mdsPlot: Multi-dimensional scaling plots giving an overview of similarities and differences between samples.

Description

Multi-dimensional scaling (MDS) plots showing a 2-d projection of distances between samples.

Usage

mdsPlot(dat, numPositions = 1000, sampNames = NULL, sampGroups = NULL, xlim, ylim, pch = 1, pal = brewer.pal(8, "Dark2"), legendPos = "bottomleft", legendNCol, main = NULL)

Arguments

dat

An RGChannelSet, a MethylSet or a matrix. We either use the getBeta function to get Beta values (for the first two) or we assume the matrix contains Beta values.

numPositions

Use the numPositions genomic positions with the most methylation variability when calculating distance between samples.

sampNames

Optional sample names. See details.

sampGroups

Optional sample group labels. See details.

xlim

x-axis limits.

ylim

y-axis limits.

pch

Point type. See par for details.

pal

Color palette.

legendPos

The legend position. See legend for details.

legendNCol

The number of columns in the legend. See legend for details.

main

Plot title.

Value

No return value. Plots are produced as a side-effect.

Details

Euclidean distance is calculated between samples using the numPositions most variable CpG positions. These distances are then projected into a 2-d plane using classical multidimensional scaling transformation.

References

I Borg, P Groenen. Modern Multidimensional Scaling: theory and applications (2nd ed.) New York: Springer-Verlag (2005) pp. 207-212. ISBN 0387948457. http://en.wikipedia.org/wiki/Multidimensional_scaling

Examples

Run this code

if (require(minfiData)) {

names <- pData(MsetEx)$Sample_Name
groups <- pData(MsetEx)$Sample_Group
mdsPlot(MsetEx, sampNames=names, sampGroups=groups)

}

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