Hello, After looking at the users guide, I have begun to analyse our data. I found problems for to do a proper design matrix, with my biologist background. We are trying to analyse a 4x2x3 factorial design. We have 3 biological replicates. I made a search in the list archives and found a lot off messages about design in limma, but I steel with doubts about it. I would like to understand the design matrix. For example, I have this targets in a pData (made by hand) FileName Variedade Treatment Time 1 File01 Var1 Con T1 2 File02 Var1 Tra T1 3 File03 Var1 Con T1 4 File04 Var1 Tra T1 5 File05 Var1 Con T1 6 File06 Var1 Tra T1 7 File07 Var1 Con T2 8 File08 Var1 Tra T2 9 File09 Var1 Con T2 10 File10 Var1 Tra T2 11 File11 Var1 Con T2 12 File12 Var1 Tra T2 13 File13 Var1 Con T3 14 File14 Var1 Tra T3 15 File15 Var1 Con T3 16 File16 Var1 Tra T3 17 File17 Var1 Con T3 18 File18 Var1 Tra T3 (...) 72 File 72 Var4 Tra T3 Then, I made this design matrix, by hand: design <- model.matrix(~-1+factor(c(1,2,1,2,1,2,3,4,3,4,3,4,5,6,5,6,5,6, 7,8,7,8,7,8,9,10,9,10,9,10,11,12,11,12,11,12, 13,14,13,14,13,14,15,16,15,16,15,16, 17,18,17,18,17,18,19,20,19,20,19,20, 21,22,21,22,21,22,23,24,23,24,23,24))) colnames(design) <- c("Ctrl_v1a1","Data_v1a1","Ctrl_v1a2","Data_v1a2", "Ctrl_v1a3","Data_v1a3", "Ctrl_v2a1","Data_v2a1","Ctrl_v2a2","Data_v2a2", "Ctrl_v2a3","Data_v2a3", "Ctrl_v3a1","Data_v3a1","Ctrl_v3a2","Data_v3a2", "Ctrl_v3a3","Data_v3a3", "Ctrl_v4a1","Data_v4a1","Ctrl_v4a2","Data_v4a2", "Ctrl_v4a3","Data_v4a3") I would like to know what is in the topTable function coef=1? With this answer I will be able to understand what are in the others coef. At this step am I impossible to get differently expressed genes for Var 1 on time 1? Tra is treated and Con is a control (not treated). After this step, I made a contrast matrix in this way: contrast.matrix <- makeContrasts("Data_v1a1-Ctrl_v1a1","Data_v1a2-Ctrl_v1a2", "Data_v1a3-Ctrl_v1a3", "Data_v2a1-Ctrl_v2a1","Data_v2a2-Ctrl_v2a2", "Data_v2a3-Ctrl_v2a3", "Data_v3a1-Ctrl_v3a1","Data_v3a2-Ctrl_v3a2", "Data_v3a3-Ctrl_v3a3", "Data_v4a1-Ctrl_v4a1","Data_v4a2-Ctrl_v4a2", "Data_v4a3-Ctrl_v4a3", levels=design) In this step I have the differentially expressed genes for Var 1 on time 1 in the coef=1 in topTable function? With this design we are able to get the differentially expressed genes in time 3 vs time 2? And for treated vs control? I try to do another design based on targets: design<-model.matrix(~Variedade*Treatment*Time, data=pData(targets)) In this case, I have a intercept and more 23 columns. Is this case, what comparison are in the topTable functions coef=1? And in coef=2? And in coef=23? Need I to do a contrast matrix, too? In this case I not need to makecontrasts for get the expressed genes for Var 4 vs Var 1 in all times, i.e., considering the treated and control included? I guess if I solve these doubts I will made my design matrix correct. So, I decide to ask for some advice from Bioconductor?s list. I am interested in several aspects (contrasts), like differences between Variedade types with treatment, and differences between Variedade types without treatment (time excluded or included). Which contrasts can answer these questions? Thanks in advance for your assistance. -- Marcelo Luiz de Laia Ph.D Candidate S?o Paulo State University (http://www.unesp.br/eng/) School of Agricultural and Veterinary Sciences Department of Technology Via de Acesso Prof.Paulo Donato Castellane s/n 14884-900 Jaboticabal - SP - Brazil Phone: +55-016-3209-2675 Cell: +55-016-97098526