nb. I should have written:
"the variance of the M-value variance as a function of the mean is more
U-shaped towards the extremes, versus the n shape for betas"
My apologies.
--t
On Thu, Aug 2, 2012 at 7:16 PM, Tim Triche, Jr. wrote:
> 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 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
>>> 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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>
>
>
> --
> *A model is a lie that helps you see the truth.*
> *
> *
> Howard Skipper
>
>
--
*A model is a lie that helps you see the truth.*
*
*
Howard Skipper
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