[BioC] split arrays

[Jamain, Adrien J]

Matt,

I am not familiar with the maize arrays, but I am using the following
procedure for Affymetrix moe430 split arrays, which have ~160 probesets
in common between A and B:
1) background-correct each chip separately at probe-level
2) get a measure of expression at probeset-level
3) plot the common probesets against each other for each pair of each
chips. If you observe the same thing as me, you will see that the trend
is linear but with intercept != 0 and slope != 1. 
4) scale the B chip with those estimated intercept and slope

Steps 1 and 2 are easily done with rma( , normalize=F).
Wolfgang Huber and I are currently writing a little package which does
steps 3 and 4 automatically.

I'm not sure whether this procedure could make sense or be adapted
somehow to your maize arrays (do they have enough probes in common?),
but anyway, some food for thoughts...

Adrien

> Gordon,
> 
> Recently you advised someone with a split set of maize arrays 
> that they could do their analysis by reading all the A slides 
> into an RGList and normalizing, then doing the same with the 
> B slides, and then combining the two datasets via
> rbind() of the two MAList objects. I have a similar (the 
> same?) set of arrays and some of the users of these arrays 
> have noted that the A and B slides perform differently, i.e. 
> more background on the B slide, for whatever reason. Though 
> I'm not actually convinced this is true, it makes me wonder 
> whether the two datasets should be combined at all since 
> there may be a "between array set"
> source of variation. Am I right to segregate these sets or is 
> there some overwhelming benefit to combining them? I'm no 
> statistician and would appreciate your take.
> 
> Thanks,
> 
> Matt