getpvalue              package:GraphAT              R Documentation

_F_u_n_c_t_i_o_n _t_o _o_b_t_a_i_n _P _v_a_l_u_e_s _f_r_o_m _t_h_e _E_d_g_e _p_e_r_m_u_t_a_t_i_o_n _a_n_d _N_o_d_e
_p_e_r_m_u_t_a_t_i_o_n _t_e_s_t_s _r_e_s_p_e_c_t_i_v_e_l_y

_D_e_s_c_r_i_p_t_i_o_n:

     The function takes as inputs two adjacency matrices. Let X denote
     the observed number of  edges in common between the two adjacency
     matrices.  To test the significance of the correlation between the
     two data sources, the function performs N random edge permutations
     and random node permutations respectively. For each permutation
     test, the function outputs the proportion of N realizations that
     resulted in X edges or more at the intersection of the two
     datasources

_U_s_a_g_e:

     getpvalue(act.mat, nonact.mat, num.iterations = 1000)

_A_r_g_u_m_e_n_t_s:

 act.mat: Adjacency matrix corresponding to first data source. That is,
          the i,j element of this matrix is 1 if data source one
          specifies a functional link between genes i and j 

nonact.mat: Adjacency matrix corresponding to first data source. That
          is, the i,j element of this matrix is 1 if data source two
          specifies a functional link between genes i and j

num.iterations: Number of realizations from random edge (node)
          permutation to be obtained

_D_e_t_a_i_l_s:

     We note that the first adjacency matrix, denoted act.mat is the
     data source that is permutated with respect to edges or notes

_V_a_l_u_e:

     A vector of length 2, where the first element is the P value from
     Random Edge Permutation and the second element is the P value from
     Random Node Permutation

_A_u_t_h_o_r(_s):

     Raji Balasubramanian rbalasub@hsph.harvard.edu

_S_e_e _A_l_s_o:

     'permEdgesM2M', 'permNodesM2M', 'makeClustM'

_E_x_a_m_p_l_e_s:

     act.mat <- matrix(0,3,3)
     act.mat[2,1] <- 1 
     act.mat[3,1] <- 1
     nonact.mat  <- matrix(0,3,3)
     nonact.mat[2,1] <- 1
     nonact.mat[3,2] <- 1
     p.val <- getpvalue(act.mat, nonact.mat, num.iterations = 100)
     print(p.val)

