positionPlots: Plots to evalue chip position effects on different...

In ARRmNormalization: Adaptive Robust Regression normalization for Illumina methylation data

Description

For each probe type, and for each sample, deviations from the chip mean are computed for a given percentile. These deviations are plotted against on-chip position.

Usage

positionPlots(quantiles,designInfo,percentiles=c(25,50,75))

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
percentiles	Vector of percentiles to be plotted. By default, the 25th, 50th and 75th percentiles are plotted. (percentiles=c(25,50,75)).

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)
positionPlots(quantiles, designInfo, percentiles=c(25,50,75))

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