Dear Ninni, If I understand you correctly, you want to conduct an F-test for changes between treatment and control at any time and any dose. You are blessed with having two replicates for every one of the 40 combinations of condition, time and dose. You are using the single-factor group-mean parametrization of the design matrix recommended in Section 9.5.2 of the limma User's Guide. In this approach, the F-test you want to do is on 20 degrees of freedom (4 time points x 5 doses) so, yes, you do need to create 20 contrasts, one for treatment vs control at each time/dose combination. To do this test, you might proceed: tp <- rep(c("tp20","tp45","tp90","tp180"),each=5) do <- rep(c("1mg","2mg","3mg","4mg","5mg"),times=4) ttt <- paste("treatment",do,tp,sep="_") ctl <- paste("control",do,tp,sep="_") all.cont <- paste(ttt,ctl,sep="-") cont.matrix <- makeContrasts(contrasts=all.cont,levels=design) fit2 <- contrasts.fit(fit,cont.matrix) fit2 <- eBayes(fit2) topTable(fit2) Best wishes Gordon > Date: Fri, 22 Nov 2013 07:03:02 -0800 (PST) > From: "Ninni Nahm [guest]" <guest at="" bioconductor.org=""> > To: bioconductor at r-project.org, ninni.nahm at gmail.com > Subject: [BioC] question about design for limma time course, 2 > conditions and drug treatment microarray experiment > > > Dear list, > > I have a conceptual question about creating a design matrix for a more complicated experimental design. > > I have microarray data of > - two different conditions (treatment/control), > - over a series of time points (20, 45, 90, 180 minutes) > - and different dose concentrations of a certain drug (no treatment, 1mg, 2mg, 3mg, 4mg, 5mg). > - and I have 2 replicates per drug, time point, and condition > I think, I know how to do it when I want to consider time only (please correct me when I'm wrong!): > > ## find genes which change over time differently between the treatment and the control. > > cont.dif <- makeContrasts( > Dif1 = (treatment_1mg_tp45-treatment_1mg_tp20)-(control_tp45-control_tp20), > Dif2 = (treatment_1mg_tp90-treatment_1mg_tp45)-(control_tp90-control_tp45), > Dif3 = (treatment_1mg_tp180-treatment_1mg_tp90)-(control_tp180-control_tp90), > levels=design) > > However, I would like to know which genes are changing over time and drug exposure between control and treatment. > Would I have to do the contrasts for every dose? > > Any help is much appreciated! > > Best, > Ninni > > > > -- output of sessionInfo(): > >> sessionInfo() > R version 3.0.2 (2013-09-25) > Platform: x86_64-pc-linux-gnu (64-bit) > > locale: > [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C > [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 > [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 > [7] LC_PAPER=en_US.UTF-8 LC_NAME=C > [9] LC_ADDRESS=C LC_TELEPHONE=C > [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C > > attached base packages: > [1] stats graphics grDevices utils datasets methods base > > other attached packages: > [1] sva_3.8.0 mgcv_1.7-27 nlme_3.1-113 corpcor_1.6.6 limma_3.18.3 > > loaded via a namespace (and not attached): > [1] grid_3.0.2 lattice_0.20-24 Matrix_1.1-0 >> > > > -- > Sent via the guest posting facility at bioconductor.org. ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}