plotMDS
Multidimensional scaling plot of distances between gene expression profiles
Plot samples on a twodimensional scatterplot so that distances on the plot approximate the typical log2 fold changes between the samples.
Usage
"plotMDS"(x, top = 500, labels = NULL, pch = NULL, cex = 1, dim.plot = c(1,2), ndim = max(dim.plot), gene.selection = "pairwise", xlab = NULL, ylab = NULL, ...)
"plotMDS"(x, labels = NULL, pch = NULL, cex = 1, dim.plot = NULL, xlab = NULL, ylab = NULL, ...)
Arguments
 x
 any data object which can be coerced to a matrix, such as
ExpressionSet
orEList
.  top
 number of top genes used to calculate pairwise distances.
 labels
 character vector of sample names or labels. Defaults to
colnames(x)
.  pch
 plotting symbol or symbols. See
points
for possible values. Ignored iflabels
is nonNULL
.  cex
 numeric vector of plot symbol expansions.
 dim.plot
 integer vector of length two specifying which principal components should be plotted.
 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 xaxis.
 ylab
 title for the yaxis.
 ...
 any other arguments are passed to
plot
, and also totext
(ifpch
isNULL
).
Details
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 rootmeansquare deviation (Euclidean distance) for the top top
genes.
Distances on the plot can be interpreted as leading log2foldchange, meaning
the typical (rootmeansquare) log2foldchange 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
.
Value

A plot is created on the current graphics device.An object of class
 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
 xxordinates of plotted points
 y
 ycordinates of plotted points
 gene.selection
 gene selection method
"MDS"
is invisibly returned.
This is a list containing the following components:
References
Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNAsequencing and microarray studies. Nucleic Acids Research 43, e47. http://nar.oxfordjournals.org/content/43/7/e47
See Also
An overview of diagnostic functions available in LIMMA is given in 09.Diagnostics.
Examples
# 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)))