Dear Nataliya, > Date: Fri, 11 Nov 2005 01:09:48 +0100 > From: Nataliya Yeremenko <eremenko at="" science.uva.nl=""> > Subject: [BioC] Design question in LIMMA > To: Bioconductor List <bioconductor at="" stat.math.ethz.ch=""> > > 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 ...." A number of warnings like this are not a problem. BTW, please don't put text in quotes unless it really is an exact quotation. I would never word a warning message in that way. > 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) This seems about the best you can do with this experiment in limma. This experiment has a complex design and you should probably consult a local statistician on it, say Prof van Houwelingen or Dr te Meerman. Best wishes Gordon > 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