moduleNetwork              package:nem              R Documentation

_I_n_f_e_r_s _a _p_h_e_n_o_t_y_p_i_c _h_i_e_r_a_r_c_h_y _u_s_i_n_g _t_h_e _m_o_d_u_l_e _n_e_t_w_o_r_k

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

     Function 'moduleNetwork' estimates the hierarchy using a divide
     and conquer approach. In each step only a subset of nodes (called
     module) is involved and no exhaustive enumeration of model space
     is needed as in function 'score'.

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

     moduleNetwork(D,type="mLL",Pe=NULL,Pm=NULL,lambda=0,delta=1,para=NULL,hyperpara=NULL,verbose=TRUE)

     ## S3 method for class 'ModuleNetwork':
     print(x,...)

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

       D: data matrix. Columns correspond to the nodes in the silencing
          scheme. Rows are phenotypes.

    type: see 'nem'

      Pe: prior position of effect reporters. Default: uniform over
          nodes in hierarchy

      Pm: prior on model graph (n x n matrix) with entries 0 <=
          priorPhi[i,j] <= 1 describing the probability of an edge
          between gene i and gene j.

  lambda: regularization parameter to incorporate prior assumptions.

   delta: regularization parameter for automated E-gene subset
          selection (CONTmLLRatio only)

    para: vector with parameters a and b for "mLL", if count matrices
          are used

hyperpara: vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"

 verbose: do you want to see progress statements printed or not?
          Default: TRUE

       x: nem object

     ...: other arguments to pass

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

     'moduleNetwork' is an alternative to exhaustive search  by the
     function 'score' and more accurate than 'pairwise.posterior' and
     'triples.posterior'.  It uses clustering to sucessively split the
     network into smaller modules, which can then be estimated
     completely. Connections between modules are estimated by
     performing a constraint greedy hillclimbing.

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

     nem object

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

     Holger Froehlich

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

[_1] Froehlich H, Fellmann M, Sueltmann H, Poustka A, Beissbarth T:
     Large Scale Statistical Inference of Signaling Pathways from RNAi
     and Microarray Data. BMC Bioinformatics, 2007.

[_2] Froehlich H, Fellmann M, Sueltmann H, Poustka A, Beissbarth T:
     Estimating Large Scale Signaling Networks through Nested Effects
     Models from Intervention Effects in Microarray Data.
     Bioinformatics, 1, 2008. 

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

     'score', 'nem'

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

        data("BoutrosRNAi2002") 
        res <- nem(BoutrosRNAiDiscrete[,9:16],para=c(.13,.05),inference="ModuleNetwork")
        
        # plot graph
        plot(res,what="graph")
        
        # plot posterior over effect positions
        plot(res,what="pos")
        
        # estimate of effect positions
        res$mappos
        

