nem.calcSignificance           package:nem           R Documentation

_S_t_a_t_i_s_t_i_c_a_l _s_i_g_n_i_f_i_c_a_n_c_e _o_f _n_e_t_w_o_r_k _h_y_p_o_t_h_e_s_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     Assess statistical significance of a network hypothesis by
     comparing it to a null hypothesis.

_U_s_a_g_e:

             nem.calcSignificance(D, x, N=1000, seed=1)

_A_r_g_u_m_e_n_t_s:

       D: data matrix with experiments in the columns (binary or
          continious)

       x: nem object

       N: number of random networks to sample

    seed: random seed

_D_e_t_a_i_l_s:

     Given data, N random network hypotheses from a null distribution
     are drawn as follows:  For each S-gene $S_k$ we randomly choose a
     number o of outgoing edges between 0 and 3. We then select o
     S-genes having at most 1 ingoing edge, connected  $S_k$ to them
     and transitively closed the graph.  For all random network
     hypotheses it is counted, how often their likelihood is bigger
     than that of the given network. This yields an exact p-value.

     Another way of assessing the statistical significance of the
     network hypothesis is to draw random permutations of the node
     labels. Note that in this case the node degree distribution is the
     same as in the given network. Again, we can obtain an exact
     p-value by counting, how often the likelihood of the permuted
     network is bigger than that of the given network.

     Finally, comparison to randomly perturbed networks (insertion or
     deletion of 1 edge) yields an exact p-value describing the
     stability of the network.

_V_a_l_u_e:

p.value.rnd: p-value of the network according to the null hypothesis
          that it is random

p.value.perm: p-value of the network according to the null hypothesis
          that a network with permuted node labels is at least as good

p.value.mod: p-value of the network according to the null hypothesis a
          randomly peturbed network is at least as good

_A_u_t_h_o_r(_s):

     Holger Froehlich

_S_e_e _A_l_s_o:

     'nem.consensus', 'nem.jackknife', 'nem.bootstrap', 'nem'

_E_x_a_m_p_l_e_s:

     ## Not run: 
        data("BoutrosRNAi2002")
        D <- BoutrosRNAiDiscrete[,9:16]   
        res = nem(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05))) # get best network
        nem.calcSignificance(D,res) # assess its statistical significance
     ## End(Not run)

