[BioC] Analysing DNA methylation microarrays in Bioconductor

Paul Geeleher paulgeeleher at gmail.com
Fri Jul 23 19:54:27 CEST 2010


I understand your approach but the main problem I'd see with such a
thresholding approach is that you are highly likely to find regions
that are just below the cutoff to be called "methylated" in one
phenotype and just above the threshold in the other phenotype. Thus
most likely not differentially methylated at all. Do you see what I
mean?

Perhaps some kind of approach that labels each reporter as having a
probability of methylation (and hence a probability of unmethylation),
which can be compared across samples of a given phenotype to give a
probability of all reporters being methylated/unmethylated in each
phenotype, then compares these probabilities between phenotypes to
give a probability of "differential methylation". That's just off the
top of my head, I think it makes sense, but I'm presuming nothing like
that has actually been implemented?

Paul.

On Fri, Jul 23, 2010 at 6:45 PM, Steve Lianoglou
<mailinglist.honeypot at gmail.com> wrote:
> Hi,
>
> On Fri, Jul 23, 2010 at 1:35 PM, Paul Geeleher <paulgeeleher at gmail.com> wrote:
>> Thanks for your reply Claus,
>>
>> What I've noticed however about these and every other tool I've found
>> is that they seem to be able to characterize a methlyation pattern in
>> a sample. I.e. say "this region appears to be methylated in this
>> sample".
>>
>> What I'd like is something that can compare the methylation levels
>> between the samples, basically outputting a probability that a
>> region/reporter is methylated in one phenotype and unmethylated in the
>> other. It would be great if anyone could point me towards such a tool,
>> or confirm that it doesn't actually exist?
>
> Well, I guess it's impossible to say that something *doesn't* exist
> (cf. the black swan), but if you have tools that tell you "this region
> is methylated" in a given sample, can't you do this yourself?
>
> Say you use all of your replicate experiments to get a "golden answer"
> for regions methylated in disease. and regions methylated in
> "normals".
>
> I could imagine storing such info in an IRanges object (or IRangesList
> (one IRanges object for each chromosome), then just doing a
> setdiff(disease, normal) to see which ranges are methylated in disease
> and not normal.
>
> Isn't that a start?
>
> -steve
>
> --
> Steve Lianoglou
> Graduate Student: Computational Systems Biology
>  | Memorial Sloan-Kettering Cancer Center
>  | Weill Medical College of Cornell University
> Contact Info: http://cbio.mskcc.org/~lianos/contact
>



-- 
Paul Geeleher
School of Mathematics, Statistics and Applied Mathematics
National University of Ireland
Galway
Ireland
--
www.bioinformaticstutorials.com



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