Hi,
Thanks for some very useful and interesting information. My problem could very much be related to the distribution of the values, but both methods use limma and eBayes, so both are using moderated t tests on the same data set.

The difference is that for Method 1 the difference in methylation within each twin pair is calculated by hand and treated as one sample by limma, while in Method 2 limma identifies the pairs and computes the differences (in my understanding).


Using Method 1 I'm getting really nice and 'expected' sort of results. Method 2 baffles me.

My thought is that even if the methylation data is bimodal, the computed differences within pairs don't have to be. Since the pairs are monozygotic twins, we can expect the differences in methylation within each pair to be close to zero for most CpG sites. So I kind of believed in the results from Method 1 (and also since the results made sense). 


With Method 2 I expected limma to compute the differences and treat the data the same way, but apparently it doesn't since I'm not getting the same results. 


Please correct me if I'm having any misunderstandings here.


Regards,
Khadeeja






________________________________
From: Andrew Teschendorff <a.teschendorff@ucl.ac.uk>
To: Kevin R. Coombes <kevin.r.coombes@gmail.com>; "ttriche@usc.edu" <ttriche@usc.edu>
Cc: James W. MacDonald <jmacdon@med.umich.edu>; "Tim Triche, Jr." <tim.triche@gmail.com>; "bioconductor@r-project.org" <bioconductor@r-project.org>; S
Sent: Friday, October 7, 2011 10:57 PM
Subject: RE: [BioC] Regression analysis problem


Dear Kevin,

How you treat the DNA methylation data will very much depend on the specific biological question you are trying to address. It is pretty clear
that if you have a mixture of different cell types, each with a different methylation pattern, that small yet  biologically
and clinically relevant changes in the cell type composition will lead to small yet statistically significant changes in methylation. 

The evidence for biologically relevant small changes in methylation (differences in means of ~0.1) is overwhelming, for instance in DNA methylation studies conducted on whole blood DNA where blood cell subtype composition changes in response to the presence of cancer.


kind regards
A.


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Andrew E Teschendorff   PhD
Heller Research Fellow
Statistical Cancer Genomics
Paul O'Gorman Building
UCL Cancer Institute
University College London
72 Huntley Street
London WC1E 6BT, UK.

Mob: +44 07876 561263
Email: a.teschendorff@ucl.ac.uk
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________________________________________
From: bioconductor-bounces@r-project.org [bioconductor-bounces@r-project.org] On Behalf Of Kevin R. Coombes [kevin.r.coombes@gmail.com]
Sent: 07 October 2011 18:26
To: ttriche@usc.edu
Cc: James W. MacDonald; Tim Triche,     Jr.; bioconductor@r-project.org; Sean Davis; khadeeja ismail
Subject: Re: [BioC] Regression analysis problem

To follow up on Tim's point:

I have yet to see any evidence that methylation data is anything other
than ternary. The typical interpretation for the beta values is
     less than 0.25 = fully unmethylated
     greater than 0.75 = fully methylated
     between 0.25 and 0.75 = partially methylated
Part of the evidence for this assertion is that we ahve several sets of
data from samples treated with drugs that should fully methylate (or
fully demethylate, respectively) everything. On those samples, 99% of
the observed values are above 0.75 (or below 0.25, respectively).

So I'm not convinced that t-tests have role to play in analyzing
genome-wide methylation data....

     Kevin

On 10/7/2011 10:39 AM, Tim Triche, Jr. wrote:
> On Fri, Oct 7, 2011 at 8:15 AM, James W. MacDonald<jmacdon@med.umich.edu>wrote:
>
>> First, by increasing the number of genes, you can more accurately
>> estimate an overall variance, which is then used in the eBayes() step to
>> 'shrink' your observed variance towards this overall variance. This is
>> one of the reasons that limma is so popular - by using information from
>> all genes, you can increase the power to detect differences in
>> individual genes.
>>
> Careful though -- this is methylation data, which tends to be strongly
> bimodal.  It's not clear that the assumption of a common variance across
> unmethylated, partially-methylated, and methylated sites is appropriate.  I
> seem to recall Gordon Smyth commenting upon this at one point -- perhaps
> he'll chime in.
>
>
>> Second, when you increase the number of pairs you are using, your power
>> to detect differences increases as well. This has nothing to do with
>> limma per se; it is just that the power of a t-test increases as you
>> increase the number of observations.
>>
> This is of course appropriate at any time that you can get more high-quality
> samples rather than fewer :-)
>

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