Ringo (version 1.36.0)

corPlot: Function to plot correlation of different samples

Description

This function can be used to visualise the (rank) correlation in expression data between different samples or sample groups.

Usage

corPlot(eset, samples = NULL, grouping = NULL, ref = NULL, useSmoothScatter = TRUE, ...)

Arguments

eset

object of class ExpressionSet holding the array data, or a numeric matrix instead

samples

which samples' expression shall be correlated to each other; either a numeric vector of sample numbers in the ExpressionSet or a character vector that must be contained in the sampleNames of the ExpressionSet, default NULL means take all samples in the ExpressionSet

grouping

an optional factor vector defining if the correlation should be assessed between groups of samples, rather than individual samples. If two or more samples are assigned into the same group, the mean over these samples' expression values is taken before computing correlation. Default NULL means assess correlation between individual samples only.

ref

reference than only applies if argument grouping is given; see relevel

useSmoothScatter

logical; should the function smoothScatter be used?

...

additional arguments, not used yet

Value

The function only returns NULL (invisible). The function is called for its side-effect producing the pairs plot.

See Also

ExpressionSet, relevel, pairs, smoothScatter

Examples

   data(sample.ExpressionSet)
   if (interactive()) 
   corPlot(sample.ExpressionSet,
    grouping=paste(sample.ExpressionSet$sex,
    sample.ExpressionSet$type, sep="."))