PCAPlot: Plot principle component analysis for gene expression data.

In PeteHaitch/RUVcorr: Removal of unwanted variation for gene-gene correlations and related analysis.

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.

Author(s)

Saskia Freytag

See Also

prcomp

Examples

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

# Create random annotation file
anno<-as.matrix(sample(1:4, dim(Y$Y)[1], replace=TRUE))
colnames(anno)<-"Factor"
try(dev.off(), silent=TRUE)
PCAPlot(Y$Y, anno=anno, Factor="Factor", numeric=TRUE, title="")

PeteHaitch/RUVcorr documentation built on May 8, 2019, 1:31 a.m.