I'm hoping someone can give me a little guidance for an unusual
normalization need.  I have 9 affy human ST arrays that have varying amounts
of RNA, so I can't use the standard quantile normalizaton approach.  The wet
lab process that involved the polya spike-ins was done in such a way as to
ensure the same amount of the polya spike-ins on each array. These probesets

Probe.Set.ID           mRna...Description            total_probes
7893306                AFFX-Bs-thr_st, polya_spike          1160
7894584                AFFX-Bs-lys_st, polya_spike           567
7894611                AFFX-Bs-dap_st, polya_spike         1189
7895139                AFFX-Bs-phe_st, polya_spike          844

I have done this successfully before using the 3' IVT yeast arrays, but on
those there were many more probesets, so I just ran expresso without the
quantile normalization step, and then did a weighted  cyclic loess using the

I see from the total_probes counts for these 4 probesets on the human ST
array that maybe I could use a weighted quantile normalization where I set
the weights = 0 for all the probes except the polya_spike probes.  So my
thought would be to do a RmaBackgroundCorrection() on all probes, then a
weighted QuantileNormalization() step, then followed with a RmaPlm() call,
as is done in Mark Robinson's doEverything() functions (which I use all the

If such an approach is advisable, would someone point out to me how to
identify the polya_spike probes, so I could then do some weighted
QuantileNormalization() or, if that's not possible, maybe a cyclic loess

Thanks very much for any help or pointers,
Richard P. Beyer, Ph.D. University of Washington
Tel.:(206) 616 7378 Env. & Occ. Health Sci. , Box 354695
Fax: (206) 685 4696 4225 Roosevelt Way NE, # 100
Seattle, WA 98105-6099

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