clusterhclust           package:goCluster           R Documentation

_C_l_u_s_t_e_r_s _a _d_a_t_a_s_e_t _u_s_i_n_g _h_i_e_r_a_r_c_h_i_c_a_l _c_l_u_s_t_e_r_i_n_g

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

     This function is used by 'goCluster' to cluster a dataset with the
     'hclust' function.

_U_s_a_g_e:

     clusterhclust(
     dataset,
     method = "complete",
     distance = "euclidean"
     )

_A_r_g_u_m_e_n_t_s:

 dataset: The dataset to be clustered. This has to be a matrix. 

  method: The clustering method that is going to be used by 'hclust'. 

distance: The distance metric that is going to be used by 'hclust'. 

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

     Hierarchical clustering will generate a tree that defines gene
     subgroups based on similarity. Each branchpoint in the tree
     denotes the splitting of a larger gene group into two smaller
     groups. The function converts the hclust format into a tree of
     lists that is being returned to the user.

_V_a_l_u_e:

     The result represent the 'hclust'-tree as a list of lists. The
     terminal nodes of the tree are character vectors holding the
     unique ids of the genes in the cluster.

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

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

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

     'clusterAlgorithmHclust-class', 'hclust'

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

     ## Get the benomyl setup
     data(benomylsetup)

     ## Extract a fraction of the dataset
     benomyldata <- benomylsetup$data$dataset[1:200,]
     benomylids  <- benomylsetup$data$uniqueid[1:200]

     ## Cluster the dataset
     clusterhclust(exprs(benomyldata))

