corrPlot: Function to plot correlation of different samples

In Ringo: R Investigation of ChIP-chip Oligoarrays

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.

Author(s)

Joern Toedling

See Also

ExpressionSet, relevel, pairs, smoothScatter

Examples

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

Ringo documentation built on Nov. 8, 2020, 5:34 p.m.