Hello everyone, and thank you for taking the time to listen to my questions. Its really great to know that there are people who are willing to share their experience, or at least helpfully point me in the right direction! I have a simple experimental design, in which marine and freshwater fish were raised at two different temperatures. I would like to be able to set up a model that is going to give me population, treatment, and population x treatment interaction terms for each gene. Furthermore, if possible, I would like to include tank effect and sex as a variable in the model. Any suggestions? I see that lots of people generally do 2x2 factorial designs in which they only wish to contrast treatment within a line, and interaction is never looked for. The code I am currently using is as follows, but it obviously only gives me part of the story. Guidance would be greatly appreciated! normalize<-normalizeBetweenArrays(E, method="quantile") dat1<-normalize[normalize$genes$ControlType==0,] Eavg<-avereps(dat1, ID=dat1$genes$ProbeName) TS <- paste(targets$Population, targets$Temperature, sep=".") TS <- factor(TS, levels=c("marine.7","fw.7","marine.23","fw.23")) design <- model.matrix(~0+TS) colnames(design) <- levels(TS) fit <- lmFit(Eavg, design) cont.matrix <- makeContrasts(marine.7vs23=marine.23-marine.7, fw.7vs23=fw.23-fw.7) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) results <- decideTests(fit2, method="global") Thanks! Matthew

My apologies if this email was already sent out. It bounced back to me, so I thought I would send it again. Hello everyone, and thank you for taking the time to listen to my questions. Its really great to know that there are people who are willing to share their experience, or at least helpfully point me in the right direction! I have a simple experimental design, in which marine and freshwater fish were raised at two different temperatures. I would like to be able to set up a model that is going to give me population, treatment, and population x treatment interaction terms for each gene. Furthermore, if possible, I would like to include tank effect and sex as a variable in the model. Any suggestions? I see that lots of people generally do 2x2 factorial designs in which they only wish to contrast treatment within a line, and interaction is never looked for. The code I am currently using is as follows, but it obviously only gives me part of the story. Guidance would be greatly appreciated! normalize<-normalizeBetweenArrays(E, method="quantile") dat1<-normalize[normalize$genes$ControlType==0,] Eavg<-avereps(dat1, ID=dat1$genes$ProbeName) TS <- paste(targets$Population, targets$Temperature, sep=".") TS <- factor(TS, levels=c("marine.7","fw.7","marine.23","fw.23")) design <- model.matrix(~0+TS) colnames(design) <- levels(TS) fit <- lmFit(Eavg, design) cont.matrix <- makeContrasts(marine.7vs23=marine.23-marine.7, fw.7vs23=fw.23-fw.7) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) results <- decideTests(fit2, method="global") Thanks! Matthew