Hi all, since I received a few emails in my mailbox by people interested in a solution for this design (or a design similar to this one), but there is apparently no (easy) solution in limma, I was wondering if anyone could suggest a package for differential expression analysis that allows dealing with: nested designs, random effects, multiple factorial designs with more than 2 levels. I identified siggenes, maanova, factDesign that could fit my needs, but I would like to have a comment by someone with more experience before diving into a new package. Best, paolo Paolo Innocenti wrote: > 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