Normalization applied to ExpressionSets

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Description

Allows the user to apply normalization routines to ExpressionSets.

Usage

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  normalize.ExpressionSet.quantiles(eset, transfn=c("none","log","antilog"))
  normalize.ExpressionSet.loess(eset, transfn=c("none","log","antilog"),...)
  normalize.ExpressionSet.contrasts(eset, span = 2/3,
      choose.subset=TRUE, subset.size=5000, verbose=TRUE, family="symmetric",
      transfn=c("none","log","antilog")) 
  normalize.ExpressionSet.qspline(eset, transfn=c("none","log","antilog"),...)
  normalize.ExpressionSet.invariantset(eset,prd.td=c(0.003, 0.007),
      verbose=FALSE, transfn=c("none","log","antilog"),
      baseline.type=c("mean","median","pseudo-mean","pseudo-median")) 
  normalize.ExpressionSet.scaling(eset, trim=0.02, baseline=-1,
      transfn=c("none","log","antilog"))

Arguments

eset

An ExpressionSet

span

parameter to be passed to the function loess.

choose.subset

use a subset of values to establish the normalization relationship

subset.size

number to use for subset

verbose

verbosity flag

family

parameter to be passed to the function loess.

prd.td

cutoff parameter (details in the bibliographic reference)

trim

How much to trim from the top and bottom before computing the mean when using the scaling normalization

baseline

Index of array to use as baseline, negative values (-1,-2,-3,-4) control different baseline selection methods

transfn

Transform the ExpressionSet before normalizing. Useful when dealing with expression values that are log-scale

baseline.type

A method of selecting the baseline array

...

Additional parameters that may be passed to the normalization routine

Details

This function carries out normalization of expression values. In general you should either normalize at the probe level or at the expression value level, not both.

Typing normalize.ExpressionSet.methods should give you a list of methods that you may use. note that you can also use the normalize function on ExpressionSets. Use method to select the normalization method.

Value

A normalized ExpressionSet.

Author(s)

Ben Bolstad, bmb@bmbolstad.com

References

Bolstad, BM (2004) Low Level Analysis of High-density Oligonucleotide Array Data: Background, Normalization and Summarization. PhD Dissertation. University of California, Berkeley.

Examples

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if (require(affydata)) {
  data(Dilution)
  eset <- rma(Dilution, normalize=FALSE, background=FALSE)
  normalize(eset)
}