[BioC] Adding chips to an existing set of normalised data

[Crispin Miller]

Hi!
Over the last few days we've been learning lots about alternate ways of dealing with low-intesity probesets and some pretty strong arguments in favour of using alternate techniques to deal with these. Firstly, thanks - the discussion has been really helpful and much appreciated! 

These have now sparked a different question for us:
We have an ever-increasing database of affymetrix chips... Currently these have been processed and normalised using MAS5.0. As we add arrays to the set, we can compare between them since the normalisation simply sets them to have the same average intensity. 

So the question is, if I am to normalise my data with, RMA say, I get a set of normalised arrays based on statistics generated over the set of chips I normalise - i.e. each array is normalised in the context of its peers, unlike MAS5.0 (as I understand it). This is, I think, due to the a(j) parameter in  the RMA model, or phi(j) for dChip which represent the probe affinity effects and can be estimated if we have 'enough arrays' (from Irizarray et al. 2003, NA Res paper).

Now, when we add experiments to the database, are the normalised expression levels calculated for one experimental chip-set comparable to the expression-levels computed for another. if not, do I need to apply RMA over the entire database each time I add a new experiment to it? And is this possible in a reasonable amount of time and memory? If not do people have alternate suggestions? We are particualrly interested in clustering and generation of expression profiles...

Crispin
http://bioinf.picr.man.ac.uk/mbcf/microarray_ma.shtml
 
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