[BioC] problems about cDNA vs genomic arrays normalization

[Jenny Drnevich]

Hi Yanju,

I have just been working with a couple of data sets similar to yours where 
a) one channel has the same reference and b) the assumptions of few 
differences between sample and reference are not necessarily upheld. In 
these cases I have been using the Rquantile or Gquantile methods of 
normalizeBetweenArrays() in limma. These methods will do a quantile 
normalization on the R or G channel indicated so they have the "same 
empirical distribution across arrays, leaving the M-values (log-ratios) 
unchanged." Say your reference is in the green channel - doing a Gquantile 
normalization would force all the reference values to have the same 
distribution, and then adjust the R channel values accordingly. For the 
statistical analysis, you use the R values directly because if you use the 
M values, it would be like you never did the normalization. If the 
reference is not all in the same channel, I manipulate the RGList so that 
they are all in the same channel, but then I also include 'dye' as a batch 
effect in the model.

HTH,
Jenny

At 10:32 AM 11/20/2006, yanju wrote:
>Dear all,
>
>I have got a microarray dataset derived from common reference design.
>The common reference is gemoic DNA.  In normal normalization, we assume
>that  large fraction of genes is not differently expressed, then the
>adjustment strategies are used to let the log-ratios have a median(mean)
>of 0. But in my case, every spot would have the same observed signal in
>the genomic channel while the signals in the cDNA channel vary greatly.
>Therefore, the strategies that i just mentioned are not suitable. I was
>wondering how to normalize this kinds of data? Is that any packages or
>functions existed already? Expecting your reply.
>
>Regards,
>Yanju
>
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Jenny Drnevich, Ph.D.

Functional Genomics Bioinformatics Specialist
W.M. Keck Center for Comparative and Functional Genomics
Roy J. Carver Biotechnology Center
University of Illinois, Urbana-Champaign

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