[BioC] Limma and Dye-Swap/Single Channel
Gordon K Smyth
smyth at wehi.EDU.AU
Thu Jul 15 03:44:57 CEST 2004
> I hybridized for two samples both Cy3-Label and Cy5-Label to one chip
> wtCy3+wtCy5 and mutantCy3+mutantCy5.
Why did you do this? Your only option now is to do a single-channel
analysis, comparing treatments across arrays, and this is an order of
magnitude less efficient than a direct comparison using Cy3 and Cy5
channels. Anyway, in limma you could use
MA <- normalizeBetweenArrays(yourRGList, method="quantile")
targetsC <- array2channel(yourtargets)
design <- model.matrix(~factor(targetsC$Target))
cor.fit <- intraspotCorrelation(MA,design)
fit <- lmscFit(MA,design,correlation=cor.fit$consensus)
fit <- eBayes(fit)
You will have to make sure that yourRGList contains no negative or missing
background-adjusted intensities before using the above. You could use for
RG <- backgroundCorrect(RG, method="normexp")
or similar to ensure this.
> I would like to normalize that data, but how?
> I tried to use the array2channel funtion, but how do I use the result?
> My targets looking like this:
> FileName Cy3 Cy5
> 1 e85b.txt WT WT
> 2 e87b.txt WT WT
> 3 e88b.txt 139 139
> 4 e89b.txt 139 139
> After array2channel:
> Channel FileName Target
> 1.1 1 e85b.txt WT
> 1.2 2 e85b.txt WT
> 2.1 1 e87b.txt WT
> 2.2 2 e87b.txt WT
> 3.1 1 e88b.txt 139
> 3.2 2 e88b.txt 139
> 4.1 1 e89b.txt 139
> 4.2 2 e89b.txt 139
> Can I get a model.Matrix out of that? Or is it not possible to normalize
> that way of hybridization?
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