plotSample: Sample representation for Principal Component Analysis

In EMA: Easy Microarray Data Analysis

Description

Sample representation for Principal Component Analysis (PCA)

Usage

plotSample(acp, axes = c(1, 2), new.plot = FALSE, lab = "quality",
palette="rainbow", lim.cos2.sample = 0, text = TRUE,
lab.title = NULL, ellipse=FALSE, ...)

Arguments

acp	result from PCA or do.pca function
axes	axes for sample representation, by default 1 and 2
new.plot	if TRUE, a new graphical device is created, by default = FALSE
lab	character. Sample label, by default = quality (points are labelled by quality index). If lab=NULL, no label is displayed.
lim.cos2.sample	keep samples with cos2 >= lim.cos2.sample, by default = 0
palette	characters. Name of a palette, By default, "rainbow" palette
text	add sample name or not, by default = TRUE
lab.title	title for the legend, by default = NULL
ellipse	if TRUE and lab provided, draw 95$%$ confidence ellipse around barycentre of each group
...	Arguments to be passed to methods, such as graphical parameters (see 'par').

Value

Sample representation on axes axes[1] and axes[2] colored by quality index (= cos2 of samples) or colored by lab

Author(s)

Nicolas Servant, Eleonore Gravier, Pierre Gestraud, Cecile Laurent, Caroline Paccard, Anne Biton, Jonas Mandel, Bernard Asselain, Emmanuel Barillot, Philippe Hupe

See Also

runPCA,PCA

Examples

data(marty)

## PCA on sample - example set
example.subset <- marty[1:100,]
pca <- runPCA(t(example.subset), verbose = FALSE, plotInertia = FALSE, plotSample = FALSE)

## Sample plot of PCA object colored by tumour type
perso.colors <- colorRampPalette(c("red", "green"))
## Not run: 
plotSample(pca, lab = marty.type.cl, palette="perso.colors", ellipse=TRUE)

## End(Not run)

EMA documentation built on March 26, 2020, 8:40 p.m.