plotMDS: Multidimensional scaling plot of microarray data
In richierocks/limma2: Linear Models for Microarray Data

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

Plot the sample relations based on MDS. Distances on the plot can be interpreted in terms of leading log2-fold-change.

## Default S3 method:
plotMDS(x, top=500, labels=colnames(x), col=NULL, cex=1, dim.plot=c(1,2), ndim=max(dim.plot), gene.selection="pairwise",
        xlab=paste("Dimension",dim.plot[1]), ylab=paste("Dimension",dim.plot[2]), ...)
## S3 method for class 'MDS'
plotMDS(x, labels=colnames(x$distance.matrix), col=NULL, cex=1, dim.plot=x$dim.plot, xlab=paste("Dimension",dim.plot[1]), ylab=paste("Dimension",dim.plot[2]),...)

`x`	any data object which can be coerced to a matrix, such as `ExpressionSet` or `EList`.
`top`	number of top genes used to calculate pairwise distances.
`labels`	character vector of sample names or labels. If `x` has no column names, then defaults the index of the samples.
`col`	numeric or character vector of colors for the plotting characters.
`cex`	numeric vector of plot symbol expansions.
`dim.plot`	which two dimensions should be plotted, numeric vector of length two.
`ndim`	number of dimensions in which data is to be represented
`gene.selection`	character, `"pairwise"` to choose the top genes separately for each pairwise comparison between the samples or `"common"` to select the same genes for all comparisons
`xlab`	title for the x-axis
`ylab`	title for the y-axis
`...`	any other arguments are passed to `plot`.

This function is a variation on the usual multdimensional scaling (or principle coordinate) plot, in that a distance measure particularly appropriate for the microarray context is used. The distance between each pair of samples (columns) is the root-mean-square deviation (Euclidean distance) for the top top genes. Distances on the plot can be interpreted as leading log2-fold-change, meaning the typical (root-mean-square) log2-fold-change between the samples for the genes that distinguish those samples.

If gene.selection is "common", then the top genes are those with the largest standard deviations between samples. If gene.selection is "pairwise", then a different set of top genes is selected for each pair of samples. The pairwise feature selection may be appropriate for microarray data when different molecular pathways are relevant for distinguishing different pairs of samples.

See text for possible values for col and cex.

A plot is created on the current graphics device.

An object of class "MDS" is invisibly returned. This is a list containing the following components:

`distance.matrix`	numeric matrix of pairwise distances between columns of `x`
`cmdscale.out`	output from the function `cmdscale` given the distance matrix
`dim.plot`	dimensions plotted
`x`	x-xordinates of plotted points
`y`	y-cordinates of plotted points
`gene.selection`	gene selection method

Di Wu and Gordon Smyth

cmdscale

An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.

# Simulate gene expression data for 1000 probes and 6 microarrays.
# Samples are in two groups
# First 50 probes are differentially expressed in second group
sd <- 0.3*sqrt(4/rchisq(1000,df=4))
x <- matrix(rnorm(1000*6,sd=sd),1000,6)
rownames(x) <- paste("Gene",1:1000)
x[1:50,4:6] <- x[1:50,4:6] + 2
# without labels, indexes of samples are plotted.
mds <- plotMDS(x,  col=c(rep("black",3), rep("red",3)) )
# or labels can be provided, here group indicators:
plotMDS(mds,  col=c(rep("black",3), rep("red",3)), labels= c(rep("Grp1",3), rep("Grp2",3)))