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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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