wrappers {clusterCons}

Functions to wrap command calls to clustering functions

Description

These are primarily internal functions called by cluscomp to execute clustering runs and are unlikely to be used directly. The wrappers are detailed in the algorithm.R file of the clusterCons package and the user can add their own wrappers to this to extend the number of algorithms supported. These five wrappers allow the user to specify the conditions under which the corresponding clustering algorithms are run and follow exactly the same specifications as the corresponding cluster functions (see agnes, pam, hclust, diana and kmeans).

Usage

agnes_clmem(x, clnum, params = list())
pam_clmem(x, clnum, params = list())
hclust_clmem(x, clnum, params = list())
diana_clmem(x, clnum, params = list())
kmeans_clmem(x, clnum, params = list())
apcluster_clmem(x,clnum,params = list())

Arguments

x

A data.frame of numerical values to be clustered which must pass the data_check function. This function simply checks that there are no missing values, that all of the data is numeric and that row.names and column.names are unique. This is essential to ensure that individual rows (e.g. genes) and columns (e.g. experimental conditions) can be identified consistently.

clnum

The number of specified clusters. When using the cluscomp function, this will be over-ridden by the cluster range specified using the parameters clmin and clmax (see cluscomp for details).

params

A list of key, value pairs specifying the parameters to pass to the clustering algorithm. These follow the exact specification of the original functions in the cluster package (see agnes, pam, hclust, diana and kmeans).

Value

Returns a data.frame with row.names matching that of the data.

cm

cluster membership identifier specifying the cluster into which the row has been classified

Author(s)

Dr. T. Ian Simpson [e-mail]

References

Merged consensus clustering to assess and improve class discovery with microarray data. Simpson TI, Armstrong JD and Jarman AP. BMC Bioinformatics 2010, 11:590.

See Also

cluster, agnes, pam, hclust, diana, kmeans and apclusterK

Examples

#load some data
data(sim_profile);

#run a basic agnes clustering with 3 clusters
cm <- agnes_clmem(sim_profile,3);

#pass some more complex parameters
agnes_params = list(metric='manhattan',method='single');
cm <- agnes_clmem(sim_profile, 3,params=agnes_params);