[BioC] Dye bias adjustment of Illumina Infinium Methylation data

[Shi, Tao]

Hi Pan,

Could you please clarify the "dye bias" you're referring to?   If you're 
referring to the bias between the two channels, I think it's clear to me from 
Illumina's document and the recent review article from Peter Laird in Nature 
Review Genetics, that there is no need of adjustment, as the two channels in 
Infinium are not corresponding to the methylation states.  The normalization 
method of adjusting median of the two channels as used in methylumi is 
inappropriate for Infinium data.

Could you please elaborate more on the color bias adjustment functions 
implemented in 

lumi?  I'm really curious to see.

Thanks!

...Tao






________________________________
From: Pan Du <dupan at northwestern.edu>
To: Ina Hoeschele <inah at vbi.vt.edu>
Cc: Simon Lin <S-Lin2 at northwestern.edu>; Sean Davis <sdavis2 at mail.nih.gov>; 
bioconductor at stat.math.ethz.ch
Sent: Tue, September 7, 2010 10:17:14 AM
Subject: Re: [BioC] Dye bias adjustment of Illumina Infinium Methylation data

Hi Ina

Based on our experience, dye bias does exist in most of our datasets, and
the bias is usually consistent within the same batch. If all your data is in
the same batch, then usually no color bias adjustment is necessary for
Illumina Infinium methylation data. This is consistent with the Illumina
explanation as you described. However, if your data includes several
different batches, then dye bias adjustment is important if the dye bias is
quite different across different batches.

The lumi package will include some color bias adjustment functions by the
end of this month (before the new release of Bioc). The lumi package will
also include support of Infinium 450K arrays in the future.


Pan


On 9/7/10 11:59 AM, "Ina Hoeschele" <inah at vbi.vt.edu> wrote:

> Can someone please clarify for me the need for dye bias adjustment for
> Illumina Infinium methylation data? Below is an 'explanation' from Illumina to
> justfy why no dye adjustment is needed:
> "We actually have two different bead types for each locus, where the 3'
> terminus corresponds to the methylated and unmethylated condition.  A single
> base extension is performed and the base after the methylation site is what
> actually determines the signal (red or green) for the locus.  Since the base
> proximal to the methylation site will always be the same whether the site is
> methylated or not, your overall signal will always be in one color for a given
> locus.  This means that no color normalization is necessary.  This is probably
> more easily explained by taking a look at cartoon in the attached Data Sheet."
> 
> Secondly, will methylumi, lumi and beadarray be able to deal with the new
> Infinium 450K methylation data?
> 
> Many thanks ... Ina
> 
> 
> ----- Original Message -----
> From: "Sean Davis" <sdavis2 at mail.nih.gov>
> To: "Chao-Jen Wong" <cwon2 at fhcrc.org>
> Cc: bioconductor at stat.math.ethz.ch
> Sent: Thursday, June 17, 2010 5:10:29 PM
> Subject: Re: [BioC] DNA-Methylation, CopyNumber Results
> 
> On Thu, Jun 17, 2010 at 4:43 PM, Chao-Jen Wong <cwon2 at fhcrc.org> wrote:
> 
>> As for Illumina Infinium methylation data, you can use the methylumi
>> package to  manipulate the data and perform quality control and
>> normalization. Subsequently use limma to do differential methylation
>> analysis.
>> 
>> 
> Just keep in mind that the data from the Illumina arrays are pretty
> non-normal, so some assumptions that come into play when using a parametric
> testing method may be in question.
> 
> Sean
> 
> 
> 
>> On 06/16/10 19:22, Noemi Andor wrote:
>>> Hi,
>>> 
>>> I need to parse some raw Methylation data from Illumina (Infinium) and
>> some copy number results (CGH, high amount of data). I would be grateful for
>> any useful information like which bioconducter packages to use, alternative
>> tools, normalization.
>>> 
>>> thank's in advance,
>>> 
>>> Noemi
>>> 
>>> _______________________________________________
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>> 
>> 
>> --
>> Chao-Jen Wong
>> Program in Computational Biology
>> Division of Public Health Sciences
>> Fred Hutchinson Cancer Research Center
>> 1100 Fairview Avenue N., M1-B514
>> PO Box 19024
>> Seattle, WA 98109
>> 206.667.4485
>> cwon2 at fhcrc.org
>> 
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> 
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