[BioC] Analysing DNA methylation microarrays in Bioconductor

[Paul Geeleher]

Of course, defining a 3rd state, "methylated", "unmethylated" and
"unsure" and only calling a reporter differentially methylated if they
are all methylated in one phenotype and unmethylated in the other
might also work. Cut-offs could be fairly arbitrary though.

Paul.

On Fri, Jul 23, 2010 at 6:54 PM, Paul Geeleher <paulgeeleher at gmail.com> wrote:
> 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
>



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