Multidimensional scaling plot of distances between gene expression profiles
Plot samples on a two-dimensional scatterplot so that distances on the plot approximate the typical log2 fold changes between the samples.
"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, ...)
- any data object which can be coerced to a matrix, such as
- number of top genes used to calculate pairwise distances.
- character vector of sample names or labels. Defaults to
- plotting symbol or symbols. See
pointsfor possible values. Ignored if
- numeric vector of plot symbol expansions.
- integer vector of length two specifying which principal components should be plotted.
- number of dimensions in which data is to be represented.
"pairwise"to choose the top genes separately for each pairwise comparison between the samples or
"common"to select the same genes for all comparisons.
- title for the x-axis.
- title for the y-axis.
- any other arguments are passed to
plot, and also to
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
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.
"common", then the top genes are those with the largest standard deviations between samples.
"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.
text for possible values for
A plot is created on the current graphics device.An object of class
- numeric matrix of pairwise distances between columns of
- output from the function
cmdscalegiven the distance matrix
- dimensions plotted
- x-xordinates of plotted points
- y-cordinates of plotted points
- gene selection method
"MDS"is invisibly returned. This is a list containing the following components:
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
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)))