shortestPath             package:GOstats             R Documentation

_S_h_o_r_t_e_s_t _P_a_t_h _A_n_a_l_y_s_i_s

_D_e_s_c_r_i_p_t_i_o_n:

     The shortest path analysis was proposed by Zhou et. al. The basic
     computation is to find the shortest path in a supplied graph
     between two Entrez Gene IDs. Zhou et al claim that other genes
     annotated along that path are likely to have the same GO
     annotation as the two end points.

_U_s_a_g_e:

     shortestPath(g, GOnode)

_A_r_g_u_m_e_n_t_s:

       g: An instance of the 'graph' class. 

  GOnode: A length one character vector specifying the GO node of
          interest. 

_D_e_t_a_i_l_s:

     The algorithm implemented here is quite simple. All Entrez Gene 
     identifiers that are annotated at the GO node of interest are
     obtained. Those that are found as nodes in the graph are retained
     and used for the computation. For every pair of nodes at the GO
     term the shortest path between them is computed using 'sp.between'
     from the RBGL package.

     There is a presumption that the graph is 'undirected'. This
     restriction could probably be lifted if there was some reason for
     it - a patch would be gratefully accepted.

_V_a_l_u_e:

     The return values is a list with the following components: 

shortestpaths : A list of the ouput from 'sp.between'. The names are
          the names of the nodes used as the two endpoints

nodesUsed : A vector of the Entrez Gene IDs that were both found at the
          GO term of interest and were nodes in the supplied graph,
          'g'. These were used to compute the shortest paths.

nodesNotUsed: A vector of Entrez Gene IDs that were annotated at the GO
          term, but were not found in the graph 'g'.

_A_u_t_h_o_r(_s):

     R. Gentleman

_R_e_f_e_r_e_n_c_e_s:

     Transitive functional annotation by shortest-path analysis of gene
     expression data, by X. Zhou and M-C J. Kao and W. H. Wong, PNAS,
     2002

_S_e_e _A_l_s_o:

     'sp.between'

_E_x_a_m_p_l_e_s:

     library(GO)
     library(RBGL)

     tst <- unique(unlist(mget(c("GO:0005778"), GOENTREZID)))
     set.seed(123)
     v1 <- randomGraph(tst, 1:10, .3, weights=FALSE)
     ## Since v1 is random, it might be disconnected and we need a
     ## connected graph to guarantee the existence of a path.
     c1 <- connComp(v1)
     largestComp <- c1[[which.max(sapply(c1, length))]]
     v2 <- subGraph(largestComp, v1)

     a1 <- shortestPath(v2, "GO:0005779")

