infer.edge.type             package:nem             R Documentation

_I_n_f_e_r _r_e_g_u_l_a_t_i_o_n _d_i_r_e_c_t_i_o_n _f_o_r _e_a_c_h _e_d_g_e

_D_e_s_c_r_i_p_t_i_o_n:

     The method infers edge types (up-regulation, down-regulation) for
     a given nem model. For an edge a->b the method looks at the
     fraction of E-genes attached to b (including b itself), which are
     up- or down-regulated in a knock-down of a. If significantly more
     genes are down-regulated than up-regulated, the edge a->b is
     assumed to be an activation. Likewise, if significantly more genes
     are up-regulated than down-regulated, a->b is assumed to be an
     inhibition. If there is no significant difference in up- and
     down-regulated edges, a->b does not have a specified type.

_U_s_a_g_e:

     infer.edge.type(x, logFC, alpha=0.05, adj.method="BY")

_A_r_g_u_m_e_n_t_s:

       x: nem object

   logFC: matrix with fold changes. The rownames of this matrix should
          correspond to the rownames of the data matrix, which was used
          to infer the nem model.

   alpha: p-value cutoff

adj.method: multiple testing correction method. Default:
          Benjamini-Yekutieli

_D_e_t_a_i_l_s:

     Significance is calculated using a two-tailed binomial test with
     null hypothesis p=0.5.

_V_a_l_u_e:

     Modified nem object. Each edge in the nem graph now has a "weight"
     and a "label" attribute. The label attribute corresponds to the
     original value in the adjacency matrix. The weight attribute
     encodes up- and down-regulation in the following way: value 2
     means up-regulation, value -1 down-regulation and value 1
     corresponds to an unknown regulation type.

_A_u_t_h_o_r(_s):

     Holger Froehlich

_S_e_e _A_l_s_o:

     'binom.test'

_E_x_a_m_p_l_e_s:

             data("BoutrosRNAi2002") 
             D <- BoutrosRNAiDiscrete[,9:16]         
             result = nem(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05)))
             resEdgeInf = infer.edge.type(result, BoutrosRNAiLogFC)
             plot.nem(resEdgeInf)

