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.

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