Hi Gordon,

I am a experimentalist, but not with great statistical knowledge and R
experiance. Recently we started doing single color experiments using Agilent
array.
I think that we does not have any specific function to read the single
channel data, But as once you suggested I used the dummy values for 'R' and
'Rb' and performed furhter normalization by the following commands.
>txt_files <- dir(pattern=".txt")
>RG<-read.maimages(txt_files, columns = list(G = "gMeanSignal", Gb =
"gBGMeanSignal",
R="gMedianSignal",Rb="gBGMedianSignal"),
annotation= c("Row", "Col",
"ProbeUID","ProbeName", "GeneName",))
> Rgene<-backgroundCorrect(RG$G,method='normexp')
>MA<-normalizeBetweenArrays(Rgene$G,method="scale")

Is this is fine?

In this case I have three samples which are treated and the other three
without treatment. I would like to get differentially expressed genes
between the treated and untreated. In this scenario what should be the best
way to create a desing matrix for my further analysis. Is the following will
be fine?
design <- cbind(tx=c(1,1,0,0,0,ntx=c(0,0,1,1,1))
Is there anything which I should specifically taken care of in these type of
scenario?

Thanks
-- 

Regards,
Abhilash
Graduate student
Department of Molecular biology
IOB

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