design matrix
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Lev Soinov ▴ 470
@lev-soinov-2119
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@james-w-macdonald-5106
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Lev Soinov wrote: > Dear List, > > I have got a very simple, I suppose, question. > Is there any difference between the following two designs if I just want to make pairwise comparisons between three groups as in the LIMMA user guide, p.31, Affymetrix and Other Single-Channel Designs. > 1. > > data <- ReadAffy() > > eset <- rma(data) > > design <- model.matrix(~ -1+factor(c(1,1,1,2,2,3,3,3))) > > colnames(design) <- c("group1", "group2", "group3") > > fit <- lmFit(eset, design) > > contrast.matrix <- makeContrasts(group2-group1, group3-group2, group3-group1, levels=design) > > fit2 <- contrasts.fit(fit, contrast.matrix) > > fit2 <- eBayes(fit2) > > 2. > > data <- ReadAffy() > > eset <- rma(data) > > design <- model.matrix(~ 0+factor(c(1,1,1,2,2,3,3,3))) > > colnames(design) <- c("group1", "group2", "group3") > > fit <- lmFit(eset, design) > > contrast.matrix <- makeContrasts(group2-group1, group3-group2, group3-group1, levels=design) > > fit2 <- contrasts.fit(fit, contrast.matrix) > > fit2 <- eBayes(fit2) > > The only difference between the above two codes is in use of the design <- model.matrix(~ 0+factor(c(1,1,1,2,2,3,3,3))) or design <- model.matrix(~ -1+factor(c(1,1,1,2,2,3,3,3))). They produce identical design matrices and, as far as I can see, identical results in fit2. So, can we use "0" and "-1" here interchangeably? > Since they produce identical design matrices it would appear they are identical, no? Anyway, the help page you are looking for is ?formula. From the 'Details' section: It can also used to remove the intercept term: 'y ~ x - 1' is a line through the origin. A model with no intercept can be also specified as 'y ~ x + 0' or 'y ~ 0 + x'. Best, Jim > With kind regards, > Lev. > > > --------------------------------- > > [[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 -- James W. MacDonald, M.S. Biostatistician 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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?write.fit >>> Lev Soinov <lev_embl1 at="" yahoo.co.uk=""> wrote: > Dear List, > > Could you help me with another small issue? > I usually write out the results of my analysis using the write.table > function as follows: > > Assuming 30000 probes in the dataset: > data <- ReadAffy() > eset <- rma(data) > > design <- model.matrix(~ -1+factor(c(1,1,1,2,2,3,3,3))) > colnames(design) <- c("group1", "group2", "group3") > contrast.matrix <- makeContrasts(group2-group1, group3-group2, group3-group1, > levels=design) > > fit <- lmFit(temp, design) > fit2 <- contrasts.fit(fit, contrast.matrix) > fit2 <- eBayes(fit2) > > C1<-topTable(fit2, coef=1, number=30000, adjust=*BH*) > > write.table(C1,file="comparison1.txt*,append=TRUE,quote=FALSE,sep="\t" ,row.n > ames=TRUE,col.names=FALSE) > > C2<-topTable(fit2, coef=2, number=30000, adjust=*BH*) > > write.table(C2,file="comparison2.txt*,append=TRUE,quote=FALSE,sep="\t" ,row.n > ames=TRUE,col.names=FALSE) > > C3<-topTable(fit2, coef=3, number=30000, adjust=*BH*) > > write.table(C3,file="comparison3.txt*,append=TRUE,quote=FALSE,sep="\t" ,row.n > ames=TRUE,col.names=FALSE) > > I then use the written out txt files (comparison1.txt, comparison2.txt and > comparison3.txt) to select significant probes on the basis of log2fold change > and adjusted p values thresholds, using Excel. > Would you say that this is a correct way to do this and could you please > recommend me some other, may be more efficient way of writing the results of > topTable for all 30000 probes out? > > With kind regards, > Lev. > > > --------------------------------- > For ideas on reducing your carbon footprint visit Yahoo! For Good this > month. ********************************************************** Electronic Mail is not secure, may not be read every day, and should not be used for urgent or sensitive issues
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Dear list, In order to normalise my data using fitPLM(), I need to create an affybatch using ReadAffy(). This time I think justRMA() is not an option I can use. Has anyone been successful at reading in a large group of Plus chips (say 100) using ReadAffy() and the normalise them using fitPLM() on a "normal" PC on Windows? What amount of memory is needed for it? Any examples on how many chips you might have loaded with memory details would be very appreciated. Regards, David
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Hi David, since we also had a similar problem (actually, we wanted to apply fitPLM to more than 400 Plus2 chips), I wrote a just-Version of fitPLM. This just-version works for up to about 100 Plus2 chips on an AMD Athlon XP 3000+ with 1 GB RAM. I have also implemented a function that allows to fit PLM models to several hundreds of Plus2 arrays by splitting up the problem in several chunks. (The background correction and the quantile normalization is done chipwise, afterwards the chips are split in several chunks such that the resulting PLM signals are the same as the ones that would have been returned by fitPLM if fitPLM could have been used.) For details, see Chapter 5 of https://eldorado.uni- dortmund.de/bitstream/2003/23306/1/diss_schwender.pdf If you are interested in any of these functions then let me know. But please note that these functions were written more than a year ago using the BioC 1.8 (??) version of affyPLM. Haven't used these function since then. So no guarantee that they also work using the BioC 2.1 version of affyPLM. (But should work if Ben Bolstad did not recode the C code and the internal affyPLM functions in fitPLM. One problem might be that the resulting object is an