Hi Naomi and list, some time ago I asked a question on how to model an experiment in limma. I think I need some additional help with it as the experiment grew in complexity. I also added a factor "batch" because the arrays were run in separate batches, and I think would be good to control for it. The dataframe with phenotypic informations ("dummy") looks like this: >> Phen Line Sex Batch BiolRep >> File1 H 1 M 1 1 >> File2 H 1 M 1 2 >> File3 H 1 M 2 3 >> File4 H 1 M 2 4 >> File5 H 1 F 1 1 >> File6 H 1 F 1 2 >> File7 H 1 F 2 3 >> File8 H 1 F 2 4 >> File9 H 2 M 1 1 >> File10 H 2 M 1 2 >> File11 H 2 M 2 3 >> File12 H 2 M 2 4 >> File13 H 2 F 1 1 >> File14 H 2 F 1 2 >> File15 H 2 F 2 3 >> File16 H 2 F 2 4 >> File17 L 3 M 1 1 >> File18 L 3 M 1 2 >> File19 L 3 M 2 3 >> File20 L 3 M 2 4 >> File21 L 3 F 1 1 >> File22 L 3 F 1 2 >> File23 L 3 F 2 3 >> File24 L 3 F 2 4 >> File25 L 4 M 1 1 >> File26 L 4 M 1 2 >> File27 L 4 M 2 3 >> File28 L 4 M 2 4 >> File29 L 4 F 1 1 >> File30 L 4 F 1 2 >> File31 L 4 F 2 3 >> File32 L 4 F 2 4 >> File33 A 5 M 1 1 >> File34 A 5 M 1 2 >> File35 A 5 M 2 3 >> File36 A 5 M 2 4 >> File37 A 5 F 1 1 >> File38 A 5 F 1 2 >> File39 A 5 F 2 3 >> File40 A 5 F 2 4 >> File41 A 6 M 1 1 >> File42 A 6 M 1 2 >> File43 A 6 M 2 3 >> File44 A 6 M 2 4 >> File45 A 6 F 1 1 >> File46 A 6 F 1 2 >> File47 A 6 F 2 3 >> File48 A 6 F 2 4 In total I have Factor "Phen", with 3 levels Factor "Line", nested in Phen, 6 levels Factor "Sex", 2 levels Factor "Batch", 2 levels I am interested in: 1) Effect of sex (M vs F) 2) Interaction between "Sex" and "Line" (or "Sex" and "Phen") Now, I can't really come up with a design matrix (not to mention the contrast matrix). Naomi Altman wrote: > You can design this in limma quite readily. Nesting really just means > that only a subset of the possible contrasts are of interest. Just > create the appropriate contrast matrix and you are all set. I am not really sure with what you mean here. Should I treat all the factors as in a factorial design? I might do something like this: phen <- factor(dummy$Phen) line <- factor(dummy$Line) sex <- factor(dummy$Sex) batch <- factor(dummy$Batch) fact <- factor(paste(sex,phen,line,sep=".")) design <- model.matrix(~ 0 + fact + batch) colnames(design) <- c(levels(fact), "batch2") fit <- lmFit(dummy.eset,design) contrast <- makeContrasts( sex= (F.H.1 + F.H.2 + F.L.3 + F.L.4 + F.A.5 + F.A.6) - (M.H.1 + M.H.2 + M.L.3 + M.L.4 + M.A.5 + M.A.6), levels=design) fit2 <- contrasts.fit(fit,contrast) fit2 <- eBayes(fit2) In this way I can correctly (I presume) obtain the effect of sex, but how can I get the interaction term between sex and line? I presume there is a "easy" way, but I can't see it... Thanks, paolo > > --Naomi > > At 12:08 PM 2/16/2009, Paolo Innocenti wrote: >> Hi all, >> >> I have an experimental design for a Affy experiment that looks like this: >> >> Phen Line Sex Biol.Rep. >> File1 H 1 M 1 >> File2 H 1 M 2 >> File3 H 1 F 1 >> File4 H 1 F 2 >> File5 H 2 M 1 >> File6 H 2 M 2 >> File7 H 2 F 1 >> File8 H 2 F 2 >> File9 L 3 M 1 >> File10 L 3 M 2 >> File11 L 3 F 1 >> File12 L 3 F 2 >> File13 L 4 M 1 >> File14 L 4 M 2 >> File15 L 4 F 1 >> File16 L 4 F 2 >> >> >> This appears to be a slightly more complicated situation than the one >> proposed in the section 8.7 of the limma users guide (p.45) or by >> Jenny on this post: >> >> https://stat.ethz.ch/pipermail/bioconductor/2006-February/011965.html >> >> In particular, I am intersted in >> - Effect of "sex" (M vs F) >> - Interaction between "sex" and "phenotype ("line" nested) >> - Effect of "phenotype" in males >> - Effect of "phenotype" in females >> >> Line should be nested in phenotype, because they are random "strains" >> that happened to end up in phenotype H or L. >> >> Can I design this in limma? Is there a source of information about how >> to handle with this? In particular, can I design a single model matrix >> and then choose the contrasts I am interested in? >> >> Any help is much appreciated, >> paolo >> >> >> -- >> Paolo Innocenti >> Department of Animal Ecology, EBC >> Uppsala University >> Norbyv?gen 18D >> 75236 Uppsala, Sweden >> >> _______________________________________________ >> 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 > > -- Paolo Innocenti Department of Animal Ecology, EBC Uppsala University Norbyv?gen 18D 75236 Uppsala, Sweden