Hello, I am analysing microarray data performed on two cell cultures, in which the gene expression were measured before (C) and after treatment (T), so that the targets look like this: Cell-line Treatment Sample Cell 1 C 1 Cell 1 T 1 Cell 2 C 2 Cell 2 T 2 Cell 1 C 3 Cell 1 T 3 Cell 2 C 4 Cell 2 T 4 Cell 1 C 5 Cell 1 T 5 Cell 2 C 6 Cell 2 T 6 All experiments were performed on single-channel Agilent arrays, with 4 samples pr. slide. I am interested in determining the differentially expressed genes between Cell1 before and after treatment, as well as Cell2 before and after treatment. This is the preliminary code: # Load and normalize data RG <- read.maimages(targets$FileName,source="agilent.median",green.onl y=TRUE) # Assume there is a col called FileName in the targets section RG <- backgroundCorrect(RG, method="normexp", offset=16) RGNorm <- normalizeBetweenArrays(RG, method="quantile") RGNorm.ave <- avereps(RGNorm, ID=RGNorm$genes$ProbeName) # Create design Pairing <- paste(rep(c('C1-','C1-','C2-','C2-'),3),c(1,1,2,2,1,1,2,2,1 ,1,2,2),rep(c('C','T'),6),sep='') pair <- factor(Pairing,levels=unique(Pairing)) design <- model.matrix( ~ 0 + pair ) colnames(design) <- c('C1.C','C1.T','C2.C','C2.T') # Fit data fit <- lmFit(RGNorm.ave, design=design) cont.matrix <- makeContrasts(C1 = C1.T-C1.C, C2=C2.T-C2.C, levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) For the experiment, is the design/contrast matrix a proper choice to answer the questions of 'Which genes are differentially expressed in Cell 1' and 'Which genes are differentially expressed in Cell 2'? Further, should I do any technical correction, such as duplicateCorrelation or similar? The reason I am asking is that even at p<=0.01 I am getting a very high number of differentially expressed probes (4500ish for Cell 1, and 7500ish for Cell 2, respectively), and I want to make sure this is biological significance, and not some technical aspect I have missed. Thanks in advance. Best, David > sessionInfo() R version 2.14.1 (2011-12-22) Platform: x86_64-unknown-linux-gnu (64-bit) locale: [1] C attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] statmod_1.4.16 limma_3.10.3 loaded via a namespace (and not attached): [1] tcltk_2.14.1 tools_2.14.1