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 'exprSet'
     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 'exprSet' 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)

