graphTheory             package:PCpheno             R Documentation

_G_r_a_p_h _t_h_e_o_r_y _t_o _t_e_s_t _a_s_s_o_c_i_a_t_i_o_n_s _b_e_t_w_e_e_n _t_w_o _o_r _m_o_r_e _r_e_l_a_t_i_o_n_s_h_i_p_s

_D_e_s_c_r_i_p_t_i_o_n:

     Graph theory approach associated with a permutation test to
     evaluate whether the number of associations is unexpectedly large.

_U_s_a_g_e:

     graphTheory(genename, interactome, perm)

_A_r_g_u_m_e_n_t_s:

genename: A vector a gene names that are associated with a particular
          phenotype

interactome: A binary matrix composed of genes (rows) and biological
          complexes (columns) (see package _ScISI_)

    perm: Numeric, number of permutation run

_D_e_t_a_i_l_s:

     We form two distinct graphs where the set of nodes are the
     proteins (genes) in the organism. In one graph we create edges
     between genes if the two genes are members of one, or more,
     protein complexes. In the second graph we create an edge between
     all genes that are associated to a particular phenotype. We then
     construct a third graph on the same node set, but where there is
     an edge in this graph only if there is an edge in both of the
     first to graphs. We count the number of edges in the third and
     test by permutation whether the number of edges is unexpectedly
     large.

_V_a_l_u_e:

     The returned value is a list with components: 

edgeCount: Number of associations observed between the genes that are
          linked to a particular phenotype and the given interactome.

edgeSimul: Number of associations if the genes that are linked to a
          particular phenotype are randomly distributed across the
          given interactome

 p.value: Returned p.value

_A_u_t_h_o_r(_s):

     R. Gentleman and N. LeMeur

_R_e_f_e_r_e_n_c_e_s:

     Balasubramanian, R., LaFramboise, T., Scholtens, D., Gentleman, R.
     (2004) A graph-theoretic approach to testing associations between
     disparate sources of functional genomics
     data.Bioinformatics,20(18),3353-3362.

_E_x_a_m_p_l_e_s:

     data(ScISI)
     data(essglist)
     ans <- graphTheory(names(essglist), ScISI, perm=3)

