findComplexes           package:apComplex           R Documentation

_E_s_t_i_m_a_t_e _a _P_r_o_t_e_i_n _C_o_m_p_l_e_x _M_e_m_b_e_r_s_h_i_p _G_r_a_p_h (_P_C_M_G) _u_s_i_n_g _p_r_o_t_e_i_n _c_o_m_p_l_e_x _c_o_m_e_m_b_e_r_s_h_i_p _d_a_t_a _f_r_o_m _A_P-_M_S _t_e_c_h_n_o_l_o_g_y

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

     Performs all steps in the local modeling algorithm described by
     Scholtens and Gentleman (2004) and Scholtens, Vidal, and Gentleman
     (submitted), beginning with an adjacency matrix recording bait-hit
     AP-MS data.

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

     findComplexes(adjMat, simMat = NULL, sensitivity = 0.75, specificity =
     0.995, Beta = 0, wsVal=NULL)

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

  adjMat: Adjacency matrix of bait-hit data from an AP-MS experiment. 
          Rows correspond to baits and columns to hits.

  simMat: An optional square matrix with entries between 0 and 1.  Rows
          and columns correspond to the proteins in the experiment, and
          should be reported in the same order as the columns of
          'adjMat'.  Higher values in this matrix are interpreted to
          mean higher similarity for protein pairs.

sensitivity: Believed sensitivity of AP-MS technology.

specificity: Believed specificity of AP-MS technology.

    Beta: Optional additional parameter for the weight to give data in
          'simMat' in the logistic regression model.

   wsVal: A numeric. This is the value assigned as the work-space in
          the call to fisher.test

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

     'findComplexes' performs all steps in the complex estimation
     algorithm using the apComplex package functions 'bhmaxSubgraph',
     'LCdelta', and 'mergeComplexes'.  These steps can also be
     performed separately by the user.

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

     An affiliation matrix representing the estimated protein complex
     memberships.

_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:

     'bhmaxSubgraph',code{LCdelta},'mergeComplexes'

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

     data(apEX)
     PCMG2 <- findComplexes(apEX,sensitivity=.7,specificity=.75)

