minet                 package:minet                 R Documentation

_M_u_t_u_a_l _I_n_f_o_r_m_a_t_i_o_n _N_e_t_w_o_r_k

_D_e_s_c_r_i_p_t_i_o_n:

     For a given dataset, 'minet' infers the network in two steps. 
     First, the mutual information between all pairs of variables in
     'dataset' is computed according to the 'estimator' argument. Then
     the algorithm given by 'method'  considers the estimated mutual
     informations in order to build the network.

_U_s_a_g_e:

     minet(dataset, method="mrnet", estimator="mi.empirical", 
           disc="equalfreq", nbins=sqrt(nrow(dataset)) )

_A_r_g_u_m_e_n_t_s:

 dataset: data.frame where columns contain variables/features and rows
          contain outcomes/samples.

  method: The name of the inference algorithm : "clr", "aracne" or
          "mrnet" (default: "mrnet") - see references.

estimator: The name of the mutual information estimator :
          "mi.empirical", "mi.mm", "mi.shrink" or "mi.sg"(default:
          "mi.empirical") - see 'build.mim'.

    disc: The name of the discretization method to be used :
          "equalfreq" or "equalwidth" (default: "equalfreq") - see
          'discretize'.

   nbins: Integer giving the number of bins to be used in the
          discretization. 

_V_a_l_u_e:

     'minet' returns a matrix which is the weighted adjacency matrix of
     the network. The weights range from 0 to 1 and can be seen as a
     confidence measure on the presence of the arcs.  In order to
     display the network, load the package Rgraphviz and use the
     following command:  
      plot( as( returned.matrix ,"graphNEL") )

_A_u_t_h_o_r(_s):

     Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi

_R_e_f_e_r_e_n_c_e_s:

     Patrick E. Meyer, Kevin Kontos, Frederic Lafitte, and Gianluca
     Bontempi.  Information-theoretic inference of large
     transcriptional regulatory networks. EURASIP Journal on
     Bioinformatics and Systems Biology, 2007.    

     Adam A. Margolin, Ilya Nemenman, Katia Basso, Chris Wiggins,
     Gustavo Stolovitzky, Riccardo Dalla Favera, and Andrea Califano.
     Aracne : An  algorithm for the reconstruction of gene regulatory
     networks in a mammalian cellular context. BMC Bioinformatics,
     2006.

     Jeremiah J. Faith, Boris Hayete, Joshua T. Thaden, Ilaria Mogno,
     Jamey Wierzbowski, Guillaume Cottarel, Simon Kasif, James J.
     Collins, and  Timothy S. Gardner. Large-scale mapping and
     validation of escherichia coli transcriptional regulation from a
     compendium of expression profiles. PLoS Biology, 2007.

_S_e_e _A_l_s_o:

     'build.mim',  'clr',  'mrnet',  'aracne'

_E_x_a_m_p_l_e_s:

     data(syn.data)
     net1 <- minet( syn.data )
     net2 <- minet( syn.data, estimator="mi.shrink" )
     net3 <- minet( syn.data, method="clr", estimator="mi.sg" )

