SCCgraph                 package:nem                 R Documentation

_C_o_m_b_i_n_e_s _S_t_r_o_n_g_l_y _C_o_n_n_e_c_t_e_d _C_o_m_p_o_n_e_n_t_s _i_n_t_o _s_i_n_g_l_e _n_o_d_e_s

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

     'SCCgraph' is used to identify all nodes which are not
     distinguishable given the data.

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

     SCCgraph(x,name=TRUE,nlength=20)

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

       x: graphNEL object or an adjacency matrix

    name: Concatenate all names of summarized nodes, if TRUE, or number
          nodes, if FALSE. Default: TRUE

 nlength: maximum length of names

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

     A graph inferred by either 'nem' or 'nemModelSelection' may have
     cycles if some phenotypic profiles are not distinguishable. The
     function 'SCCgraph' identifies cycles in the graph (the strongly
     conneced components) and summarizes them in a single node. The
     resulting graph is then acyclic.

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

   graph: a graphNEL object with connected components of the input
          graph summarized into single nodes

     scc: a list mapping SCCs to nodes

which.scc: a vector mapping nodes to SCCs

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

     Florian Markowetz <URL: http://genomics.princeton.edu/~florian>,
     Holger Froehlich <URL: http://www.dkfz.de/mga2/people/froehlich>

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

     'nem', 'transitive.reduction'

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

        data("BoutrosRNAi2002")
        D   <- BoutrosRNAiDiscrete[,9:16]
        res <- nem(D,para=c(.13,.05))
        # 
        sccg <- SCCgraph(res$graph,name=TRUE)
        #
        par(mfrow=c(1,2))
        plot(res, main="inferred from data")
        plot(sccg$graph, main="condensed (rel,key)")

