My apologizes, but I am not sure if that I am doing is correct in limma package and nor if it answers my questions! I need a help. I run an analysis whose script finding below, at the end of this message. I make it after reading the user's manual and the help in html. If it is possible, I would like to known this script is correct to answer the following questions: - Which genes are up-regulated in the three times? - Which genes are down-regulated in the three times? This first 2 questions I have known how for to do. But, if you have a suggestions, send it me, please. - Which are up-regulateds in the time 1 and later they do decrease in the times 2 and 3? - Which are up-regulateds in the times 1 and 2 and later it does decrease in the time 3? - Which are down-regulateds in the time 1 and up-regulated in the times 2 and 3? - Which are down-regulateds in the times 1 and 2 and up-regulated in the time 3? I believe that these are the main questions. Would you have suggestions? The experiment design is: Time 1day 2day 3day Rep1 Rep1 Rep1 Un Treated Rep2 Rep2 Rep2 Rep3 Rep3 Rep3 Rep1 Rep1 Rep1 Treated Rep2 Rep2 Rep2 Rep3 Rep3 Rep3 2 treatment (treated and untreated); 3 repetitions, and 3 times. My script (step-by-step) > library(limma) > RG <- read.maimages(files, columns=list(Rf="DataVol",Gf="CtrlVol",Rb="DataBkgd",Gb="CtrlBkgd")) > show(RG) > summary(RG$R) > genes.names[1:10,] > printer <- list(ngrid.r=4, ngrid.c=5, nspot.r=16, nspot.c=24, ndups=2, spacing=1, npins=20, start="topleft") > printer > MA <- normalizeWithinArrays(RG, method="none", printer) > boxplot(MA$M~col(MA$M)) > MA <- normalizeWithinArrays(RG, printer) > boxplot(MA$M~col(MA$M)) > MA.fa <- normalizeBetweenArrays(MA,method="scale") > boxplot(MA.fa$M~col(MA.fa$M)) > design <- model.matrix(~ -1+factor(c(1,1,1,2,2,2,3,3,3))) > colnames(design) <- c("time1","time2","time3") > fit <- lmFit(MA.fa,design) > contrast.matrix <- makeContrasts(time2-time1, time3-time2,time3-time1,levels=design) > fit2 <- contrasts.fit(fit,contrast.matrix) > fit3 <- eBayes(fit2) > time2.time1 <- topTable(fit3, coef=1, adjust="fdr") > time3.time2 <- topTable(fit3, coef=2, adjust="fdr") > time3.time1 <- topTable(fit3, coef=3, adjust="fdr") > clas <- classifyTests(fit3) > vennDiagram(clas) If someone already accomplished an analysis as this, could send me some suggestions! You would could suggest another way to analyze it, for example. Thaks very much! I am sorry, but the English is not my native language. -- Marcelo Luiz de Laia, M.Sc. Dep. de Tecnologia, Lab. Bioqu?mica e de Biologia Molecular Universidade Estadual Paulista - UNESP Via de Acesso Prof. Paulo Donato Castelane, Km 05 14.884-900 - Jaboticabal, SP, Brazil PhoneFax: 16 3209-2675/2676/2677 R. 202/208/203 (trab.) HomePhone: 16 3203 2328 - www.lbm.fcav.unesp.br - mlaia@yahoo.com