getGeneSim               package:GOSim               R Documentation

_C_o_m_p_u_t_e _f_u_n_c_t_i_o_n_a_l _s_i_m_i_l_a_r_i_t_y _f_o_r _g_e_n_e_s.

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

     Calculate the pairwise functional similarities for a list of genes
     using different strategies.

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

     getGeneSim(genelist, similarity = "OA", similarityTerm = "JiangConrath", normalization = TRUE, verbose = TRUE)

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

genelist: character vector of Entrez gene IDs 

similarity: method to calculate the functional similarity between gene
          products 

similarityTerm: method to compute the similarity of GO terms 

normalization: normalize the similarities to [0,1] by transforming
          sim(x,y) <- sim(x,y)/sqrt(sim(x,x)*sim(y,y)) 

 verbose: print out some information 

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

     The method to calculate the pairwise functional similarity between
     gene products can either be:  \begin{ldescription}

"_m_a_x" the maximum similarity between any two GO terms

"_m_e_a_n" the average similarity between any two GO terms

"_O_A" the optimal assignment (maximally weighted bipartite matching) of
     GO terms associated to the gene having fewer annotation to the GO
     terms of the other gene. \end{ldescription}

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

     n x n similarity matrix (n = number of genes)

_N_o_t_e:

     The result depends on the currently set ontology.

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

     Holger Froehlich

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

     H. Froehlich, N. Speer, C. Spieth, and A. Zell, Kernel Based
     Functional Gene Grouping, Proc. Int. Joint Conf. on Neural
     Networks (IJCNN), 6886 - 6891, 2006

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

     'getGeneSimPrototypes', 'getTermSim', 'setOntology'

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

             # see evaluateClustering

