[Bioc-devel] Use of confounders in downstream analysis

Kasper Daniel Hansen kasperdanielhansen at gmail.com
Tue Apr 21 21:35:07 CEST 2015

Well, your question is a very general analysis approach question, and not
really related to Bioconductor per se.  Helping people with these kinds of
questions is something I, and many others, find difficult over the
internet, especially very open ended questions.


On Tue, Apr 21, 2015 at 3:19 PM, Aileen Bahl <aileen.bahl at helsinki.fi>

> Thanks and sorry,
> I didn't get a lot of response at the Bioconductor support site and thus
> tried it here. However, good to know where would be the best place...
> Best,
> Aileen
> Zitat von Sean Davis <seandavi at gmail.com>:
>  Hi, Aileen.
>> This list isn't really the best place to ask questions like this and is
>> really reserved for discussion around package development.  Could you
>> please post to:
>> https://support.bioconductor.org/
>> That way, you benefit from more eyes and everyone benefits from potential
>> answers.
>> Thanks,
>> Sean
>> On Tue, Apr 21, 2015 at 12:31 PM, Aileen Bahl <aileen.bahl at helsinki.fi>
>> wrote:
>>  Dear all,
>>> I have some problems in understanding how exactly to include confounders
>>> in my downstream analysis. I will provide a short description of my
>>> analysis and problem and I would be very happy if some of you could help
>>> me
>>> understanding how exactly to go ahead with that:
>>> I normalized 450k data and then used lmFit() to find differentially
>>> methylated CpGs. My design matrix looks like this:
>>> model.matrix(~Pair+FatPercentage+EstradiolLevel). So, basically I want to
>>> identify CpG sites that are associated with changes in estradiol levels.
>>> As
>>> I want to perform within-pair analysis of monozygotic twins I added pair
>>> information looking like c(1,1,2,3,2,3...). I also added the fat
>>> percentage
>>> as a confounder as we saw significant correlations with the first
>>> principal
>>> component of the data. Does this look right to you?
>>> Now, after having identified significantly differentially methylated
>>> CpGs,
>>> we want to use the GSA package and look at correlations between
>>> methylation
>>> and expression data. For GSA the pairs can be specified directly in the
>>> function call. Does that also work with continuous traits or only if you
>>> have to groups? Additionally, I am not really sure how to include
>>> confounders then. Do I have to use adjusted or unadjusted data? If I use
>>> adjusted data, would I use the same design matrix as above and not
>>> include
>>> pair information in the function call? Would that be still a within-pair
>>> comparison then? And for the adjustment itself, would it be something
>>> like
>>> adj.m <- normalizedM-fit$coef[,-1]%*%t(myDesign[,-1]) or do I also have
>>> to
>>> include the columns for pair and fat percentage in this adjustment
>>> somehow?
>>> If I don't have to use unadjusted data, how would I include information
>>> on
>>> fat percentage and the estradiol levels then?
>>> Similarly, for the correlations between methylation and expression... Do
>>> I
>>> just use the adjusted data sets and then compute correlations over all
>>> individuals? Is that then still considering the within-pair changes? Or
>>> would I use delta betas for correlation analysis? In the latter case,
>>> would
>>> I use adjusted data? Would that then be like adjusting for pair twice if
>>> I
>>> use the design matrix from above? Or would I have to change the matrix
>>> and
>>> if yes, how?
>>> One last thing - say I wanted to perform differential analysis between
>>> two
>>> groups (not within-pair) but still have some twin pairs included in the
>>> analysis, would I then used duplicateCorrelation() instead of including
>>> the
>>> pair information directly in the design matrix? Or if that's not the
>>> right
>>> way to go, what should I do in that case?
>>> Sorry for that many questions! However, I would really appreciate any
>>> kind
>>> of help or ideas, to be able to understand how to go on...
>>> Thanks a lot in advance and best regards,
>>> Aileen
>>> _______________________________________________
>>> Bioc-devel at r-project.org mailing list
>>> https://stat.ethz.ch/mailman/listinfo/bioc-devel
> _______________________________________________
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> https://stat.ethz.ch/mailman/listinfo/bioc-devel

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