"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, ...)
pointsfor possible values. Ignored if
"pairwise"to choose the top genes separately for each pairwise comparison between the samples or
"common"to select the same genes for all comparisons.
plot, and also to
"MDS"is invisibly returned. This is a list containing the following components:
cmdscalegiven the distance matrix
topgenes. 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
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)))