[BioC] Save rma normalization results for later use

[Laurent Gautier]

To try out quickly, the RefPlus package can be an "off-the-shelf" option.


L.




Wolfgang Huber wrote:
> 
> Hi Christian,
> 
> there is some subtlety how quantile normalisation deals with ties 
> (values on one array that are exactly the same), but I think your real 
> problem will be the probset summarisation step, where you need to 
> extract, store, and later apply the probe weights in the RMA model.
> 
> Henrik Bengtsson recently suggested (on the bioc-devel list):
> 
> For the purpose of fitting the RMA-style log-additive model, I'd say 
> that Ben [Bolstad]'s robust estimators implemented in preprocessCore are 
> much
> better (and more flexible, e.g. support weights) than using median
> polish.  See
> 
>    help("rcModelPLM", package="preprocessCore")
> 
> 
> 
> Best wishes
>      Wolfgang
> 
> ----------------------------------------------------
> Wolfgang Huber, EMBL-EBI, http://www.ebi.ac.uk/huber
> 
> 
> 
>  Ruckert wrote:
>> I have a bunch of 2000 arrays I want to normalize with rma() from affy 
>> package. Then from time to time there will be single arrays to be 
>> analyzed together with these 2000. To apply the same normalization 
>> procedure to the single arrays later I want to split the rma step in 
>> its elements.
>>
>> bg.correct(data, method="rma")
>> As it's array wise I think no problem for the single array.
>>
>> normalize(data,  method="quantiles")
>> I think here I need to save the mean values for each row to normalize 
>> the single array later with this values (I know it's not totally exact 
>> but I think acceptable). In my understanding of the quantile 
>> normalization the sorted perfect match values should be exactly the 
>> same for every sample, but I got differences.
>>
>> So my questions are:
>> 1. How does the last step look to got exactly the same results as with 
>> rma()
>> 2. Why the differences in quantile normalization?
>> 3. Is there a better way to handle this task?
>>
>> Any help would be appreciated,
>>
>> Christian
>>
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> 
> 
> -- 
> 
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