clusterpam             package:goCluster             R Documentation

_C_l_u_s_t_e_r_s _a _d_a_t_a_s_e_t _w_i_t_h _t_h_e _p_a_m _f_u_n_c_t_i_o_n

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

     This function is used in the goCluster framework to cluster a
     dataset with the pam function.

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

     clusterpam(dataset, clusters, distance = "euclidian")

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

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

clusters: This specifies the number of clusters that the dataset should
          be partitioned into. 

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

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

     PAM clustering will partition the dataset of the parent object
     into the number of clusters specified by the user. This list of
     groups can subsequently be analyzed by statistical means for any
     enrichment of functional categories. PAM is considered to be more
     stable than K-means clustering and offers better techniques for
     validating the results e.g. the stability of clusters. It yields a
     deterministic outcome, but might take very long on large datasets
     (see 'clara').

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

     A "tree" (list of lists) of clusters. The first level will hold as
     many list elements as the number of times the clustering has been
     repeated. Each of these elements holds a number of lists equal to
     the number of clusters requested. Each of node on this second
     level hold the unique ids of the genes in the cluster.

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

     require(cluster)

     ## 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
     clusterpam(exprs(benomyldata), 4)

