[BioC] Practical utilization of SQN in ST1.0 Gene normalization.

[Richard Friedman]

Tim,

	Thank you for your instructive reply. I am looking for a canned and  
validated
function based on SQN that can be applied as easily rma that could  
perhaps yield  differentially expressed
genes for a set of arrays where rma did not. I am not confident of my  
ability to develop
such a method. I am hoping that someone else has done so.

Best wishes,
Rich



On Oct 27, 2011, at 11:32 AM, Tim Triche, Jr. wrote:

> 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 at 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,
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> 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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