GCRMA explained in words
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@richard-friedman-513
Last seen 10.2 years ago
Dear Bioconductor list, I am trying to get a understanding and verbal description of GCRMA. I know that it involves fitting for non-specific binding in a GC-content dependent way, but it is not clear how. If I were to explain RMA in words (thanks to Benilton and other list members) I would say that the probe frequency vs intensity plot is fit with a Guassian Model for noise and an exponential model for signal. Is it correct to say that in GCRMA, the fit is the same as in RMA but with set of probes of a given GC content fit separately? If not, how would one put it with comparable simplicity? I've read the papers several times and some of the presentations on Dr. Irizarry's web-site, and I am still not sure that I understand how the fit is done. Thanks and best wishes, Rich ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Oncoinformatics Core Lecturer Department of Biomedical Informatics Box 95, Room 130BB or P&S 1-420C Columbia University Medical Center 630 W. 168th St. New York, NY 10032 (212)305-6901 (5-6901) (voice) friedman at cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ "42 is the answer. Dylan got it wrong. 'Blowin' in the wind' is not the answer. It isn't even a number' " - Rose Friedman, age 9
Cancer probe gcrma Cancer probe gcrma • 1.1k views
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@zhijin-jean-wu-1370
Last seen 10.2 years ago
TO put it simple: RMA and GCRMA share the same idea of assuming that the observed intensity of a probe is a sum of specific (S) and non-specific (B,background) components, and both compute posterior mean of specific component given the observed sum. The main differences are: -RMA assumes that background follows normal distribution. It also assumes that background for all probes are from the same normal distribution. Therefore RMA uses all probes to estimate the one set of parameters of that normal distribution. -GCRMA assumes log normal distribution for background. It also assumes that the parameters of that log normal distribution depends on the probe sequence (a little more complicated than just GC content). Therefore it uses "similar" probes to estimate those parameters. Which probes are similar is determined by their sequences. -GCRMA computes posterior expectation of log(S) but RMA computes posterior expectation of "S" Hope this helps, Jean Wu On Tue, 7 Feb 2006, Richard Friedman wrote: > Dear Bioconductor list, > > I am trying to get a understanding > and verbal description of GCRMA. I know that > it involves fitting for non-specific binding in > a GC-content dependent way, but it is not clear how. > If I were to explain RMA in words (thanks to Benilton and other > list members) I would say that > the probe frequency vs intensity plot is fit with > a Guassian Model for noise and an exponential model > for signal. Is it correct to say that in GCRMA, the fit is > the same as in RMA but with set of probes of a given GC > content fit separately? If not, how would one put it with > comparable simplicity? I've read the papers several times > and some of the presentations on Dr. Irizarry's web-site, > and I am still not sure that I understand how the fit > is done. > > Thanks and best wishes, > Rich > ------------------------------------------------------------ > Richard A. Friedman, PhD > Associate Research Scientist > Herbert Irving Comprehensive Cancer Center > Oncoinformatics Core > Lecturer > Department of Biomedical Informatics > Box 95, Room 130BB or P&S 1-420C > Columbia University Medical Center > 630 W. 168th St. > New York, NY 10032 > (212)305-6901 (5-6901) (voice) > friedman at cancercenter.columbia.edu > http://cancercenter.columbia.edu/~friedman/ > > "42 is the answer. Dylan got it wrong. 'Blowin' > in the wind' is not the answer. It isn't even > a number' " - Rose Friedman, age 9 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor >
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@james-w-macdonald-5106
Last seen 1 hour ago
United States
