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