| 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 = "clusterModule"):
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 = "clusterModule"):
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 = "clusterModule"):
non-interactive setup of the class. The options are specified
using a list.signature(object = "clusterModule"): run the
clustering. signature(object = "clusterModule"): remove all
cluster data so that the execute function can be run
again.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)