getTermSim               package:GOSim               R Documentation

_G_e_t _p_a_i_r_w_i_s_e _G_O _t_e_r_m _s_i_m_i_l_a_r_i_t_i_e_s.

_D_e_s_c_r_i_p_t_i_o_n:

     Returns the pairwise similarities between GO terms. Different
     calculation method are implemented.

_U_s_a_g_e:

     getTermSim(termlist, method = "JiangConrath", verbose = TRUE)

_A_r_g_u_m_e_n_t_s:

termlist: character vector of GO terms 

  method: one of
          ("JiangConrath","Resnik","Lin","CoutoEnriched","CoutoJiangConrath","CoutoResnik","CoutoLin") 

 verbose: print out various information or not 

_D_e_t_a_i_l_s:

     Currently the following methods for computing GO term similarities
     are implemented:  \begin{ldescription}

"_R_e_s_n_i_k" information content of minimum subsumer (ICms) [1]

"_J_i_a_n_g_C_o_n_r_a_t_h" 1 - min(1, IC(term1) - 2ICms + IC(term2)) [2]

"_L_i_n" frac{2ICms}{(IC(term1) + IC(term2))} [3]

"_C_o_u_t_o_E_n_r_i_c_h_e_d" FuSSiMeg enriched term similarity by Couto et al. [4].
     Requires enrichement factors to be set by 'setEnrichmentFactors'.

"_C_o_u_t_o_R_e_s_n_i_k" average information content of common disjunctive
     ancestors of 'term1' and 'term2' (ICshare) [5]

"_C_o_u_t_o_J_i_a_n_g_C_o_n_r_a_t_h" 1 - min(1, IC(term1) -2ICshare + IC(term2)) [5]

"_C_o_u_t_o_L_i_n" frac{2ICshare}{(IC(term1) + IC(term2))} [5]
     \end{ldescription}

_V_a_l_u_e:

     n x n matrix (n = number of GO terms) with similarities between GO
     terms scaled to [0,1]

_N_o_t_e:

     All calculations use normalized information contents for each GO
     term. Normalization is achieved by dividing each information
     content by the maximum information content within the currently
     set ontology ("BP","MF","CC")

_A_u_t_h_o_r(_s):

     Holger Froehlich

_R_e_f_e_r_e_n_c_e_s:

     [1] P. Resnik, Using Information Content to evaluate semantic
     similarity in a taxonomy, Proc. 14th Int. Conf. Artificial Intel.,
     1995 \newline [2] J. Jiang, D. Conrath, Semantic Similarity based
     on Corpus Statistics and Lexical Taxonomy, Proc. Int. Conf.
     Research in Comp. Ling., 1998 \newline [3] D. Lin, An
     Information-Theoretic Definition of Similarity, Proc. 15th Int.
     Conf. Machine Learning, 1998 \newline [4] F. Couto, M. Silva, P.
     Coutinho, Implementation of a Functional Semantic Similarity
     Measure between Gene-Products, DI/FCUL TR 03-29, Department of
     Informatics, University of Lisbon, 2003 \newline [5] Couto, F.;
     Silva, M. & Coutinho, P., Semantic Similarity over the Gene
     Ontology: Family Correlation and Selecting Disjunctive Ancestors,
     Conference in Information and Knowledge Management, 2005

_S_e_e _A_l_s_o:

     'getMinimumSubsumer', 'getDisjCommAnc', 'setEnrichmentFactors',
     'setOntology'

_E_x_a_m_p_l_e_s:

      # setOntology("BP")

      # Lin's method
      getTermSim(c("GO:0006955","GO:0007584"),method="Lin")
      # Couto's method combined with Jiang-Conrath distance
      getTermSim(c("GO:0006955","GO:0007584"),method="CoutoJiangConrath") 

      # set enrichment factors
      setEnrichmentFactors(alpha=0.1,beta=0.5) 
      getTermSim(c("GO:0006955","GO:0007584"),method="CoutoEnriched")

