Dear James, I am sorry for the delayed reply, I was excited to do the analyses (a good sign from a science point of view). Thank you very much for your help. Although not independent as you say, I would guess the operation is having a significant effect on gene expression, so I decided to do 2 separate analyses (comparing case vs. control for both before and after the operation). Kind regards, John. On Thu, Oct 13, 2011 at 6:57 PM, James W. MacDonald <jmacdon at="" med.umich.edu=""> wrote: > Hi John, > > On 10/13/2011 1:29 PM, john herbert wrote: >> >> Hello, >> I have a complex design matrix to produce (for me anyway) for single >> colour array data. >> >> I have 16 samples for 8 patients, which are paired (P1 to P4). >> >> Patients 1 and 3 are control patients and patients 2 and 4 are affected >> >> Before and after refer to before and after an operation. >> >> File1 P1 Control before >> File2 P1 Control after >> File3 P2 Affected before >> File4 P2 Affected after >> File5 P3 Control before >> File6 P3 Control after >> File7 P4 Affected before >> File8 P4 Affected after >> >> If to look at the Limma manual, section "8.3 Paired Samples" looks >> almost good for this design. >> >> However, does it need combining with a factorial design (section "8.7 >> Factorial Designs") as there is a before and after op? > > You don't have a factorial design. This is just a simple control vs affected > with pairing. > > If the before and after were different people, this could be analyzed as a > factorial design, but since they are the same person you cannot assume > independence between the before and after samples. > > pairs <- factor(rep(1:4, each = 2)) > type <- factor(rep(1:2, each = 2, times = 2)) > > design <- model.matrix(~type+pairs) > fit <- lmFit(eset, design) > fit2 <- eBayes(fit) > > topTable(fit2, coef = 2) > > Best, > > Jim > > > >> >> How do I combine a design for paired samples with a factorial design? >> >> >> > From the manual, below, the paired and the factorial examples looks >> scary to combine. ? >> >> FileName SibShip Treatment >> File1 1 C >> File2 1 T >> File3 2 C >> File4 2 T >> File5 3 C >> File6 3 T >> >> A moderated paired t-test can be computed by allowing for sib-pair >> e ects in the linear model: >>> >>> SibShip<- factor(targets$SibShip) >>> Treat<- factor(targets$Treatment, levels=c("C","T")) >>> design<- model.matrix(~SibShip+Treat) >>> fit<- lmFit(eset, design) >>> fit<- eBayes(fit) >>> topTable(fit, coef="TreatT") >> >> frame: >> >> FileName Strain Treatment >> File1 WT U >> File2 WT S >> File3 Mu U >> File4 Mu S >> File5 Mu S >> >>> TS<- paste(targets$Strain, targets$Treatment, sep=".") >>> TS >> >> [1] "WT.U" "WT.S" "Mu.U" "Mu.S" "Mu.S" >> >>> TS<- factor(TS, levels=c("WT.U","WT.S","Mu.U","Mu.S")) >>> design<- model.matrix(~0+TS) >>> colnames(design)<- levels(TS) >>> fit<- lmFit(eset, design) >>> cont.matrix<- makeContrasts( >> >> ? WT.SvsU=WT.S-WT.U, >> ? Mu.SvsU=Mu.S-Mu.U, >> ? Diff=(Mu.S-Mu.U)-(WT.S-WT.U), levels=design) >>> >>> fit2<- contrasts.fit(fit, cont.matrix) >>> fit2<- eBayes(fit2) >> >> We can use topTable() to look at lists of di erentially expressed >> genes for each of three >> contrasts, or else >> 48> ?results<- decideTests(fit2) >>> >>> vennDiagram(results) >> >> _______________________________________________ >> Bioconductor mailing list >> Bioconductor at r-project.org >> 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 > Douglas Lab > University of Michigan > Department of Human Genetics > 5912 Buhl > 1241 E. Catherine St. > Ann Arbor MI 48109-5618 > 734-615-7826 > > ********************************************************** > Electronic Mail is not secure, may not be read every day, and should not be > used for urgent or sensitive issues >