transform a proportion and they all
have relative strengths and weaknesses in practice.



On Thu, Aug 2, 2012 at 4:19 PM, Gordon K Smyth <smyth@wehi.edu.au> wrote:

> 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@gmail.com>
>> To: bioconductor@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
>>
>
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