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)