Dear List I hope somebody may give me an explanation of strange phenomena I need some explanation about my results obtained by LIMMA analysis of my microarray data. I have some experience with limma and some other packages from Bioconductor however I must say that I'm rather biologist than bioinformatician. Briefly description of my experiment: I'm comparing two groups of patients with different forms of one disease (let's say group A and group B) RNA from 18 patients (11 from A and 7 from B) were hybridized to 36 44K Agilent human microarrays All of microarrays were performed against common reference and each one had a dye-swap hybridization. targets: filename sampleID Cy3 Cy5 1 A1.ST.txt A1 Ref A1 2 A1.DS.txt A1 A1 Ref .... 21 A11.ST.txt A11 Ref A11 22 A11.DS.txt A11 A11 Ref 23 B1.ST.txt B1 Ref B1 24 B1.DS.txt B1 B1 Ref .... 35 B7.ST.txt B7 Ref B7 36 B7.DS.txt B7 B7 Ref after importing the data, removing all types of control spots from dataset, performing "loess" within array normalization like and "scale" between normalization: >MA.offset <- normalizeWithinArrays(RG, method="loess", bc.method="normexp", offset = 50) >MA.scale <- normalizeBetweenArrays(MA.offset, method="scale") >dim(MA.scale) [1] 38133 36 Design matrix is rather simple in my case: >design <- modelMatrix(targets, ref="Ref") and to account for the possible dye-effect include: >design <-cbind(Dye=1, design) and then linear model: >fit1 <-lmFit(MA.scale,design) >cont.matrix<-makeContrasts(Dye, + A=(A1+....+A11)/11, + B=(B1+...+B7)/7, + AvsB=((A1+....+A11)/11-(B1+...+B7)/7), + design) >fit2<-contrasts.fit(fit1,cont.matrix) >fit3<-eBayes(fit2) So far so good but then: >d<-decideTests(fit3, adjust.method="fdr", p.value=0.001) >summary(d) Dye A B AvsB -1 3026 14909 14530 8161 0 32497 6720 7881 21544 1 2610 16504 15722 8428 gives me terrible amount of regulations and dye-effects: even with incredibly stricken adjustments and p.value cutoff I'm getting ennormous amount of regulation: >d<-decideTests(fit3, adjust.method="bonferroni", p.value=0.0001) >summary(d) Dye A B AvsB -1 320 12390 11606 2584 0 37496 12633 14242 31896 1 317 13110 12285 3653 Even if to play with cut-off on fold change the amount of the data with fold change above 1.5 is 1000 genes for up-regulation...:( In general it can't be true as far as I understand biological problem and statistical surrounding of the data, It's virtualy not possible that different human patients could produce such a similar expression profile to each other (I mean within one group). Form other hand it is violating major assumption of microarray dif.expression testing that only small proportion of the genes could be differentially expressed. May anybody give me a hint on what I'm doing not correct in data treatment I tried to fit the model slightly different way and got completely ironic results which I can't interpret myself at all, so I'm lost afterwards >biolrep <- c(1,1,2,2,3,3,....,18,18) >corfit<-duplicateCorrelation(MA.scale, ndups=1, block=biolrep) >corfit$consensus [1] -0.9645838 which is not bad as I do understand that there is nice negative correlation between dye-swaps however there is nowhere stated that all this arrays are belonging to two different groups (A and B), (probably there is no matter for this function as it calculates the correlation between each pair only) Thanks a lot for any help and advise. > sessionInfo() R version 2.6.1 (2007-11-26) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] statmod_1.3.1 limma_2.12.0 > Which afterwards gives a really simple design: -- V. Krasikov Swammerdam Institute for Life Sciences Plant Pathology University of Amsterdam Kruislaan 318 1098SM Amsterdam Telephone: +31(0)20 5257839 Telefax: +31(0)20 5257934 E-mail: krasikov at science.uva.nl