[BioC] Regarding quantile normalization.

[Veerendra GP]

hi sean,

I am sorry the code i mentioned in the last mail was not a running code.
here i have displayed the complete running code.

library (limma);
targets <- readTargets("target.txt")
#print (targets);
RG<-read.maimages(targets$FileName,source="agilent",columns=list(R="rProcessedSignal",G="gProcessedSignal"),path="data_files");
#to remove control spots
status <- rep("gene", nrow(RG$genes));
status[grep("UHNcntrl*", RG$genes[,"ProbeName"])] <- "cntrl";
status[grep("UHNblank*", RG$genes[,"ProbeName"])] <- "cntrl";
RGnc <- RG[status!="cntrl",];
# to visualize MA plots before normalization.
d <- dim(targets)
for(i in 1: d[1])
{
       pdf(paste("RG",i,".pdf",sep="_"))
       plotMA(RGnc, array = i)
        dev.off()
}
# to visualize density plot before normalization.
pdf("RG_Density_Plot_before_quantile.pdf")
plotDensities(RGnc)
dev.off()

MA.q <- normalizeBetweenArrays(RGnc, method="quantile")
#To visualize data after quantile noralization
pdf("MA.q_Density_Plot_after_quantile.pdf");
plotDensities(MA.q);
dev.off();

pdf("Box_Plot_after_quantile.pdf");
boxplot(MA.q$M~col(MA.q$M));
dev.off();

designM <- c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1)
# To fit linear regression model
fit <- lmFit(MA.q, design=designM)
#To fit baysiean model, whihc basically calulates p value, odds ratio etc.
eb <- eBayes(fit)
#To fit FDR and get all the result
tb<-topTable(eb, adjust.method="fdr",genelist = RGnc$genes, n=13056)
#To write to file
write.table(tb,file='normalResults_with_quantile.txt',sep="\t")

filter <- tb[(tb$logFC)>= 1 & (tb$P.Val)< 0.05 | (tb$logFC) <= -1 &
(tb$P.Val)< 0.05,]
filter2 <- tb[(tb$logFC)>= 1 | (tb$logFC) <= -1,]
write.table(filter2, file="geneList_with_no_Pval.txt",row.names=
FALSE,sep="\t")
write.table(filter, file="geneList_with_quantile.txt",row.names=
FALSE,sep="\t")

I agree that the boxplots should be identical after the quantile
normalization but the boxplot which I have sent you in the last mail is
obtained after the quantile normalization as per the above mentioned code.
here I am also attaching the density plots for the same set of data.

On Sat, Jan 1, 2011 at 9:36 PM, Sean Davis <sdavis2 at mail.nih.gov> wrote:

> On Sat, Jan 1, 2011 at 9:23 AM, Veerendra GP <gpveerendra09 at gmail.com>
> wrote:
> > hellow everyone!
> >
> > I will be delighted if any one can help me with this! I am working on a
> > microarray dual channel data, and i need to do a inter array
> normalization.
> > I have tried with quantile normalization (available in limma package)
> method
> > but the result i am getting is not satisfactory as the boxes are not
> > appearing to be of the same sizes which according to the fact should be
> the
> > same. can you please give me a reason for this. and is there any better
> > normalization method available for dual channel experiment.
>
> Hi, Veerendra.
>
> If you are doing "separate channel normalization" using quantile
> normalization of red and green channels between arrays, then I do not
> think there is any requirement that the boxplots of ratios be
> identical.  Is that what you mean by "boxes"?  Perhaps I am
> misunderstanding what you have done, though.  If you want comments on
> the code, then feel free to post as close to a reproducible example as
> you can and the output of sessionInfo().
>
> Sean
>



-- 
Veerendra.
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