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normalize.AffyBatch.normalize2Reference(
abatch,
type = c("separate", "pmonly", "mmonly", "together"),
ref = NULL)
{AffyBatch}
AffyBatch
.rma
or expresso
please cite Bolstad et al, Bioinformatics (2003). The type argument should be one of
"separate","pmonly","mmonly","together"
which indicates whether
to normalize only one probe type (PM,MM) or both together or separately.
The function uses the data supplied in ref
to use as the reference
distribution. In other words, the PMs in abatch
will be normalized
to have the same distribution as the data in ref
. If ref
is
NULL
, the normalizing takes place using the average quantiles
of the PM values in abatch
(just as in normalize.AffyBatch.quantile
).
Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P. (2003) A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2) ,pp 185-193.
normalize
# first, let affy/expresso know that the method exists
# normalize.AffyBatch.methods <- c(normalize.AffyBatch.methods, "normalize2Reference")
# example not run, as it would take a while
# RawData <- ReadAffy(celfile.path=FilePath)
# Batch1Step1 <- bg.correct(RawData, "rma")
# Batch1Step2 <- normalize.AffyBatch.quantiles(Batch1Step1)
# referencePM <- pm(Batch1Step2)[,1]
# Batch1Step3 <- computeExprSet(Batch1Step2, "pmonly", "medianpolish")
# Batch2Step1 <- bg.correct(RawData2, "rma")
# Batch2Step2 <- normalize.AffyBatch.normalize2Reference(Batch2Step1, ref = referencePM)
# Batch2Step3 <- computeExprSet(Batch2Step2, "pmonly", "medianpolish")
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