Dear BioC gurus,

I am trying to use the individual channels from a 2-color array as a measure
of gene expression level following the scripts in the Limma User Guide.

The code the guide suggests is:

MA <- normalizeBetweenArrays(MA, method="Aquantile")

Throughout the guide, MA is an objected created by normalizing the RG values
[for example using: MA <- normalizeWithinArrays(RG, method="loess")].
So for the command above, are the A values obtained from a 1st step of
normalization (like loess in this case) re-normalized using Aquantile?

Since the idea of using Aquantile is so that the log ratios are not changed
but the samples are adjusted to have the same distribution of A values, my
concern is that by using a 1st step of normalization the log-ratios do not
remain untouched. Does the guide mean to do the following:

MA <- normalizeBetweenArrays(RG, method="Aquantile")

where RG is the raw data.


I would really appreciate any input.

Sincerely,
Hari Easwaran
(Johns Hopkins University)

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