| plotMDS {limma} | R Documentation |
Plot the sample relations based on MDS.
plotMDS(x, top=500, labels=colnames(x), col=NULL, cex=1, dim.plot=c(1,2), ndim=max(dim.plot),...)
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 |
... |
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 for the top top genes which best distinguish that pair of samples.
That is, Euclidean distance is used, but for a different gene subset for each pair of samples.
See text for possible values for col and cex.
A plot is created on the current graphics device.
Di Wu and Gordon Smyth
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.
plotMDS(x, col=c(rep("black",3), rep("red",3)) )
# with labels as groups, group indicators are plotted.
plotMDS(x, col=c(rep("black",3), rep("red",3)), labels= c(rep("Grp1",3), rep("Grp2",3)))