mdsPlot: Multi-dimensional scaling plots giving an overview of...
In minfi: Analyze Illumina Infinium DNA methylation arrays

Description Usage Arguments Details Value Author(s) References See Also Examples

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

1
2
3

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

`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.

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.

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

Martin Aryee aryee@jhu.edu.

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

qcReport, controlStripPlot, densityPlot, densityBeanPlot, par, legend

if (require(minfiData)) {

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

}