[BioC] Save rma normalization results for later use

[Wolfgang Huber]

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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