Limma design question (model interactions)
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@wolfgang-raffelsberger-1805
Last seen 10.3 years ago
Dear list, here a question about the appropriate design of a specific test layout for use with limma. The problem seems almost trivial, but among the numerous postings I haven?t found something resolving my problem : I have 2 samples (A and B) that were hybridized against a (common) reference (Ref). Now I would like to find those genes that differ from A to Ref but NOT in B to Ref (i.e. A and B would differ, but without those AvR~0 ). In my mind, differentially regulated genes in both A and B could be described as an interaction, but the code shown below for integrating an interaction component won?t work to give the answer : The experiment layout : FileName Cy3 Cy5 array1 A Ref array2 A Ref array3 B Ref array4 B Ref > library(limma) > dat1 <- matrix(runif(200,-3,3),nc=4,dimnames=list(as.character(1:50),c("AvR"," AvR","BvR","BvR"))) # just an example .. > f.A <- c(1,1,0,0) > f.B <- c(0,0,1,1) > design2 <- model.matrix(~f.A + f.A:f.B ) > fit2 <- lmFit(dat1, design2) Coefficients not estimable: f.A:f.B > fit2 <- eBayes(fit2) Warning message: In ebayes(fit = fit, proportion = proportion, stdev.coef.lim = stdev.coef.lim) : Estimation of var.prior failed - set to default value > topTable(fit2, coef=1) # any A or B different to Ref, but contains also those with AvR ~ BvR or AvR ~ 0 > topTable(fit2, coef=2) # just for DE genes as A vs B, but contains also those with AvR ~0 > topTable(fit2, coef=3) # just returns just NAs I suppose a part of the problem is, that the last column of design2 holds just 0s : > design2[,3] # column for interactions contains only 0s 1 2 3 4 0 0 0 0 I also tried (following a posting from Gordon Smyth, 2006-06-01) : > design3 <- model.matrix(~ f.A * f.B ) > design3 # the matrix has one more column, again, the column for interactions contains only 0s And finally with lmFit() & eBayes() I got the same results as from lmFit(dat1, design2). Of course there is the less perfect solution of doing 2 comparisons (1: A v Ref, 2: A vs B; as described in Limma use guide chapter 8.4) and then seeking only genes at the intersection. But I'm surprised I can't get this as a single model working ! Do you have any suggestions how the design / model-matrix should be set to test (from an integrated model) for differences in A to Ref but NOT in B to Ref ? Thank?s very much, Wolfgang Raffelsberger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wolfgang Raffelsberger, PhD Laboratoire de BioInformatique et G?nomique Int?gratives CNRS UMR7104, IGBMC 1 rue Laurent Fries, 67404 Illkirch Strasbourg, France Tel (+33) 388 65 3300 Fax (+33) 388 65 3276 wolfgang.raffelsberger at igbmc.fr
limma limma • 1.3k views
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Naomi Altman ★ 6.0k
@naomi-altman-380
Last seen 3.6 years ago
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You have a compound hypothesis, so you will need to do 2 contrasts: i.e. you want B=Ref intersect A != Ref Interaction means that the effect of B depends on the level of A observed. Since you do not observe A and B together in the same samples, you cannot test interaction. --Naomi At 03:10 PM 6/4/2008, Wolfgang Raffelsberger wrote: >Dear list, >here a question about the appropriate design of >a specific test layout for use with limma. The >problem seems almost trivial, but among the >numerous postings I haven?t found something resolving my problem : > >I have 2 samples (A and B) that were hybridized >against a (common) reference (Ref). Now I would >like to find those genes that differ from A to >Ref but NOT in B to Ref (i.e. A and B would differ, but without those AvR~0 ). >In my mind, differentially regulated genes in >both A and B could be described as an >interaction, but the code shown below for >integrating an interaction component won?t work to give the answer : > >The experiment layout : >FileName Cy3 Cy5 >array1 A Ref >array2 A Ref >array3 B Ref >array4 B Ref > > > library(limma) > > dat1 <- > matrix(runif(200,-3,3),nc=4,dimnames=list(as.character(1:50),c("AvR" ,"AvR","BvR","BvR"))) > # just an example .. > > f.A <- c(1,1,0,0) > > f.B <- c(0,0,1,1) > > design2 <- model.matrix(~f.A + f.A:f.B ) > > fit2 <- lmFit(dat1, design2) >Coefficients not estimable: f.A:f.B > > fit2 <- eBayes(fit2) >Warning message: >In ebayes(fit = fit, proportion = proportion, >stdev.coef.lim = stdev.coef.lim) : >Estimation of var.prior failed - set to default value > > > topTable(fit2, coef=1) # any A or B different > to Ref, but contains also those with AvR ~ BvR or AvR ~ 0 > > topTable(fit2, coef=2) # just for DE genes as > A vs B, but contains also those with AvR ~0 > > topTable(fit2, coef=3) # just returns just NAs > >I suppose a part of the problem is, that the >last column of design2 holds just 0s : > > design2[,3] # column for interactions contains only 0s >1 2 3 4 >0 0 0 0 > >I also tried (following a posting from Gordon Smyth, 2006-06-01) : > > design3 <- model.matrix(~ f.A * f.B ) > > design3 # the matrix has one more column, > again, the column for interactions contains only 0s >And finally with lmFit() & eBayes() I got the >same results as from lmFit(dat1, design2). > >Of course there is the less perfect solution of >doing 2 comparisons (1: A v Ref, 2: A vs B; as >described in Limma use guide chapter 8.4) and >then seeking only genes at the intersection. But >I'm surprised I can't get this as a single model working ! >Do you have any suggestions how the design / >model-matrix should be set to test (from an >integrated model) for differences in A to Ref but NOT in B to Ref ? > >Thank?s very much, >Wolfgang Raffelsberger > >. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . >Wolfgang Raffelsberger, PhD >Laboratoire de BioInformatique et G?nomique Int?gratives >CNRS UMR7104, IGBMC 1 rue Laurent Fries, 67404 Illkirch Strasbourg, France >Tel (+33) 388 65 3300 Fax (+33) 388 65 3276 >wolfgang.raffelsberger at igbmc.fr > >_______________________________________________ >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 Naomi S. Altman 814-865-3791 (voice) Associate Professor Dept. of Statistics 814-863-7114 (fax) Penn State University 814-865-1348 (Statistics) University Park, PA 16802-2111
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