Dear friends, I have DNA differently methylated data of (done with differentially methylated hybridization technique) three cancerous experiments. The experimental design is as follows: Normal vs Tumor: wherein Normal is labeled with cy3 and Tumor is labeled with cy5. Normal vs Pre malignant: wherein Normal is labeled with cy3 and Pre malignant is labeled with cy5. Tumor vs Pre maligmant: wherein Tumor is labeled with cy3 and pre malignent is labeled with cy5. I wanted to fit linear model for this experimental design. I have gone through the Limma documentation and done analysis. But, as design matrix need to be more perfect, I would like to confirm the matrix design with your opinion. I also would like to know fitting between array normalization in this context will be meaningfull or not? So, I request you to help me in this context. Here is my sessional info : library(limma) targets<- readTargets("/home/veerendra/MicroarrayData/target.txt") RG <- read.maimages(targets$SlideNumber, source="agilent", path="/home/veerendra/MicroarrayData/DATA") status <- rep("gene", nrow(RG$genes)) status[grep("NC1_*",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("NC2_*",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("LACC*",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("PC_*",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("DarkCorner",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("HsCGHBrightCorner",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("NegativeControl",RG$genes[,"SystematicName"])] <- "cntrl" status[grep("SM_*",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("DCP*",RG$genes[,"ProbeName"])] <- "cntrl" status[grep("unmapped",RG$genes[,"SystematicName"])] <- "cntrl" RGnc <- RG[status!="cntrl",] MA<-normalizeWithinArrays(RGnc,method="loess",bc.method="normexp") design <- modelMatrix(targets, ref="N") N P T P T [1,] 0 1 [2,] 1 0 [3,] 1 -1 fit <- lmFit(MA, design) contrast.matrix <- cbind("N-T"=c(1,0),"N-P"=c(0,1),"P-T"=c(1,-1)) N-T N-P P-T [1,] 1 0 1 [2,] 0 1 -1 rownames(contrast.matrix) <- colnames(design) fit2 <- contrasts.fit(fit, contrast.matrix) fit2 <- eBayes(fit2) tb<-topTable(fit2, adjust="BH", n=200000) write.table(tb,file="testtb.txt", sep="\t") Thanking you in anticipation. Regards, Veerendra. [[alternative HTML version deleted]]