object of class exprSet and not ExpressionSet.) I was thinking about publishing these functions. But didn't do it because of the R packages BufferedMatrix and oligo, in which the idea of splitting the problem into several chunks is implemented in a more elegant way. Best, Holger -------- Original-Nachricht -------- > Datum: Fri, 5 Oct 2007 13:57:53 +0200 (CEST) > Von: darteta001 at ikasle.ehu.es > An: bioconductor at stat.math.ethz.ch > Betreff: [BioC] reading many affy U133 Plus_2.0 > Dear list, > > In order to normalise my data using fitPLM(), I need to create an > affybatch using ReadAffy(). This time I think justRMA() is not an > option I can use. > > Has anyone been successful at reading in a large group of Plus chips > (say 100) using ReadAffy() and the normalise them using fitPLM() on > a "normal" PC on Windows? What amount of memory is needed for it? > Any examples on how many chips you might have loaded with memory > details would be very appreciated. > > Regards, > > David > > _______________________________________________ > 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 -- Psssst! Schon vom neuen GMX MultiMessenger geh?rt? Der kanns mit allen: http://www.gmx.net/de/go/multimessenger
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David, You might have a look at the R package 'aroma.affymetrix' ... you should be able to read/process/analyze 100s of affy chips. I regularly process >50 exon arrays, which are far larger than U133s. I believe you can do it all in less than 500M RAM. Full details are at: http://groups.google.com/group/aroma-affymetrix Cheers, Mark On 05/10/2007, at 9:57 PM, <darteta001 at="" ikasle.ehu.es=""> <darteta001 at="" ikasle.ehu.es=""> wrote: > Dear list, > > In order to normalise my data using fitPLM(), I need to create an > affybatch using ReadAffy(). This time I think justRMA() is not an > option I can use. > > Has anyone been successful at reading in a large group of Plus chips > (say 100) using ReadAffy() and the normalise them using fitPLM() on > a "normal" PC on Windows? What amount of memory is needed for it? > Any examples on how many chips you might have loaded with memory > details would be very appreciated. > > Regards, > > David > > _______________________________________________ > 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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Yeah, but can he do fitPLM()? I was going to suggest that as well, but Henrik's page doesn't mention that as a possibility. Jim Mark Robinson wrote: > David, > > You might have a look at the R package 'aroma.affymetrix' ... you > should be able to read/process/analyze 100s of affy chips. I > regularly process >50 exon arrays, which are far larger than U133s. > I believe you can do it all in less than 500M RAM. > > Full details are at: > http://groups.google.com/group/aroma-affymetrix > > Cheers, > Mark > > > On 05/10/2007, at 9:57 PM, <darteta001 at="" ikasle.ehu.es=""> > <darteta001 at="" ikasle.ehu.es=""> wrote: > >> Dear list, >> >> In order to normalise my data using fitPLM(), I need to create an >> affybatch using ReadAffy(). This time I think justRMA() is not an >> option I can use. >> >> Has anyone been successful at reading in a large group of Plus chips >> (say 100) using ReadAffy() and the normalise them using fitPLM() on >> a "normal" PC on Windows? What amount of memory is needed for it? >> Any examples on how many chips you might have loaded with memory >> details would be very appreciated. >> >> Regards, >> >> David >> >> _______________________________________________ >> 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 > > _______________________________________________ > 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 -- James W. MacDonald, M.S. Biostatistician 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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Sure he can ;) See the RmaPlm class. /Henrik On 10/5/07, James W. MacDonald <jmacdon at="" med.umich.edu=""> wrote: > Yeah, but can he do fitPLM()? I was going to suggest that as well, but > Henrik's page doesn't mention that as a possibility. > > Jim > > > > Mark Robinson wrote: > > David, > > > > You might have a look at the R package 'aroma.affymetrix' ... you > > should be able to read/process/analyze 100s of affy chips. I > > regularly process >50 exon arrays, which are far larger than U133s. > > I believe you can do it all in less than 500M RAM. > > > > Full details are at: > > http://groups.google.com/group/aroma-affymetrix > > > > Cheers, > > Mark > > > > > > On 05/10/2007, at 9:57 PM, <darteta001 at="" ikasle.ehu.es=""> > > <darteta001 at="" ikasle.ehu.es=""> wrote: > > > >> Dear list, > >> > >> In order to normalise my data using fitPLM(), I need to create an > >> affybatch using ReadAffy(). This time I think justRMA() is not an > >> option I can use. > >> > >> Has anyone been successful at reading in a large group of Plus chips > >> (say 100) using ReadAffy() and the normalise them using fitPLM() on > >> a "normal" PC on Windows? What amount of memory is needed for it? > >> Any examples on how many chips you might have loaded with memory > >> details would be very appreciated. > >> > >> Regards, > >> > >> David > >> > >> _______________________________________________ > >> 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 > > > > _______________________________________________ > > 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 > > -- > James W. MacDonald, M.S. > Biostatistician > Affymetrix and cDNA Microarray Core > University of Michigan Cancer Center > 1500 E. Medical Center Drive > 7410 CCGC > Ann Arbor MI 48109 > 734-647-5623 > > _______________________________________________ > 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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