normalize.AffyBatch.normalize2Reference: Quantile Normalization to a Reference Distribution
In caret: Classification and Regression Training

Description Usage Arguments Details Value Author(s) References See Also Examples

Quantile normalization based upon a reference distribution. This function normalizes a matrix of data (typically Affy probe level intensities).

normalize.AffyBatch.normalize2Reference(
   abatch, 
   type = c("separate", "pmonly", "mmonly", "together"), 
   ref = NULL)

`abatch`	An `{AffyBatch}`
`type`	A string specifying how the normalization should be applied. See details for more.
`ref`	A vector of reference values. See details for more.

This method is based upon the concept of a quantile-quantile plot extended to n dimensions. No special allowances are made for outliers. If you make use of quantile normalization either through 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).

A normalized AffyBatch.

Max Kuhn, adapted from Ben Bolstad, bolstad@stat.berkeley.edu

Bolstad, B (2001) Probe Level Quantile Normalization of High Density Oligonucleotide Array Data. Unpublished manuscript

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")