quantilePlots: Diagnostic plots for evaluation of background effects and dye...

In ARRmNormalization: Adaptive Robust Regression normalization for Illumina methylation data

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 <[e-mail]>

Examples

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

ARRmNormalization documentation built on Nov. 8, 2020, 5:25 p.m.