Thank you, Gordon, for your replies. Regarding question 2), below, I would like to learn how to write the model in its correct equation form. I was thinking about Yij=u+Ti+Gj+TGij+error (T=Time, G=Genotype) Regarding 3) below, in the FStat help, it states that FStat tests for all contrasts = 0. Thus, I thought that a significant FStat would represent a test in clas in which all contrasts were non-zero. classifyTestsF classifies genes which have two or more significant contrasts, correct? i'm sorry, then i don't understand the equivalence classifyTestsF(FStat.only=T)==FStat thank you .... and sorry for more questions. > > 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 > > >