How to normalize one additional CEL file?
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Wiktor Mazin ▴ 30
@wiktor-mazin-4213
Last seen 10.2 years ago
Dear List, Suppose you normalize a training set in some way (RMA, etc) to build a predictor and then want to apply this predictor to a test set consisting of only 1 CEL file (actually, you may have several test set CEL files but you have to preprocess and predict them individually). How can you preprocess / normalize 1 (test set) CEL file using the same normalization settings used for the training set? (NB! Training and test set may not be normalized together) Are there some functions / packages for this purpose, or would you suggest some other way of resolving this? Looking forward to your reply. best regards, Wiktor Mazin [[alternative HTML version deleted]]
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@vincent-j-carey-jr-4
Last seen 9 weeks ago
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Another conceptually relevant Bioconductor package is frma. On Thu, Aug 12, 2010 at 9:20 AM, Wiktor Mazin <wiktor.mazin@gmail.com>wrote: > Dear List, > > Suppose you normalize a training set in some way (RMA, etc) to build a > predictor and then want to apply this predictor to a test set consisting of > only 1 CEL file (actually, you may have several test set CEL files but you > have to preprocess and predict them individually). > > How can you preprocess / normalize 1 (test set) CEL file using the same > normalization settings used for the training set? (NB! Training and test > set > may not be normalized together) > Are there some functions / packages for this purpose, or would you suggest > some other way of resolving this? > > Looking forward to your reply. > > best regards, > Wiktor Mazin > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: > http://news.gmane.org/gmane.science.biology.informatics.conductor > [[alternative HTML version deleted]]
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Wiktor Mazin ▴ 30
@wiktor-mazin-4213
Last seen 10.2 years ago
Dear List, Suppose you normalize a training set in some way (RMA, etc.) to create a predictor and then you want to apply this predictor on a single-CEL- file basis, that is, your test set consists of only 1 new CEL file. (actually your test set may consist of several new CEL files but they have to preprocessed and predicted individually) How can you preprocess / normalize one (test set) CEL file with the same settings used to preprocess the training set? (NB! training and test set may not be normalized together!) Are there some functions / packages that allow this, or would you suggest another way of dealing with this? Looking forward to hearing from you. Best regards, Wiktor Mazin
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@christian-ruckert-3294
Last seen 5.4 years ago
Germany
See bioconductor package RefPlus for an implementation of the RMA normalization which stores normalization parameters for later use on additional samples. Greetings, Christian Am 12.08.2010 15:20, schrieb Wiktor Mazin: > Dear List, > > Suppose you normalize a training set in some way (RMA, etc) to build a > predictor and then want to apply this predictor to a test set consisting of > only 1 CEL file (actually, you may have several test set CEL files but you > have to preprocess and predict them individually). > > How can you preprocess / normalize 1 (test set) CEL file using the same > normalization settings used for the training set? (NB! Training and test set > may not be normalized together) > Are there some functions / packages for this purpose, or would you suggest > some other way of resolving this? > > Looking forward to your reply. > > best regards, > Wiktor Mazin > > [[alternative HTML version deleted]] > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor
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Wiktor Mazin ▴ 30
@wiktor-mazin-4213
Last seen 10.2 years ago
Dear Rich (and Vincent), Thanks for making me aware of the fRMA package that does look interesting. However, why do I have to normalize all of the cels with fRMA at the beginning? In my case, the new samples will arrive sequentially and it will not be possible to normalize all cels at once. Is fRMA then still a good option? Also, I'm looking into miRNA samples - but I guess this is no problem as long as I use the function makeVectorPackage in frmaTools, right? Thanks for your assistance. best gards, Wiktor On Fri, Aug 13, 2010 at 6:24 PM, Richard Friedman < friedman@cancercenter.columbia.edu> wrote: > Dear Wiktor, > > I believe that the fRMA package will enable you to do what you want. > You would have to normalize all of the cels with fRMA athe the beginning. > > Best wishes, > 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 > http://cancercenter.columbia.edu/~friedman/<http: cancercenter.colu="" mbia.edu="" %7efriedman=""/> > > In Memoriam, > George Scithers > > > > > On Aug 12, 2010, at 8:28 AM, Wiktor Mazin wrote: > > Dear List, >> >> Suppose you normalize a training set in some way (RMA, etc.) to create a >> predictor and then you want to apply this predictor on a single- CEL-file >> basis, >> that is, your test set consists of only 1 new CEL file. (actually your >> test set >> may consist of several new CEL files but they have to preprocessed and >> predicted >> individually) >> >> How can you preprocess / normalize one (test set) CEL file with the same >> settings used to preprocess the training set? (NB! training and test set >> may not >> be normalized together!) >> Are there some functions / packages that allow this, or would you suggest >> another way of dealing with this? >> >> Looking forward to hearing from you. >> >> Best regards, >> Wiktor Mazin >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor@stat.math.ethz.ch >> https://stat.ethz.ch/mailman/listinfo/bioconductor >> Search the archives: >> http://news.gmane.org/gmane.science.biology.informatics.conductor >> > > [[alternative HTML version deleted]]
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Dear Wiktor, I was imprecise. fRMA, unlike RMA, gives the same normalization for each sample independt of the other samples. I have not as yet used it myself. Also, I am not familiar with affy miRNA chips or if there is an fRMA funcion for them yet. I hope this helps, Rich On Tue, 17 Aug 2010, Wiktor Mazin wrote: > Dear Rich (and Vincent), > > Thanks for making me aware of the fRMA package that does look interesting. > > However, why do I have to normalize all of the cels with fRMA at the beginning? > > In my case, the new samples will arrive sequentially and it will not be possible to normalize all cels at once. Is fRMA then still a good option? > > Also, I'm looking into miRNA samples - but I guess this is no problem as long as I use the function makeVectorPackage in frmaTools, right? > > Thanks for your assistance. > > best gards, > Wiktor > > > On Fri, Aug 13, 2010 at 6:24 PM, Richard Friedman <friedman at="" cancercenter.columbia.edu=""> wrote: > Dear Wiktor, > > ? ? ? ?I believe that the fRMA package will enable you to do what you want. > You would have to normalize all of the cels with fRMA athe the beginning. > > Best wishes, > 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/ > > In Memoriam, > George Scithers > > > > > On Aug 12, 2010, at 8:28 AM, Wiktor Mazin wrote: > > Dear List, > > Suppose you normalize a training set in some way (RMA, etc.) to create a > predictor and then you want to apply this predictor on a single-CEL-file basis, > that is, your test set consists of only 1 new CEL file. (actually your test set > may consist of several new CEL files but they have to preprocessed and predicted > individually) > > How can you preprocess / normalize one (test set) CEL file with the same > settings used to preprocess the training set? (NB! training and test set may not > be normalized together!) > Are there some functions / packages that allow this, or would you suggest > another way of dealing with this? > > Looking forward to hearing from you. > > Best regards, > Wiktor Mazin > > _______________________________________________ > Bioconductor mailing list > Bioconductor at stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/bioconductor > Search the archives: http://news.gmane.org/gmane.science.biology.informatics.conductor > > > > -- ------------------------------------------------------------ Richard A. Friedman, PhD Associate Research Scientist Herbert Irving Comprehensive Cancer Center Biomedical Informatics Shared Resource 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/ "The last 250 pages of the last Harry Potter book took place in one day because alot happened in that day. All of Ulysses takes place in one day and nothing happened in that day." -Rose Friedman, age 11
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