clusterAlgorithmHclust-class {goCluster}R Documentation

Class "clusterAlgorithmHclust", provides hierarchical clustering for goCluster.

Description

This can be used to group an expression dataset according to hierarchical clustering. The resulting gene groups can subsequently be analysed for significant enrichment of specific annotations.

Details

The class provides a wrapper around the clusterhclust function. Please read the corresponding documentation for further details.

Slots

tree:
Object of class "hclust", holds another copy of the clustering result (the direct result from the call to hclust).
method:
Object of class "character", defines the agglomeration method that is going to be used for the hierarchical clustering.
distance:
Object of class "character", specifies the distance matrix that will be used.

Additional slots are described in the documentation of the clusterAlgorithm-class and clusterModule-class.

Extends

Class "clusterAlgorithm", directly. Class "clusterModule", by class "clusterAlgorithm".

Methods

config
signature(object = "clusterAlgorithmHclust"): interactive setup of the class. You will be asked to specify the distance matrix as well as the agglomeration method.
setup
signature(object = "clusterAlgorithmHclust"): returns the configuration of the object as a list. This list can again be used for the non-interactive setup of the class.
setup<-
signature(object = "clusterAlgorithmHclust"): non-interactive setup of the class. The options are specified using a list.
execute
signature(object = "clusterAlgorithmHclust"): run the clustering.
reset
signature(object = "clusterAlgorithmHclust"): remove all cluster data so that the execute function can be run again.
print
signature(object = "clusterAlgorithmHclust"): This function prints some basic information about the content of this object.

Author(s)

Gunnar Wrobel, http://www.gunnarwrobel.de.

See Also

clusterhclust, goCluster-class, clusterModule-class, clusterAlgorithm-class, clusterAlgorithmKmeans-class, clusterAlgorithmClara-class, clusterAlgorithmPam-class

Examples

## Predefined setup for goCluster
data(benomylsetup)

## Change the setup to
## hierarchical clustering
benomylsetup$data$dataset  <- benomylsetup$data$dataset[1:200,]
benomylsetup$data$uniqueid <- benomylsetup$data$uniqueid[1:200]
benomylsetup$classalgo <- "clusterAlgorithmHclust"
benomylsetup$algo$method   <- "complete"
benomylsetup$algo$distance <- "euclidean"

## 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)


[Package goCluster version 1.0.3 Index]