[BioC] RMA normalisation of test microarray data to training data

[Benilton Carvalho]

If the interest is really at *normalization*: save the target
distribution estimated from the training set, then use
preprocessCore::normalize.quantiles.use.target.

b

On 13 January 2012 14:08, James W. MacDonald <jmacdon at med.umich.edu> wrote:
> Hi Daniel,
>
>
> On 1/13/2012 8:47 AM, Daniel Brewer wrote:
>>
>> Hello,
>>
>> We have done some analysis on a set of Affy microarray data normalised by
>> RMA and produced a predictor.  We would like to test this predictor on a
>> training set we have.  Is it possible to RMA normalise the test dataset so
>> that the probes have the same distribution as the training dataset without
>> normalising all the data together?  Our concern is that if you normalise
>> them all together then this would mean we would have to go through all the
>> analyses again.
>
>
> If you had used the frma package for the initial processing, then yes. There
> may even be some way to use frma with your extant processed data, but you
> would have to look to see.
>
> Best,
>
> Jim
>
>
>>
>> Thanks
>>
>> Dan
>>
>
> --
> James W. MacDonald, M.S.
> Biostatistician
> Douglas Lab
> University of Michigan
> Department of Human Genetics
> 5912 Buhl
> 1241 E. Catherine St.
> Ann Arbor MI 48109-5618
> 734-615-7826
>
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