normalize.exprSet {affyPLM}R Documentation

Normalization applied to exprSets

Description

Allows the user to apply normalization routines to exprSets.

Usage

  normalize.exprSet.quantiles(eset,transfn=c("none","log","antilog"))
  normalize.exprSet.loess(eset,transfn=c("none","log","antilog"),...)
  normalize.exprSet.contrasts(eset, span = 2/3, choose.subset = TRUE, subset.size = 5000, verbose = TRUE, family = "symmetric",transfn=c("none","log","antilog"))
  normalize.exprSet.qspline(eset,transfn=c("none","log","antilog"),...)
  normalize.exprSet.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.exprSet.scaling(eset,trim=0.02,baseline=-1,transfn=c("none","log","antilog"))

Arguments

eset An exprSet
span parameter to be passed to the function loess.
choose.subset
subset.size
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 exprSet 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.exprSet.methods should give you a list of methods that you may use. note that you can also use the normalize function on exprSets. Use method to select the normalization method.

Value

A normalized exprSet.

Author(s)

Ben Bolstad, bolstad@stat.berkeley.edu

References

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

See Also

normalize

Examples

data(affybatch.example)
eset <- rma(affybatch.example,normalize=FALSE,background=FALSE)
normalize(eset)

[Package affyPLM version 1.6.0 Index]