I have a large dataset of ~10000 CEL files that needs to be normalized. I 
tried using aroma.affy for this, but the job is running for more than 3 
weeks and in need of help/suggestions. The data is in standard affy rat2302 
chip. I steps I used are (based on: 


verbose <- Arguments$getVerbose(-8, timestamp=TRUE)
chipType <- "rat2302"
cdf <- AffymetrixCdfFile$byChipType(chipType) #, tags="r3")
cs <- AffymetrixCelSet$byName("tissues", cdf=cdf)
bc <- RmaBackgroundCorrection(cs)
csBC <- process(bc,verbose=verbose)
qn <- QuantileNormalization(csBC, typesToUpdate="pm")
csN <- process(qn, verbose=verbose)

plm <- RmaPlm(csN)
fit(plm, verbose=verbose)

All steps were ok but  the last step : fit(plm,verbose=verbose) is running 
for nearly 3 weeks. Is this needed for probe summarization? What exactly 
does this fit do? 

Any suggestion will be helpful.


When reporting problems on aroma.affymetrix, make sure 1) to run the latest 
version of the package, 2) to report the output of sessionInfo() and 
traceback(), and 3) to post a complete code example.

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