corPlot {Ringo} | R Documentation

## 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.

### 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="."))

[Package Ringo version 1.46.0 Index]