plotMDS: Multidimensional scaling plot of distances between gene...

In limma: Linear Models for Microarray Data

Description

Plot samples on a two-dimensional scatterplot so that distances on the plot approximate the typical log2 fold changes between the samples.

Usage

## Default S3 method:
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, plot = TRUE, ...)
## S3 method for class 'MDS'
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, for example an ExpressionSet or an EList. Rows represent genes or genomic features while columns represent samples.
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 if labels is non-NULL.
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 x-axis.
ylab	title for the y-axis.
plot	logical. If TRUE then a plot is created on the current graphics device.
...	any other arguments are passed to plot, and also to text (if pch is NULL).

Details

This function uses multidimensional scaling (MDS) to produce a principal coordinate (PCoA) or principal component (PCA) plot showing the relationships between the expression profiles represented by the columns of x. If gene.selection = "common", or if the top is equal to or greater than the number of rows of x, then a PCA plot is constructed from the top genes with largest standard deviations across the samples.

If gene.section = "pairwise" and top is less than nrow(x) then a PCoA plot is produced and distances on the plot represent the leading log2-fold-changes. The leading log-fold-change between a pair of samples is defined as the root-mean-square average of the top largest log2-fold-changes between those two samples. The PCA and PCoA plots produced by gene.selection="common" and gene.selection="pairwise", respectively, use similar distance measures but the PCA plot uses the same genes throughout whereas the PCoA plot potentially selects different genes to distinguish each pair of samples. The pairwise choice is the default. It potentially gives better resolution than a PCA plot if different molecular pathways are relevant for distinguishing different pairs of samples.

If pch=NULL, then each sample is represented by a text label, defaulting to the column names of x. If pch is not NULL, then plotting symbols are used.

See text for possible values for col and cex.

Value

If plot=TRUE, a plot is created on the current graphics device.

An object of class "MDS" is also 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

Author(s)

Di Wu and Gordon Smyth

References

Ritchie, ME, Phipson, B, Wu, D, Hu, Y, Law, CW, Shi, W, and Smyth, GK (2015). limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Research 43, e47. http://nar.oxfordjournals.org/content/43/7/e47

See Also

cmdscale

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)))

Example output

limma documentation built on Nov. 8, 2020, 8:28 p.m.