Practical utilization of SQN in ST1.0 Gene normalization.
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@richard-friedman-513
Last seen 7.1 years ago
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 Irving Cancer Research Center Columbia University 1130 St. Nicholas Ave New York, NY 10032 (212)851-4765 (voice) friedman at cancercenter.columbia.edu 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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Tim Triche ★ 4.2k
@tim-triche-3561
Last seen 13 months ago
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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 > Irving Cancer Research Center > Columbia University > 1130 St. Nicholas Ave > New York, NY 10032 > (212)851-4765 (voice) > friedman@cancercenter.**columbia.edu <friedman@cancercenter.columbia.edu> > http://cancercenter.columbia.**edu/~friedman/<http: cancercenter.co="" lumbia.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 > > ______________________________**_________________ > Bioconductor mailing list > Bioconductor@r-project.org > https://stat.ethz.ch/mailman/**listinfo/bioconductor<https: stat.et="" hz.ch="" mailman="" listinfo="" bioconductor=""> > Search the archives: http://news.gmane.org/gmane.** > science.biology.informatics.**conductor<http: news.gmane.org="" gmane.="" science.biology.informatics.conductor=""> > -- If people do not believe that mathematics is simple, 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=""> [[alternative HTML version deleted]]
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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, > Center for Computational Biology and Bioinformatics (C2B2)/ > National Center for Multiscale Analysis of Genomic Networks (MAGNet) > Room 824 > Irving Cancer Research Center > Columbia University > 1130 St. Nicholas Ave > New York, NY 10032 > (212)851-4765 (voice) > friedman at cancercenter.columbia.edu > 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 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at r-project.org > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- > If people do not believe that mathematics is simple, > it is only because they do not realize how complicated life is. John > von Neumann
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On Thu, Oct 27, 2011 at 8:58 AM, Richard Friedman < friedman@cancercenter.columbia.edu> wrote: > 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. Wouldn't you be concerned about the trade-off between sensitivity and specificity if the only thing that distinguished DE results from non- DE results was the method? It seems a bit much to put such faith in methodology when biology is inherently complex. "Validation" usually consists of a handful of scenarios that the authors could think of. I am not confident of my ability to develop > such a method. I am hoping that someone else has done so. > The closest I've seen to this description was in the charm package, but... look in the code, you'll see what I mean. Validating a new method is pretty tricky even for people who have developed existing methods. And of course since new methods are far less sexy than new biology, there is a disincentive to exhaustively test all assumptions. JMHO -- If people do not believe that mathematics is simple, 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=""> [[alternative HTML version deleted]]
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