Richard Friedman wrote: > Dear Bioconductor list, > > I am trying to get a understanding > and verbal description of GCRMA. I know that > it involves fitting for non-specific binding in > a GC-content dependent way, but it is not clear how. > If I were to explain RMA in words (thanks to Benilton and other > list members) I would say that > the probe frequency vs intensity plot is fit with > a Guassian Model for noise and an exponential model > for signal. This isn't an explanation of RMA, but an explanation of how the background is estimated when running RMA (an explanation of RMA would need to talk about robust model fitting). Is it correct to say that in GCRMA, the fit is > the same as in RMA but with set of probes of a given GC > content fit separately? Basically, yes. The idea is to 'bin' MM probes based on the GC content, and then estimate the background for each bin. Since the background binding will be affected by the GC content of the probes, you get a better estimate of background than you would if you ignore the GC content, which is what RMA does. After background correction, GCRMA and RMA are identical. HTH, Jim If not, how would one put it with > comparable simplicity? I've read the papers several times > and some of the presentations on Dr. Irizarry's web-site, > and I am still not sure that I understand how the fit > is done. > > Thanks and best wishes, > Rich > ------------------------------------------------------------ > Richard A. Friedman, PhD > Associate Research Scientist > Herbert Irving Comprehensive Cancer Center > Oncoinformatics Core > Lecturer > Department of Biomedical Informatics > Box 95, Room 130BB or P&S 1-420C > Columbia University Medical Center > 630 W. 168th St. > New York, NY 10032 > (212)305-6901 (5-6901) (voice) > friedman at cancercenter.columbia.edu > http://cancercenter.columbia.edu/~friedman/ > > "42 is the answer. Dylan got it wrong. 'Blowin' > in the wind' is not the answer. It isn't even > a number' " - Rose Friedman, age 9 > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor -- James W. MacDonald Affymetrix and cDNA Microarray Core University of Michigan Cancer Center 1500 E. Medical Center Drive 7410 CCGC Ann Arbor MI 48109 734-647-5623
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Jim, Thanks!. So I do understand GCRMA! I should have said the "background correction portion" of GCRMA which is the part I was having trouble with. I realize that the background correction portion is followed quantile normalization and robust multichip summarization via (according to my most recent information) median polish. Best wishes, Rich On Feb 8, 2006, at 8:34 AM, James W. MacDonald wrote: > Richard Friedman wrote: >> Dear Bioconductor list, >> I am trying to get a understanding >> and verbal description of GCRMA. I know that >> it involves fitting for non-specific binding in >> a GC-content dependent way, but it is not clear how. >> If I were to explain RMA in words (thanks to Benilton and other >> list members) I would say that >> the probe frequency vs intensity plot is fit with >> a Guassian Model for noise and an exponential model >> for signal. > > This isn't an explanation of RMA, but an explanation of how the > background is estimated when running RMA (an explanation of RMA would > need to talk about robust model fitting). > > Is it correct to say that in GCRMA, the fit is >> the same as in RMA but with set of probes of a given GC >> content fit separately? > > Basically, yes. The idea is to 'bin' MM probes based on the GC > content, and then estimate the background for each bin. Since the > background binding will be affected by the GC content of the probes, > you get a better estimate of background than you would if you ignore > the GC content, which is what RMA does. > > After background correction, GCRMA and RMA are identical. > > HTH, > > Jim > > > If not, how would one put it with >> comparable simplicity? I've read the papers several times >> and some of the presentations on Dr. Irizarry's web-site, >> and I am still not sure that I understand how the fit >> is done. >> Thanks and best wishes, >> Rich >> ------------------------------------------------------------ >> Richard A. Friedman, PhD >> Associate Research Scientist >> Herbert Irving Comprehensive Cancer Center >> Oncoinformatics Core >> Lecturer >> Department of Biomedical Informatics >> Box 95, Room 130BB or P&S 1-420C >> Columbia University Medical Center >> 630 W. 168th St. >> New York, NY 10032 >> (212)305-6901 (5-6901) (voice) >> friedman at cancercenter.columbia.edu >> http://cancercenter.columbia.edu/~friedman/ >> "42 is the answer. Dylan got it wrong. 'Blowin' >> in the wind' is not the answer. It isn't even >> a number' " - Rose Friedman, age 9 >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor > > > -- > James W. MacDonald > Affymetrix and cDNA Microarray Core > University of Michigan Cancer Center > 1500 E. Medical Center Drive > 7410 CCGC > Ann Arbor MI 48109 > 734-647-5623 > ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Oncoinformatics Core Lecturer Department of Biomedical Informatics Box 95, Room 130BB or P&S 1-420C Columbia University Medical Center 630 W. 168th St. New York, NY 10032 (212)305-6901 (5-6901) (voice) friedman at cancercenter.columbia.edu http://cancercenter.columbia.edu/~friedman/ "42 is the answer. Dylan got it wrong. 'Blowin' in the wind' is not the answer. It isn't even a number' " - Rose Friedman, age 9
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