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)

