clusterAlgorithmHclust-class    package:goCluster    R Documentation

_C_l_a_s_s "_c_l_u_s_t_e_r_A_l_g_o_r_i_t_h_m_H_c_l_u_s_t", _p_r_o_v_i_d_e_s _h_i_e_r_a_r_c_h_i_c_a_l _c_l_u_s_t_e_r_i_n_g
_f_o_r _g_o_C_l_u_s_t_e_r.

_D_e_s_c_r_i_p_t_i_o_n:

     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.

_D_e_t_a_i_l_s:

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

_S_l_o_t_s:

     '_t_r_e_e': Object of class '"hclust"', holds another copy of the
          clustering result (the direct result from the call to
          hclust). 

     '_m_e_t_h_o_d': Object of class '"character"', defines the agglomeration
          method that is going to be used for the hierarchical
          clustering.

     '_d_i_s_t_a_n_c_e': 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'.

_E_x_t_e_n_d_s:

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

_M_e_t_h_o_d_s:

     _c_o_n_f_i_g 'signature(object = "clusterModule")': interactive setup of
          the class. You will be asked to specify the distance matrix
          as well as the agglomeration method.

     _s_e_t_u_p '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. 

     _s_e_t_u_p<- 'signature(object = "clusterModule")': non-interactive
          setup of the class. The options are specified using a list.

     _e_x_e_c_u_t_e 'signature(object = "clusterModule")': run the clustering.

     _r_e_s_e_t 'signature(object = "clusterModule")': remove all cluster
          data so that the 'execute' function can be run again.

_A_u_t_h_o_r(_s):

     Gunnar Wrobel, <URL: http://www.gunnarwrobel.de>.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     ## 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)

