Hi Gordon, Thanks very much for clarifying this. Cheers, Dick ********************************************************************** ********* Richard P. Beyer, Ph.D. University of Washington Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695 Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100 Seattle, WA 98105-6099 http://depts.washington.edu/ceeh/ServiceCores/FC5/FC5.html ********************************************************************** ********* On Tue, 2 Nov 2004, Gordon K Smyth wrote: >> Date: Mon, 1 Nov 2004 13:55:12 -0800 (PST) >> From: Dick Beyer <dbeyer@u.washington.edu> >> Subject: [BioC] confusion using model.matrix with two-color arrays in >> limma >> To: Bioconductor <bioconductor@stat.math.ethz.ch> >> Message-ID: >> <pine.lnx.4.43.0411011355120.12459@hymn02.u.washington.edu> >> Content-Type: TEXT/PLAIN; charset=ISO-8859-1; format=flowed >> >> I am struggling with trying to use model.matrix, which is straight- forward for >> single color arrays, on two-color dye-swap data. For two-color data, using modelMatrix is simple >> and straight-forward. >> >> I would like to know how to sort of combine these two calls, model.matrix and modelMatrix, so I >> can easily specify my design matrix (with modelMatrix), and easily specify complex contrasts (as >> with model.matrix). >> >> My particular experiment is a 2x2x2 design with factors, T,R,C each having levels 0/1. I would >> like to specify the interaction T*R*C, without having to explicitly type it out using >> makeContrasts(). >> >> How can I use model.matrix() so the dye-swaps are combined correctly (as is done easily with >> modelMatrix), or how can I specify makeContrasts() so I don't have to type out the T*R*C >> interaction? >> >> My targets are (where H signifies the case for parameter T=0, T signifies parameter T=1, etc): >> Cy3 Cy5 >> 1 REF HC >> 2 HC REF >> 3 REF HRC >> 4 HRC REF >> 5 REF TC >> 6 TC REF >> 7 REF TRC >> 8 TRC REF >> 9 REF T >> 10 T REF >> 11 REF TR >> 12 TR REF >> 13 REF H >> 14 H REF >> 15 REF HR >> 16 HR REF >> My parameters are: >> T R C >> 1 0 0 1 >> 2 0 0 1 >> 3 0 1 1 >> 4 0 1 1 >> 5 1 0 1 >> 6 1 0 1 >> 7 1 1 1 >> 8 1 1 1 >> 9 1 0 0 >> 10 1 0 0 >> 11 1 1 0 >> 12 1 1 0 >> 13 0 0 0 >> 14 0 0 0 >> 15 0 1 0 >> 16 0 1 0 >> >> If I use the modelMatrix and makeContrast approach, I would use something like the following to >> get the T*R*C interaction: >> design <- modelMatrix(targets,ref="REF") >> fit <- lmFit(RG, design) >> contrast.matrix <- makeContrasts("H-HC-HR+HRC-T+TC+TR- TRC",levels=design) >> fit2 <- contrasts.fit(fit, contrasts.matrix) >> >> If I use the model.matrix approach (ignoring the necessary dye- swapping): >> dd <- data.frame( >> T=factor(c(0,0,0,0,1,1,1,1,1,1,1,1,0,0,0,0)), >> R=factor(c(0,0,1,1,0,0,1,1,0,0,1,1,0,0,1,1)), >> C=factor(c(1,1,1,1,1,1,1,1,0,0,0,0,0,0,0,0))) >> design.lm <- model.matrix(~ T*R*C, data=dd, contrasts = list(T="contr.sum", R="contr.sum", >> C="contr.sum")) >> fit <- lmFit(RG, design.lm) >> and have no need for the contrasts.fit() call. >> >> If I knew how to correctly specify a dye-flip parameter in the "dd" data.frame, I think I would be >> where I'd like to be. > > Just multiply each row of design.lm by -1 whenever Cy5 is REF: > > dyeswap <- 1-2*(targets$Cy5=="REF") > design.lm <- dyeswap * design.lm > fit <- lmFit(RG, design.lm) > > BTW there's no compelling reason to incorporate dye-swaps into your experiment if you're using a > common reference. And if REF was always Cy3, then you could use model.matrix() to create the > design matrix. > > Gordon > >> Any help or suggestions to try from anyone would be greatly appreciated. >> Thanks very much, >> Dick >> ******************************************************************* ************ >> Richard P. Beyer, Ph.D. University of Washington >> Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695 >> Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100 >> Seattle, WA 98105-6099 >> http://depts.washington.edu/ceeh/ServiceCores/FC5/FC5.html > > >