pca: Principal components plot.

In phenoTest: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation.

Description

Creates a Principal Components plot where we can show paired samples, and confidence intervals for the mean of every group of interest. We can also choose the component or components we want to plot.

Usage

pca(x, group, group2, pair, names, ellipse = FALSE, main = "", components = c(1, 2))

Arguments

x	An object of class ExpressionSet.
group	Variable in pData(x) that contains the groups of interest. Samples of the same group will be plotted with the same color.
group2	Variable in pData(x) that contains secondary groups of interest. Sample of the same secondary group of interest will be plotted with the same symbol.
pair	Variable in pData(x) that contains the information about the pairs of data. Those pairs will be joined by a line.
names	Variable in pData(x) that contains the information about the names of the samples.
ellipse	If we want to plot ellipses with the 95 percent confidence intervals for every group.
main	A title for the plot.
components	Which components we want to plot. By default the first principal component will be plotted on the x axis and the second principal component will be plotted on the y axis. More than two components may be specified. If so multiple plots will be produced.

Author(s)

Evarist Planet

See Also

prcomp.

Examples

data(eset)
pca(x=eset, group='Relapse', names='GEOaccession')
#pca(x=eset, group='Relapse', names='GEOaccession', components=1:3)

phenoTest documentation built on Nov. 8, 2020, 7:53 p.m.