| clusterAlgorithmClara-class {goCluster} | R Documentation |
This can be used to group a dataset according to partitioning around medoids. The resulting gene groups can subsequently be analysed for significant enrichment of specific annotations.
The class provides a wrapper around the clusterclara
function. Please read the corresponding documentation for further
details.
clusters:"numeric", determines
the number of clusters the partitioning around medoids will
identify.repeats:"numeric", specifies
how often the clustering is repeated in case clara is not run with
a fixed initialization.fixed:"logical", if true, a
fixed seed will be used for the partitioning around medoids. distance:"character",
specifies the distance matrix that will be used.
Additional slots are described in the documentation of the
clusterAlgorithm-class and clusterModule-class.
Class "clusterAlgorithm", directly.
Class "clusterModule", by class "clusterAlgorithm".
signature(object = "clusterAlgorithmClara"):
interactive setup of the class. You will be asked to specify the
number of clusters clara clustering should result in and whether a
fixed seed should be used. If not the class offers to repeat the
clustering. In addition the distance matrix needs to be defined.signature(object = "clusterAlgorithmClara"):
returns the configuration of the object as a list. This list can
again be used for the non-interactive setup of the class. signature(object = "clusterAlgorithmClara"):
non-interactive setup of the class. The options are specified
using a list. signature(object = "clusterAlgorithmClara"): run the
clustering. signature(object = "clusterAlgorithmClara"): remove all
cluster data so that the execute function can be run again.signature(object = "clusterAlgorithmClara"):
This function prints some basic information about the content of
this object.Gunnar Wrobel, work@gunnarwrobel.de, http://www.gunnarwrobel.de.
clusterclara,
goCluster-class,
clusterModule-class,
clusterAlgorithm-class,
clusterAlgorithmKmeans-class,
clusterAlgorithmPam-class,
clusterAlgorithmHclust-class,
.
## Predefined setup for goCluster
## (This configuration selects the
## clara clustering)
data(benomylsetup)
## Setup a new goCluster object
test <- new("goCluster")
setup(test) <- benomylsetup
## Retrieve annotation
test@data <- execute(test@data, test)
## Cluster the dataset
test@algo <- execute(test@algo, test)