Hello This is a long letter about my efforts of analysis of data in Limma. I have a question about the proper design of my experiment I have 3 groups to compare: A, O, and Y. With 5 A, 8 O, and 7 Y biological samples. I've performed in total 28 two-color microarrays (Agilent 44K) with a mixed number of dye-swaps. My targets file is: HybID fileName sampleID Cy3 Cy5 1 124879.txt YvsO Y1 O1 2 124880.txt OvsY O1 Y1 3 124919.txt YvsO Y2 O2 4 124972.txt OvsY O2 Y2 5 124984.txt YvsO Y3 O3 6 124957.txt OvsY O3 Y3 7 130365.txt YvsO Y4 O4 8 130366.txt OvsY O4 Y4 9 130372.txt YvsO Y5 O5 10 130374.txt OvsY O5 Y5 11 124881.txt AvsO A1 O1 12 124882.txt OvsA O1 A1 13 124982.txt AvsO A2 O2 14 124983.txt OvsA O2 A2 15 130351.txt AvsO A2 O2 16 124985.txt AvsO A3 O3 17 124958.txt OvsA O3 A3 18 130352.txt AvsO A3 O3 21 130355.txt AvsO A4 O4 22 130361.txt OvsA O4 A4 23 130362.txt AvsO A5 O5 24 130363.txt OvsA O5 A5 19 130353.txt AvsO A6 O6 20 130354.txt OvsA O6 A6 25 130375.txt AvsO A7 O7 26 130376.txt OvsA O8 A7 27 130377.txt AvsO A7 O6 28 130396.txt OvsA O6 A7 After import of the data, normalization within and between arrays and evaluation of diagnostic plots, the question about fitting linear model arised. I didn't succeed to create proper direct design for all 3 groups. However for separate Y vs O, and A vs O it works Ok with the design of type: design <- cbind(Y1vsO1 = c(-1,1,0,0,0,0,0,0,0,0), Y2vsO2 = c(0,0,-1,1,0,0,0,0,0,0), Y3vsO3 = c(0,0,0,0,-1,1,0,0,0,0), Y4vsO4 = c(0,0,0,0,0,0,-1,1,0,0), Y5vsO5 = c(0,0,0,0,0,0,0,0,-1,1)) But here I think I loose info about O6, O7 and O8 which are extra biological replicates. The same is valid for A vs O and I had to exclude last three hybs. What is your advise in that case? I have also tried to split all data into separate channels, so producing 56 single-channel data sets. (The reason for that was that I have even and odd number of replicates for my groups mixed in hybridizations) >targets2 <- targetsA2C(targets) >u <- unique(targets2$Target) >f <- factor(targets2$Target, levels=u) >design <- model.matrix(~0+f) >colnames(design) <- u It works not bad until >corfit <- intraspotCorrelation(MA, design) It took a lot of time and generated 43 warnings: "exceed amount of iterations ...." fit <- lmscFit(MA, design, correlation=corfit$consensus) Than a BIG question appeared: "What is the contrasts matrix is in my case?" cont.matrix <- makeContrasts("(A1+A2+A3+...)/7-(O1+O2+O3+...)/8",levels=design) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) topTable(fit2, adjust="BH", number=30, resort.by"M") Is it correct for A vs O comparison? I've got the table finally... And needles to say top 10 is different from my direct design A vs O (see above) Regards Nataliya -- Dr. Nataliya Yeremenko Universiteit van Amsterdam Faculty of Science IBED/AMB (Aquatische Microbiologie) Nieuwe Achtergracht 127 NL-1018WS Amsterdam the Netherlands tel. + 31 20 5257089 fax + 31 20 5257064