compCorrGraph            package:GOstats            R Documentation

_A _f_u_n_c_t_i_o_n _t_o _c_o_m_p_u_t_e _a _c_o_r_r_e_l_a_t_i_o_n _b_a_s_e_d _g_r_a_p_h _f_r_o_m _G_e_n_e
_E_x_p_r_e_s_s_i_o_n _D_a_t_a

_D_e_s_c_r_i_p_t_i_o_n:

     Given a set of gene expression data (an instance of the
     'ExpressionSet' class) this function computes a graph based on
     correlations between the probes.

_U_s_a_g_e:

     compCorrGraph(eSet, k = 1, tau = 0.6)

_A_r_g_u_m_e_n_t_s:

    eSet: An instance of the 'ExpressionSet' class. 

       k: The power to raise the correlations to. 

     tau: The lower cutoff for absolute correlations. 

_D_e_t_a_i_l_s:

     Zhou et al. describe a method of computing a graph between probes
     (genes) based on estimated correlations between probes. This
     function implements some of their methods.

     Pearson correlations between probes are computed and then these
     are raised to the power 'k'. Any of the resulting estimates that
     are less than 'tau' in absolute value are set to zero.

_V_a_l_u_e:

     An instance of the 'graph' class. With edges and edge weights
     determined by applying the algorithm described previously.

_A_u_t_h_o_r(_s):

     R. Gentleman

_R_e_f_e_r_e_n_c_e_s:

     Zhou et al., Transitive functional annotation by shortest-path
     analysis of gene expression data.

_S_e_e _A_l_s_o:

     'compGdist'

_E_x_a_m_p_l_e_s:

      ## Create an ExpressionSet to work with
      set.seed(123)
      exprMat <- matrix(runif(50 * 5), nrow=50)
      genData <- new("ExpressionSet", exprs=exprMat)

      corrG = compCorrGraph(genData)

