Dear list, I am writing to ask if anyone can help me to define a design matrix for our experiment. I am working with MoGene ST1.0 chips.Samples are from WT or HIV- transgenic mouse bone marrow-derived macrophages that we grew in dishes and then either exposed to TB or not for 24 hours. Each pair is from the same mouse (i.e. 5202/5203, 5208/5209,5204/5205, 5206/5207, 5210/5211). Totally 10 chips with experimental design as following: Chip Pair TB HIV 5202 1 NoTB NoHIV 5203 1 TB NoHIV 5204 2 NoTB HIV 5205 2 TB HIV 5206 3 NoTB HIV 5207 3 TB HIV 5208 4 NoTB NoHIV 5209 4 TB NoHIV 5210 5 NoTB HIV 5211 5 TB HIV We want to know the main effect of TB/NoTBtreatment(independent of the HIV status) and HIV/NoHIV status(independent of the TB/NoTB treatment). I am trying to use Limma to do this. But I am not sure if it is appropriate to treat each pair of correlated arrays as a block or not.I want to use the R script as following to define a design matrix and fit a linear model: --------------- TS<-paste(targets$TB,targets$HIV,sep=".") TS<-factor(TS,levels=c("NoTB.NoHIV","TB.NoHIV","NoTB.HIV","TB.HIV")) design<-model.matrix(~0+TS) block<-targets$Pair dupCor<-duplicateCorrelation(intensity,design,block=block) dupCor$consensus.correlation fit<-lmFit(intensity,design,block=block,correlation=dupCor$consensus) --------------- If so, my design matrix will simply be: NoTB.NoHIV TB.NoHIV NoTB.HIV TB.HIV 1 1 0 0 0 2 0 1 0 0 3 0 0 1 0 4 0 0 0 1 5 0 0 1 0 6 0 0 0 1 7 0 1 0 0 8 0 1 0 0 9 0 0 1 0 10 0 0 0 1 Is it correct? Any suggestions from you are appreciated. julin