| clusterAlgorithmPam-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 clusterpam
function. Please read the corresponding documentation for further
details.
clusters:"numeric", determines
the number of clusters partitioning around medoids will
identify.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 = "clusterAlgorithmPam"):
interactive setup of the class. You will be asked to specify the
number of clusters PAM clustering should result in. In addition
the distance matrix needs to be defined.signature(object = "clusterAlgorithmPam"):
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 = "clusterAlgorithmPam"):
non-interactive setup of the class. The options are specified
using a list. signature(object = "clusterAlgorithmPam"): run the
clustering. signature(object = "clusterAlgorithmPam"): remove all
cluster data so that the execute function can be run again.signature(object = "clusterAlgorithmPam"):
This function prints some basic information about the content of
this object.Gunnar Wrobel, http://www.gunnarwrobel.de.
clusterpam,
goCluster-class,
clusterModule-class,
clusterAlgorithm-class,
clusterAlgorithmKmeans-class,
clusterAlgorithmClara-class,
clusterAlgorithmHclust-class,
.
## Predefined setup for goCluster
## (This configurations selects the
## clara clustering)
data(benomylsetup)
## Change the setup to
## pam clustering
benomylsetup$data$dataset <- benomylsetup$data$dataset[1:200,]
benomylsetup$data$uniqueid <- benomylsetup$data$uniqueid[1:200]
benomylsetup$classalgo <- "clusterAlgorithmPam"
## 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)