pumaClust                package:puma                R Documentation

_P_r_o_p_a_g_a_t_e _p_r_o_b_e-_l_e_v_e_l _u_n_c_e_r_t_a_i_n_t_y _i_n _m_o_d_e_l-_b_a_s_e_d _c_l_u_s_t_e_r_i_n_g _o_n _g_e_n_e _e_x_p_r_e_s_s_i_o_n _d_a_t_a

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

     This function clusters gene expression using a Gaussian mixture
     model including probe-level  measurement error. The inputs are
     gene expression levels and the probe-level standard deviation
     associated with expression measurement for each gene on each chip.
     The outputs  is the clustering results.

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

     pumaClust(e=NULL, se=NULL, efile=NULL, sefile=NULL, 
           subset=NULL, gsnorm=FALSE, clusters, 
           iter.max=100, nstart=10, eps=1.0e-6, del0=0.01)

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

       e: either a valid 'ExpressionSet' object, or a data frame
          containing the expression level for each gene on each chip. 

      se: data frame containing the standard deviation of gene
          expression levels. 

   efile: character, the name of the file which contains gene
          expression measurements. 

  sefile: character, the name of the file which contains the standard
          deviation of gene expression measurements. 

  subset: vector specifying the row number of genes which are clustered
          on.

  gsnorm: logical specifying whether do global scaling normalisation or
          not. 

clusters: integer, the number of clusters. 

iter.max: integer, the maximum number of iterations allowed in the
          parameter initialisation. 

  nstart: integer, the number of random sets chosen in the parameter
          initialisation. 

     eps: numeric, optimisation parameter. 

    del0: numeric, optimisation parameter. 

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

     The input data is specified either as an 'ExpressionSet' object
     (in which case se, efile and sefile will be ignored), or by e and
     se, or by efile and sefile.

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

     The result is a list with components

     cluster: vector, containing the membership of clusters for each
     gene; centers: matrix, the center of each cluster; centersigs:
     matrix, the center variance of each cluster; likelipergene:
     matrix, the likelihood of belonging to each cluster for each gene;
     bic: numeric, the Bayesian Information Criterion score.

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

     Xuejun Liu, Magnus Rattray

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

     Liu,X., Lin,K.K., Andersen,B., and Rattray,M. (2006)  Propagating
     probe-level uncertainty in model-based gene expression clustering,
      technical report available upon request.

     Liu,X., Milo,M., Lawrence,N.D. and Rattray,M. (2005) A tractable
     probabilistic model for  Affymetrix probe-level analysis across
     multiple chips, Bioinformatics, 21(18):3637-3644.

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

     Related method 'mmgmos'

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

       data(Clust.exampleE)
       data(Clust.exampleStd)
       pumaClust.example<-pumaClust(Clust.exampleE,Clust.exampleStd,clusters=7)

