> Date: Tue, 12 Oct 2004 12:49:38 -0400 > From: karen <kschlauc@vt.edu> > Subject: [BioC] (no subject) > To: bioconductor@stat.math.ethz.ch > Message-ID: <41E2F73D@zathras> > Content-Type: text/plain; charset="ISO-8859-1" > > Hi Folks, > > Thanks, Fangxin, for your replies. > > I still have a few questions on my linear model for the following experiment. > If someone could help, I'd be grateful. > > This is an an Affymetrix time series experiment, 7 time points, > 2 genotypes, 2 replicates of each (28 arrays). > > Of interest are genes that are differentially expressed > between genotypes across all but the first time state. > > The design I set up with 14 treatments and 6 contrasts: > > genotype1_time1, genotype1_time2.... > genotype2_time1, genotype2_time2.... > > genotype1_time2 - genotype2_time2 > genotype1_time3 - gentotype2_time3 ... > > The code: > > treatment.vector<-c(rep(1,2),rep(2,2),rep(3,2), rep(4,2), rep(5,2), rep(6,2), > rep(7,2), > > rep(8,2),rep(9,2),rep(10,2),rep(11,2),rep(12,2),rep(13,2),rep(14,2)) > treatments<-factor(treatment.vector,labels=exp.labels) > design<-model.matrix(~-1+treatments) > fit <- lmFit(Mrma, design) > contrast.matrix<-makeContrasts(KO.15min-WT.15min, > KO.30min-WT.30min, > KO.90min-WT.90min, > KO.3hr-WT.3hr, > KO.6hr-WT.6hr, > KO.24hr- WT.24hr,levels=design) > > fit2 <- contrasts.fit(fit, contrast.matrix) > fit3 <- eBayes(fit2) > clas <- classifyTestsF(fit3,fstat.only=FALSE) > FStats<-FStat(fit3) > > The Questions: > 1) Is this an acceptable model to use? You seem to be fitting a separate coefficient for each factor combination, and then extracting contrasts of interest between the coefficients. This is a basic approach which is generally applicable. So, yes. > 2) How would I report the model via an equation? > Even using contrasts, (2 main effects) will the model be written as Y=xij + e You're just fitting a linear regression model, and then testing hypotheses about the coefficients, so it could in principle be written as a regression equation. But do you really need to? > 3) Should significant FStats be significant in 1 or more contrasts, but not > necessarily in all six? Here, as elsewhere in statistics, there is no apriori rule about what must be significant. Any combination is presumably possible. Gordon > Thank you for any help, > Karen