Hi all,

I have a few questions. I am analyzing  20 experiments  performed  in
Agilent 4x44K. Tumor and adjacent normal were labeled with Cy5 and Cy3
respectively. The experiments include two dye swaps also. The feature
extraction were performed using Agilent feature extraction software version
8.5. The txt files obtained after feature extraction, subjected to further
analysis by limma. The script has given below
txt_files <- dir(pattern=".txt")
RG<-read.maimages(txt_files, source="agilent")
design<-c(1,1,1,1,1,1,1,1,1,1,1,-1,1,1,1,1,1,1,1,-1) # this include two dye
swap also
Rgene<-backgroundCorrect(RG,method="normexp")
fit<-lmFit(Rgene,design)
fit<-eBayes(fit)
topTable(fit,adjust="fdr", number=100)

As a result I get the format in which 'B' indicates what parameter? And is
this analysis method is enough to get a good data?
In Agilent arrays, I think generally it is normalized, in that case how the
data will be affected by normalizing it again using above method?
How can we decide which normalization methods should be used? Do you think
its always better to decide by plotting different graphs?

Comments and answers will be appreciated.

Regards,
Abhilash


-- 

Regards,
Abhilash

	[[alternative HTML version deleted]]

