[BioC] How to validate normalization?

krasikov@science.uva.nl krasikov at science.uva.nl
Thu Dec 1 14:58:55 CET 2005

Dear all

Here I post again the question about normalization
I'm sorry that this question might be obvious for statistician.

The general question:
How to validate the normalization outcome?
Density plots?
I have tried "loees with aquantile" and "vsn" and outcome of the
decideTests is more or less the same - a lot of probes with differential

Here below the code I used in limma:

RG <- read.maimages(...)
...assigning spotTypes
...removing controlspots from the RG
RGb <- backgroundCorrect(RG,method="minimum")
MA <- normalizeWithinArrays(RGb, method="loess")
MA <- normalizeBetweenArrays(MA, method="Aquantile")
fit <- lmFit(MA, design)
fit <- contrasts.fit(fit, contrast.matrix)
fit <- eBayes(fit)
res <- decideTests(fit, method = "separate", adjust.method="BH",
+ p.value=0.001)
write.fit(fit, results = res, file = "...", digits=2, adjust="BH", sep="\t")

In that condition I've got 1800 up and 1800 down probes (out from 8100)
Decreasing p.value to 0.0001 gave me 800 up and 800 down.

I would like to mention here, that quite a big part of obtained data
is physiologically relevant in my experiment,
and the nature of the experiment suggests big differential expression.

Thanks in advance for any comments on this?

Best wishes

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