aracne                 package:minet                 R Documentation

_A_l_g_o_r_i_t_h_m _f_o_r _t_h_e _R_e_c_o_n_s_t_r_u_c_t_i_o_n _o_f _A_c_c_u_r_a_t_e _C_e_l_l_u_l_a_r _N_E_t_w_o_r_k_s

_D_e_s_c_r_i_p_t_i_o_n:

     This function takes the mutual information matrix as input in
     order  to return the infered network according to the Aracne
     algorithm. This algorithm applies the data processing inequality
     to  all triplets of nodes in order to remove the least significant
      edge in each triplet.

_U_s_a_g_e:

     aracne( mim, eps=0 )

_A_r_g_u_m_e_n_t_s:

     mim: A square matrix whose i,j th element is the mutual
          information   between variables X_i and X_j - see
          'build.mim'.

     eps: Numeric value indicating the threshold used when removing an
          edge : for each triplet of nodes (i,j,k), the  weakest edge,
          say (ij), is removed if its weight is below
          min{(ik),(jk)}-eps - see references. 

_D_e_t_a_i_l_s:

     The Aracne procedure starts by assigning to each pair of nodes a
     weight equal to their mutual information. Then,  the weakest edge
     of each triplet is interpreted as an indirect interaction and is
     removed if the difference between the two lowest weights is above
     a threshold 'eps'.

_V_a_l_u_e:

     'aracne' returns a matrix which is the weighted adjacency matrix
     of the network. In order to display the network, load the package
     Rgraphviz and use the following command: 
      plot( as( returned.matrix ,"graphNEL") )

_R_e_f_e_r_e_n_c_e_s:

     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.

_S_e_e _A_l_s_o:

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

_E_x_a_m_p_l_e_s:

     data(syn.data)
     mim <- build.mim(discretize(syn.data))
     net <- aracne(mim)

