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 1.3.3 Index]