normalizeARRm {ARRmNormalization} | R Documentation

## Perform ARRm normalization

### Description

This function perform Adaptive Robust Regression method (ARRm) normalization on Beta values. The method corrects for background intensity, dye bias and spatial on-chip position. By default, chip mean correction is also performed.

### Usage

    normalizeARRm(betaMatrix, designInfo, backgroundInfo, outliers.perc = 0.02,
    goodProbes = NULL,chipCorrection=TRUE)

### Arguments

`betaMatrix` | `matrix` containing the Beta values. Rows are probes, columns are samples.
`designInfo` | A `data.frame` containing a column named `chipInfo` containing the chip indices and a column named `positionInfo` containing the position indices
`backgroundInfo` | A `data.frame` containing two columns: `green` contains the background intensity in the green channel for each sample and `red` contains the background intensity in the red channel for each sample
`outliers.perc` | Proportion (between 0 and 1) of outliers to be removed from the ARRm regression
`goodProbes` | Ids of the probes to be normalized (Id. of the form "cg00000029")
`chipCorrection` | logical, should normalization correct for chip mean?

### Value

A `matrix` containing the normalized Beta values

### Author(s)

Jean-Philippe Fortin <jfortin@jhsph.edu>

### See Also

`getBackground` to see how to obtain background information from control probes, and `getDesignInfo` to see how to obtain position and chip indices

### Examples

    data(greenControlMatrix)
    data(redControlMatrix)
    data(sampleNames)
    data(betaMatrix)
    backgroundInfo=getBackground(greenControlMatrix, redControlMatrix)
    designInfo=getDesignInfo(sampleNames)
    normMatrix=normalizeARRm(betaMatrix, designInfo, backgroundInfo, outliers.perc = 0.02)

[Package ARRmNormalization version 1.20.0 Index]