Hi Tim.

I didn't mean we don't normalization methylation data because there is no
standard method.  What I want to say is the most of the existing
normalization methods are derived from microarray which don't fit the
methylation data. Most of these methods such as
quantile normalization assume that  most genes are not differentially
expressed. However, In DNA methylation data, global hypomethylation is
observed in many diseases such as cancer . Improper normalization method
would erase the real biological difference.

Jack

2012/6/19 Tim Triche, Jr. <tim.triche@gmail.com>

> On Tue, Jun 19, 2012 at 7:46 AM, Yao Chen <chenyao.bioinfor@gmail.com>wrote:
>
>> As for as I know, this is no standard normalization for methylation data.
>
>
> As far as I know, there is no standard for microarray or RNAseq
> normalization either!  But that doesn't mean an investigator should ignore
> the issue of technical (as opposed to biological) fixed or varying effects
> in their data.  Especially if it could materially impact the outcome of a
> study.  lumi offers quantile normalization, minfi & methylumi will do dye
> bias normalization, etc.
>
> For example, GenomeStudio appears to choose a reference array for dye bias
> adjustment within each batch of 450k samples, and correct using the
> normalization controls so that the chips in the run have equivalent Cy3:Cy5
> bias to the reference.  This is less than optimal if you then want to
> compare with another, separate batch.  Personally I feel that it's better
> to start from IDATs.
>
> Another possibility is pernicious batch effects -- something like ComBat
> seems to work very well for those, usually, although as noted it's always
> up to the investigator to ensure that they are reporting on biologically
> (vs. technically) interesting differences.
>
> See for example http://www.biomedcentral.com/1755-8794/4/84
>
>
>> For me, I prefer keeping the raw value and just adjusting the technical
>> variants. Anyone has better solution. Please let me know.
>
>
> See above.  If the usual MDS plots indicate a supervised effect, one
> should fix it, preferably on the logit scale with ComBat, SVA, or something
> else appropriate to the task (i.e. if you're doing unsupervised analyses, a
> different method might be optimal).
>
>  thanks,
>
> --t
>
>
>
> Jack
>>
>> 2012/6/19 Tim Triche, Jr. <tim.triche@gmail.com>
>>
>>>  Look up Andrew Jaffe and Rafa Irizarry's paper on "bump hunting" for
>>> regional differences.  Or run a smooth over it (caveat: I just wrote
>>> smoothing "the way I want it" yesterday, after being provoked by a
>>> collaborator, so you might have to use lumi).
>>>
>>> The function "dmrFinder" in the "charm" package is specifically meant for
>>> this sort of thing.
>>>
>>> Also, if you're doing linear tests, be careful with normalization, mask
>>> your SNPs and chrX probes, and maybe use M-values (logit(beta)) for the
>>> task.  The latter is more important for epidemiological datasets than
>>> something like cancer, where every single interesting result from M-value
>>> testing has been reproduced using untransformed beta values when I ran
>>> comparisons (e.g. HELP hg17 methylation differences for IDH1/2 mutants
>>> vs.
>>> Illumina hm450 differences for IDH1/2 mutants, the complete absence of
>>> any
>>> differences for TET2 mutants regardless of platform, etc.)
>>>
>>> Mark Robinson just chimed in, I see.  Probably a good idea to read his
>>> reply carefully as well.
>>>
>>>
>>>
>>> On Tue, Jun 19, 2012 at 3:57 AM, Gustavo Fernández Bayón
>>> <gbayon@gmail.com>wrote:
>>>
>>> > Hi everybody.
>>> >
>>> > As a newbie to bioinformatics, it is not uncommon to find difficulties
>>> in
>>> > the way biological knowledge mixes with statistics. I come from the
>>> Machine
>>> > Learning field, and usually have problems with the naming conventions
>>> > (well, among several other things, I must admit). Besides, I am not an
>>> > expert in statistics, having used the barely necessary for the
>>> validation
>>> > of my work.
>>> >
>>> > Well, let's try to be more precise. One of the topics I am working more
>>> > right now is the analysis of methylation array data. As you surely
>>> now, the
>>> > final processed (and normalized) beta values are presented in a pxn
>>> matrix,
>>> > where there are p different probes and n different samples or
>>> individuals
>>> > from which we have obtained the beta-values. I am not currently working
>>> > with the raw data.
>>> >
>>> > Imagine, for a moment, that we have identified two regions of probes, A
>>> > and B, with a group of nA probes belonging to A, another group (of nB
>>> > probes) that belongs to B, and the intersection is empty. Say that we
>>> want
>>> > to find a way to show there is a statistically significant difference
>>> > between the methylation values of both regions.
>>> > As far as I have seen in the literature, comparisons (statistical
>>> tests)
>>> > are always done comparing the same probe values between case and
>>> control
>>> > groups of individuals or samples. For example, when we are trying to
>>> find
>>> > differentiated probes.
>>> >
>>> > However, if I think of directly comparing all the beta values from
>>> region
>>> > A (nA * n values) against the ones in region B (nB * n values) with a,
>>> say,
>>> > t test, I get the suspicion that something is not being done the way it
>>> > should. My knowledge of Biology and Statistics is still limited and I
>>> > cannot explain why, but I have the feeling that there is something
>>> formally
>>> > wrong in this approximation. Am I right?
>>> >
>>> > What I have done in similar experiments has been to find differentiated
>>> > probes, and then do a test to the proportion of differentiated probes
>>> to
>>> > total number of them, so I could assign a p-value to prove that there
>>> was a
>>> > significant influence of the region of reference.
>>> >
>>> > Several questions here: which could be a coherent approximation to the
>>> > regions A and B problem stated above? Is there any problem with
>>> methylation
>>> > data I am not aware of which makes only the in-probe analysis valid?
>>> Any
>>> > bibliographic references that could help me seeing the subtleties
>>> around?
>>> >
>>> > As you can see, concepts are quite interleaved in my mind, so any help
>>> > would be very appreciated.
>>> > Regards,
>>> > Gustavo
>>> >
>>> >
>>> >
>>> >
>>> > ---------------------------
>>> > Enviado con Sparrow (http://www.sparrowmailapp.com/?sig)
>>> >
>>> > _______________________________________________
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>>> >
>>>
>>>
>>>
>>> --
>>> *A model is a lie that helps you see the truth.*
>>> *
>>> *
>>> Howard Skipper<
>>> http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
>>>
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>>>
>>>
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>>
>>
>
>
> --
> *A model is a lie that helps you see the truth.*
> *
> *
> Howard Skipper<http://cancerres.aacrjournals.org/content/31/9/1173.full.pdf>
>
>

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