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,control=set.default.parameters(unique(colnames(D)), para=c(.13,.05)))
        # 
        sccg <- SCCgraph(res$graph,name=TRUE)
        #
        par(mfrow=c(1,2))
        plot.nem(res, main="inferred from data")
        plot(sccg$graph, main="condensed (rel,key)")

