clusterComp           package:clusterStab           R Documentation

_E_s_t_i_m_a_t_e _M_i_c_r_o_a_r_r_a_y _C_l_u_s_t_e_r _S_t_a_b_i_l_i_t_y

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

     This function estimates the stability of clustering solutions
     using microarray data. Currently only agglomerative hierarchical
     clustering is supported.

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

     ## S4 method for signature 'exprSet':
     clusterComp(object, cl, seednum = NULL, B = 100,
     sub.frac = 0.8, method = "ave", adj.score = FALSE)
     ## S4 method for signature 'matrix':
     clusterComp(object, cl, seednum = NULL, B = 100,
     sub.frac = 0.8, method = "ave", adj.score = FALSE)

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

  object: Either a matrix or 'exprSet' 

      cl: The number of clusters. This may be estimated using 'benhur'

 seednum: A value to pass to 'set.seed', which will allow for exact
          reproducibility at a later date.

       B: The number of permutations.

sub.frac: The proportion of genes to use in each subsample. This value
          should be in the range of 0.75 - 0.85 for best results

  method: The linkage method to pass to 'hclust'.  Valid values include
          "average", "centroid", "ward", "single", "mcquitty", or
          "median".

adj.score: Boolean. Should the stability scores be adjusted for cluster
          size? Defaults to 'FALSE'.

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

     This function estimates the stability of a clustering solution by
     repeatedly subsampling the data and comparing the cluster
     membership of the subsamples to the original clusters.

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

     The output from this function is an object of class 'clusterComp'.
     See the 'clusterComp-class' man page for more information.

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

     James W. MacDonald <jmacdon@med.umich.edu>

_R_e_f_e_r_e_n_c_e_s:

     A. Ben-Hur, A. Elisseeff and I. Guyon. A stability based method
     for discovering structure in clustered data. Pacific Symposium on
     Biocomputing, 2002. Smolkin, M. and Ghosh, D. (2003).  Cluster
     stability scores for microarray data in cancer studies . BMC
     Bioinformatics 4, 36 - 42.

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

     data(sample.exprSet)
     clusterComp(sample.exprSet, 3)

