testCpG: function to cluster sequences based on their CpG and GC...
In cobindR: Finding Co-occuring motifs of transcription factor binding sites

Description Usage Arguments Value Author(s) References See Also Examples

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.

1 2	## S4 method for signature 'cobindr' testCpG(x, max.clust = 4, do.plot = F, n.cpu = NA)

`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.

`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

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

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

plot.gc

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)