clusterAlgorithmKmeans-class {goCluster} | R Documentation |
This can be used to group a dataset according to kmeans. The resulting gene groups can subsequently be analysed for significant enrichment of specific annotations.
The class provides a wrapper around the clusterkmeans
function. Please read the corresponding documentation for further
details.
repeats
:"numeric"
, specifies
how often the clustering should be repeated to account for the
statistical variability of the clustering.clusters
:"numeric"
, determines
the number of clusters the kmeans clustering will identify.
Additional slots are described in the documentation of the
clusterAlgorithm-class
and clusterModule-class
.
Class "clusterAlgorithm"
, directly.
Class "clusterModule"
, by class "clusterAlgorithm"
.
signature(object = "clusterAlgorithmKmeans")
:
interactive setup of the class. You will be asked to specify the
number of clusters the kmeans clustering should result in and how
often the clustering should be repeated.signature(object = "clusterAlgorithmKmeans")
:
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 = "clusterAlgorithmKmeans")
:
non-interactive setup of the class. The options are specified
using a list.signature(object = "clusterAlgorithmKmeans")
: run the
clustering. signature(object = "clusterAlgorithmKmeans")
: remove all
cluster data so that the execute
function can be run
again.signature(object = "clusterAlgorithmKmeans")
:
This function prints some basic information about the content of
this object.Gunnar Wrobel, http://www.gunnarwrobel.de.
clusterkmeans
,
goCluster-class
,
clusterModule-class
,
clusterAlgorithm-class
,
clusterAlgorithmKmeans-class
,
clusterAlgorithmClara-class
,
clusterAlgorithmPam-class
## Predefined setup for goCluster data(benomylsetup) ## Change the setup to ## hierarchical clustering benomylsetup$classalgo <- "clusterAlgorithmKmeans" benomylsetup$algo$repeats <- 10 ## 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)