You would normally be using aquantile if you are doing single-channel
analysis.  Otherwise, I'm not sure why you would use aquantile.

Sean
On Sep 13, 2011 10:59 AM, "Hari Easwaran" <hariharan.pe@gmail.com> wrote:
> Hi Sean,
> So it is fine to have the log ratios normalized before the data is subject
> to Aquantile normalization?
>
> Thanks for your help.
>
> Hari
>
>
> On Tue, Sep 13, 2011 at 10:43 AM, Sean Davis <sdavis2@mail.nih.gov> wrote:
>
>> On Tue, Sep 13, 2011 at 10:35 AM, Hari Easwaran <hariharan.pe@gmail.com>
>> wrote:
>> > 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.
>>
>> Hi, Hari.
>>
>> normalizeBetweenArrays is often used AFTER normalizeWithinArrays for
>> two-color arrays, so an MAList is often used as the object for
>> normalizeBetweenArrays. Per the help page, the Aquantile method does
>> leave the M-values unchanged.
>>
>> Sean
>>
>
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