RUVcorr (version 1.4.2)

PCAPlot: Plot principle component analysis for gene expression data.

Description

PCAPlot generates principle component plots for with the possibility to color arrays according to a known factor.

Usage

PCAPlot(Y, comp = c(1, 2), anno = NULL, Factor = NULL, numeric = FALSE, new.legend = NULL, title)

Arguments

Y

A matrix of gene expression values or an object of class prcomp.

comp

A vector of length 2 specifying which principle components to be used.

anno

A dataframe or a matrix containing the annotation of the arrays.

Factor

A character string describing the column name of anno used for coloring.

numeric

A logical scalar indicating whether Factor is numerical.

new.legend

A vector describing the names used for labelling; if NULL labels in Factor are used.

title

A character string giving the title.

Value

PCAPlot returns a plot.

See Also

prcomp

Examples

Y<-simulateGEdata(500, 500, 10, 2, 5, g=NULL, Sigma.eps=0.1,
250, 100, intercept=FALSE, check.input=FALSE)
PCAPlot(Y$Y, title="")

## Create random annotation file
anno<-as.matrix(sample(1:4, dim(Y$Y)[1], replace=TRUE))
colnames(anno)<-"Factor"
try(dev.off(), silent=TRUE)
par(mar=c(5.1, 4.1, 4.1, 2.1), mgp=c(3, 1, 0), las=0, mfrow=c(1, 1))
PCAPlot(Y$Y, anno=anno, Factor="Factor", numeric=TRUE, title="")