
Dear All,

Now I'm using limma to do microarray analysis. 

In limma User's Guide (ver. 22 October 2008), Chapter 9, there is only one case for "Separate Channel Analysis of Two-Color Data".
My question is, if there is duplication spots within each array (e.g. 3 spots/gene) , how to use duplicateCorrelation() to correct within-array replicate spots in this case?

Best Wishes,

Kunlin Zhang

I also copy the information of this case:

FileName Cy3 Cy5
File1 wt.young wt.old
File2 wt.old wt.young
File3 mu.young mu.old
File4 mu.old mu.young

> MA <- normalizeBetweenArrays(MA, method="Aquantile")
> targets2 <- targetsA2C(targets)
> targets2
channel.col FileName Target
File1.1 1 File1 wt.young
File1.2 2 File1 wt.old
File2.1 1 File2 wt.old
File2.2 2 File2 wt.young
File3.1 1 File3 mu.young
File3.2 2 File3 mu.old
File4.1 1 File4 mu.old
File4.2 2 File4 mu.young

The following code produces a design matrix with eight rows and four columns:

> u <- unique(targets2$Target)
> f <- factor(targets2$Target, levels=u)
> design <- model.matrix(~0+f)
> colnames(design) <- u

Inference proceeds as for within-array replicate spots except that the correlation to be estimated
is that between the two channels for the same spot rather than between replicate
spots.

> corfit <- intraspotCorrelation(MA, design)
> fit <- lmscFit(MA, design, correlation=corfit$consensus)

Subsequent steps proceed as for log-ratio analyses. For example if we want to compare wildtype
young to mutant young animals, we could extract this contrast by

> cont.matrix <- makeContrasts("mu.young-wt.young",levels=design)
> fit2 <- contrasts.fit(fit, cont.matrix)
> fit2 <- eBayes(fit2)
> topTable(fit2, adjust="BH")




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s. It's easy!

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