quantilePlots {ARRmNormalization} | R Documentation

## Diagnostic plots for evaluation of background effects and dye bias effects on different percentiles

### Description

For each probe type, and for each sample, several percentiles are plotted against background intensity, and also against dye bias.

### Usage

    quantilePlots(quantiles,backgroundInfo,designInfo,percentilesI=NULL,percentilesII=NULL)

### Arguments

`quantiles` | A `list` containing three matrices. `list$green`, `list$red` and `list$II` must contain respectively the matrices of percentiles obtained from a `betaMatrix` for the Type I Green probes, Type I Red probes and Type II probes. See `getQuantiles`.
`designInfo` | `designInfo` matrix returned by `getDesignInfo`
`backgroundInfo` | `"backgroundInfo"` matrix returned by `getBackground`
`percentilesI` | List of percentiles to be plotted for Type I probes. Must be a vector of integers from 1 to 100. If set to `NULL` (by default), the sequence (5,10,...,95) of percentiles is plotted.
`percentilesII` | List of percentiles to be plotted for Type II probes. Must be a vector of integers from 1 to 100. If set to `NULL` (by default), the sequence (10,20,...,90) of percentiles is plotted.

### Value

Plots are produced and saved as pdf in the current directory.

### Author(s)

Jean-Philippe Fortin <jfortin@jhsph.edu>

### Examples

    data(greenControlMatrix)
    data(redControlMatrix)
    data(sampleNames)
    data(betaMatrix)
    quantiles=getQuantiles(betaMatrix)
    backgroundInfo=getBackground(greenControlMatrix, redControlMatrix)
    designInfo=getDesignInfo(sampleNames)
    quantilePlots(quantiles, backgroundInfo, designInfo)

[Package ARRmNormalization version 1.20.0 Index]