Dear Veerendra, Your design matrix looks fine as far as I can see. Between array normalization would make no difference to your results. That is only required if you want to try separate channel analysis. Best wishes Gordon > Date: Thu, 12 Aug 2010 19:10:10 +0530 > From: Veerendra GP <gpveerendra09 at="" gmail.com=""> > To: bioconductor at stat.math.ethz.ch > Cc: prashantha.hebbar at manipal.edu, shama.prasad at manipal.edu > Subject: [BioC] Help to confirm Design matrix and Between array > Normalization. > > 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. ______________________________________________________________________ The information in this email is confidential and intend...{{dropped:4}}