| clusterAlgorithmHclust-class {goCluster} | R Documentation |
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.
The class provides a wrapper around the clusterhclust
function. Please read the corresponding documentation for further
details.
tree:"hclust", holds another
copy of the clustering result (the direct result from the call to
hclust). method:"character", defines
the agglomeration method that is going to be used for the
hierarchical clustering.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 = "clusterAlgorithmHclust"):
interactive setup of the class. You will be asked to specify the
distance matrix as well as the agglomeration method.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. signature(object = "clusterAlgorithmHclust"):
non-interactive setup of the class. The options are specified
using a list.signature(object = "clusterAlgorithmHclust"): run the
clustering.signature(object = "clusterAlgorithmHclust"): remove all
cluster data so that the execute function can be run
again.signature(object = "clusterAlgorithmHclust"):
This function prints some basic information about the content of
this object.Gunnar Wrobel, work@gunnarwrobel.de, http://www.gunnarwrobel.de.
clusterhclust,
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$data$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)