score                  package:nem                  R Documentation

_C_o_m_p_u_t_e_s _t_h_e _m_a_r_g_i_n_a_l _l_i_k_e_l_i_h_o_o_d _o_f _p_h_e_n_o_t_y_p_i_c _h_i_e_r_a_r_c_h_i_e_s

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

     Function to compute the marginal likelihood of a set of phenotypic
     hierarchies.

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

     score(models, D, type="mLL", para=NULL, hyperpara=NULL, Pe=NULL, Pm=NULL, lambda=0, delta=1, verbose=TRUE, graphClass="graphNEL")

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

     PhiDistr(Phi, Pm, a=1, b=ncol(Phi)^2)

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

  models: a list of adjacency matrices with unit main diagonal 

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

    type: (1.) marginal likelihood "mLL" (only for cout matrix D), or
          (2.) full marginal likelihood "FULLmLL" integrated over a and
          b and depending on hyperparameters a0, a1, b0, b1 (only for
          count matrix D), or (3.) "CONTmLL" marginal likelihood for
          probability matrices, or (4.) "CONTmLLDens" marginal
          likelihood for probability log-density matrices, or (5.)
          "CONTmLLRatio" for log-odds ratio matrices

    para: Vector with parameters 'a' and 'b' (for "mLL" with count
          data)

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

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

      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)

 verbose: output while running or not

graphClass: output inferred graph either as graphNEL or matrix

       x: nem object

     ...: other arguments to pass

     Phi: adjacency matrix

       a: parameter of the inverse gamma prior for v=1/lambda

       b: parameter of the inverse gamma prior for v=1/lambda

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

     Scoring models by marginal log-likelihood is implemented in
     function 'score'. Input consists of models and data, the type of
     the score ('"mLL"', '"FULLmLL"', '"CONTmLL"' or '"CONTmLLDens"' or
     '"CONTmLLRatio"'), the corresponding paramters ('para') or
     hyperparameters ('hyperpara'), a prior for phenotype positions
     ('Pe') and model structures 'Pm' with regularization parameter
     'lambda'. If a structure prior 'Pm' is provided, but no
     regularization parameter 'lambda', Bayesian model averaging with
     an inverse gamma prior on 1/lambda is performed.  With type
     "CONTmLLRatio" usually an automated selection of most relevant
     E-genes is performed by introducing a "null" S-gene. The
     corresponding prior probability of leaving out an E-gene is set to
     delta/no. S-genes. 

     'score' is usually called within function 'nem'.

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

   graph: the inferred directed graph (graphNEL object)

     mLL: marginal likelihood of final model

     pos: posterior over effect positions

  mappos: MAP estimate of effect positions

    type: as used in function call

    para: as used in function call

hyperpara: as used in function call

  lambda: as in function call

selected: selected E-gene subset

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

     Holger Froehlich, Florian Markowetz <URL:
     http://genomics.princeton.edu/~florian>

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

[_1] Markowetz F, Bloch J, Spang R, Non-transcriptional pathway features
     reconstructed from secondary effects of RNA interference,
     Bioinformatics, 2005.

[_2] Markowetz F, Probabilistic Models for Gene Silencing Data, PhD
     thesis, Free University Berlin, 2006.

[_3] 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. Proc. German
     Conf. Bioinformatics (GCB), pp. 45 - 54, 2007.

[_4] Froehlich H, Fellmann M, Sueltmann H, Poustka A, Beissbarth T:
     Large Scale Statistical Inference of Signaling Pathways from RNAi
     and Microarray Data, BMC Bioinformatics, 8:386, 2007.

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

     'nem', 'mLL', 'FULLmLL', 'enumerate.models'

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

        # Drosophila RNAi and Microarray Data from Boutros et al, 2002
        data("BoutrosRNAi2002")
        D <- BoutrosRNAiDiscrete[,9:16]

        # enumerate all possible models for 4 genes
        models <- enumerate.models(unique(colnames(D)))

        # score models with marginal likelihood
        result <- score(models,D,type="mLL",para=c(.13,.05))
        
        # plot graph
        plot(result,what="graph")

        # plot scores
        plot(result,what="mLL") 
         
        # plot posterior of E-gene positions
        plot(result,what="pos")
        
        # MAP estimate of effect positions
        result$mappos[[which.max(result$mLL)]]

