You answered your own question -- it is used for normalization, not
background correction or summarization.

The normal-exponential deconvolution model is standard for background
correction; a robust linear model is typically used for summarization (i.e.
in RMA) although depending on the application this may differ.  You might
want to look at the CHARM paper by Martin Aryee for some insights towards
designing preprocessing steps for a new platform, or Wei Shi's paper for
some insights about background correction as a separate step.  Just because
everyone uses RMA or GCRMA doesn't mean it's going to be the best option for
every oligonucleotide array.

FWIW, my experience has been that SQN does not necessarily perform exactly
as expected unless you have a substantial number (thousands) of control
probes with known properties (sequence, etc.).  Your mileage may vary. Be
sure to scrutinize the overall distribution of summary statistics with and
without SQN (i.e., compare to full quantile, loess, etc.) while
experimenting with it.  The method seems to be more sensitive to
assumptions.



On Thu, Oct 27, 2011 at 7:11 AM, Richard Friedman <
friedman@cancercenter.columbia.edu> wrote:

> Dear Bioconductor List,
>
>        I read Zhijin Wu and Martin Aryee's paper of subset Quantile
> normalization with great interest.
> The method is implemented in a CRAN package SQN which requires the labeling
> of the negative control
> probes. It is not clear to me how to identify those probes for the
> Affymetrix Gene ST1.0 chips.
> It is also not clear to me how to integrate this step into the usual
> normalization workflow to replace
> rma for the chip metioned above. As I understand it, SQN would only replace
> the quantile normalization
> step, but not the background correction or summarization steps. How then
> can it be used for practical normalization
> in oligo. I would appreciate any suggestions members of the list might
> have.
>
> Thank you as always,
> Rich
> ------------------------------**------------------------------
> Richard A. Friedman, PhD
> Associate Research Scientist,
> Biomedical Informatics Shared Resource
> Herbert Irving Comprehensive Cancer Center (HICCC)
> Lecturer,
> Department of Biomedical Informatics (DBMI)
> Educational Coordinator,
> Center for Computational Biology and Bioinformatics (C2B2)/
> National Center for Multiscale Analysis of Genomic Networks (MAGNet)
> Room 824
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> 1130 St. Nicholas Ave
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> (212)851-4765 (voice)
> friedman@cancercenter.**columbia.edu <friedman@cancercenter.columbia.edu>
> http://cancercenter.columbia.**edu/~friedman/<http://cancercenter.columbia.edu/~friedman/>
>
> I am a Bayesian. When I see a multiple-choice question on a test and I
> don't
> know the answer I say "eeney-meaney-miney-moe".
>
> Rose Friedman, Age 14
>
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-- 
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it is only because they do not realize how complicated life is. John von
Neumann<http://www-groups.dcs.st-and.ac.uk/~history/Biographies/Von_Neumann.html>

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