Normalize the data from the two platforms separately and then combine by just combining the rows from one platform to the other. Since testing for differential expression is done per-gene, there is not a need to have 133a and 133b "combined" in any formal way.

Well this is old but still we were able to solve this issue and I'd like to share it so no one else will suffer.

I assume the two chips you use share some probesets and you only want to deal with those shared ones as you can't really compare non existing probes.

You need to tinker with the code of rma function. You can see the code by ctrl + click on the rma function written in your script but I'll try to clear enough here so you won't need to do that.

Also edit: do not load Gdata package beforehand

The critical parts are 1) :

exprs <- .Call("rma_c_complete_copy", pm(object, subset),
          pNList, ngenes, normalize, background, bgversion,
          verbose, PACKAGE = "affy")

that outputs the resulting expressions and 2)

new("ExpressionSet", phenoData = phenoData(object), annotation = annotation(object), 
    protocolData = protocolData(object), experimentData = experimentData(object), 
    exprs = exprs

that creates the resulting object of the function.

In part 1 inputs normalize, background, bgversion and verbose normally comes from the inputs of the original rma function. Fill them as you would normally. To set them to default just use

verbose = TRUE
destructive = TRUE
normalize = TRUE
bgversion = 2

There are 3 things you need to create manualy: pNList, nGenes and pm(object, subset). Read both groups seperately. I just placed them in different directories

setwd('HGU133a')
affyA <- ReadAffy()
setwd('..')
setwd('HGU133b')
affyB <- ReadAffy()
setwd('..')

You want to get the common probes so do

pNListA = probeNames(affyA)
pNListB = probeNames(affyA)
subsetList = pNListA[pNListA %in% pNListB]

Now you have the subsets so you can request pms of both samples and stitch them together

subsetPmA = pm(affyA, unique(subsetList))
subsetPmB = pm(affyB, unique(subsetList))
allPm = cbind(subsetPm, subsetPmOldOrdered) #this will go into the .call function

The two variables left for part 1 is simple just do

ngenes = length(unique(subsetList))
pNList = split(0:(length(subsetList) - 1), subsetList)

and run part 1 to get normalized expression values

exprs <- .Call("rma_c_complete", allPm, 
               pNList, ngenes, normalize, background, bgversion, 
               verbose, PACKAGE = "affy")

To create the new object you need to stitch the components of the two objects together. For annotation, if one of your chips has a subset of the probes in the other probe, just use that one, but I don't think it matters that much, you have what you need at this point. Just don't mix up the order when you are using combine.

phenoD = combine(phenoData(affyA), phenoData(affyB))
annot =  annotation(affyA)
protocolD = combine(protocolData(affyA), protocolData(affyB))
experimentD = experimentData(affyA)

newNormalized = new("ExpressionSet", phenoData = phenoD, annotation = annot, 
    protocolData = protocolD, experimentData = experimentD, 
    exprs = exprs)

That's it. you now have your handcrafted rma output. Use it as you normally would.

I suspect that there is no standardized way to do this - but if you have already found a published method I would go with that - and cite it.

In the end it all depends on whether the genes used for normalization do actually represent genes that don't change across the experiments. That's probably the critical element.

The package matchprobes for R (I am not sure it is maintained) has a function (combineAffyBatch) to combine different chips. It combines the probes by sequence, not by id.