bhmaxSubgraph           package:apComplex           R Documentation

_F_i_n_d _m_a_x_i_m_a_l _B_H-_c_o_m_p_l_e_t_e _s_u_b_g_r_a_p_h

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

     Given an adjacency matrix of bait-hit AP-MS protein data, this
     function finds the maximal BH-complete subgraphs and reports them
     as an affiliation matrix.

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

     bhmaxSubgraph(adjMat,unrecip=1)

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

  adjMat: 'adjMat' is an N by (N+M) adjacency matrix with N equal to
          the number of bait proteins and M equal to the number of
          hit-only proteins.  'adjMat' should have row and column names
          corresponding to the proteins in the experiment.  An entry of
          "1" in the ith row and jth column of 'adjMat' corresponds to
          bait protein i finding protein j as a hit.  All other entries
          should be 0. 

 unrecip: By default set to 1 so that unreciprocated bait-bait edges
          are treated as present.  If set to 0, unreciprocated
          bait-bait edges will be treated as absent.

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

     A BH-complete subgraph with n bait nodes and m hit-only nodes for
     AP-MS data is defined as a subgraph for which all n*(n-1)+nm
     directed edges exist.  A maximal BH-complete subgraph is a
     BH-complete subgraph which is not contained in any other
     BH-complete subgraph.

     This function first forms a symmetric matrix out of the first N
     columns of 'adjMat'.  By default, unreciprocated bait-bait
     observations will be treated as present.  If 'unrecip' is set to
     0, they will be treated as absent.  If the sensitivity of the
     AP-MS technology is believed to be less than the specificity, then
     it is suggested that 'unrecip'=1.

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

     A matrix with (N+M) rows and a number of columns equal to the
     number of maximal BH-complete subgraphs.  An entry of "1" in the
     ith row and jth column indicates membership of the ith protein in
     the jth maximal BH-complete subgraph.

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

     Denise Scholtens

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

     Scholtens D and Gentleman R.  Making sense of high-throughput
     protein-protein interaction data.  Statistical Applications in
     Genetics and Molecular Biology 3, Article 39 (2004).

     Scholtens D, Vidal M, and Gentleman R.  Local modeling of global
     interactome networks.  Bioinformatics 21, 3548-3557 (2005).

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

     'mergeComplexes','findComplexes'

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

     data(apEX)
     PCMG0 <- bhmaxSubgraph(apEX)
     PCMG1 <- mergeComplexes(PCMG0,apEX,sensitivity=.7,specificity=.75)

