testCpG {cobindR} | R Documentation

## function to cluster sequences based on their CpG and GC content

### Description

diagnostical function - GC content and CpG content are clustered using 2D gaussian models (Mclust). FALSE is returned if > max.clust (default=1) subgroups are found using the bayesian information criterion (BIC). If do.plot=TRUE, the results are visualized.

### Usage

    ## S4 method for signature 'cobindr'
    testCpG(x, max.clust = 4, do.plot = F, n.cpu = NA)

### Arguments

`x` | an object of the class "cobindr", which will hold all necessary information about the sequences and the hits.
`max.clust` | integer describing the maximal number of clusters which are used for separating the data.
`do.plot` | logical flag, if do.plot=TRUE a scatterplot for the GC and CpG content for each sequence is produced and the clusters are color coded.
`n.cpu` | number of CPUs to be used for parallelization. Default value is 'NA' in which case the number of available CPUs is checked and than used.

### Value

`result` | logical flag, FALSE is returned if more than one subgroups are found using the bayesian information criterion (BIC)
`gc` | matrix with rows corresponding to sequences and columns corresponding to GC and CpG content

### Author(s)

Robert Lehmann <r.lehmann@biologie.hu-berlin.de>

### References

the method uses clustering functions from the package "mclust" (http://www.stat.washington.edu/mclust/)

### See Also

`plot.gc`

### Examples

    cfg <- cobindRConfiguration()
    sequence_type(cfg) <- 'fasta'
    sequence_source(cfg) <- system.file('extdata/example.fasta', package='cobindR')
    # avoid complaint of validation mechanism
    pfm_path(cfg) <- system.file('extdata/pfms',package='cobindR')
    pairs(cfg) <- ''
    runObj <- cobindr( cfg)
    testCpG(runObj, max.clust = 2, do.plot = TRUE)

[Package cobindR version 1.20.0 Index]