getGeneSimPrototypes          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 _o_f _g_e_n_e_s _w_i_t_h _r_e_s_p_e_c_t _t_o _a _f_e_a_t_u_r_e _v_e_c_t_o_r _r_e_p_r_e_s_e_n_t_a_t_i_o_n.

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

     Computes the pairwise functional similarities for a list of genes:
     Each gene is represented by a feature vector containing the gene's
     similarities to predefined prototype genes.

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

     getGeneSimPrototypes(genelist, prototypes = NULL, similarity = "max",
                          similarityTerm = "JiangConrath", pca = TRUE,
                          normalization = TRUE, verbose = TRUE) 

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

genelist: character vector of Entrez gene IDs 

prototypes: character vector of Entrez gene IDs representing the
          prototypes 

similarity: method to calculate the similarity to prototypes 

similarityTerm: method to compute the GO term similarity 

     pca: perform PCA on feature vectors to reduce dimensionality 

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

 verbose: print additional information

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

     The method calls 'getGeneFeaturesPrototypes' to calculate the
     feature vectors. The functional similarity between two genes is
     essentially given by the dot product between their feature
     vectors.

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

     List with items 

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

"prototypes": prototypes (= prinicipal components, if PCA has been
          performed)

"features": feature vectors for each gene: n x d data matrix

_N_o_t_e:

     The result depends on 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] 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 \newline [2] N. Speer, H.
     Froehlich, A. Zell, Functional Grouping of Genes Using Spectral
     Clustering and Gene Ontology, Proc. Int. Joint Conf. on Neural
     Networks (IJCNN), pp. 298 - 303, 2005

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

     'getGeneFeaturesPrototypes', 'selectPrototypes', 'getGeneSim',
     'getTermSim', 'setOntology'

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

      ## Not run: 
      may take some time ...
      proto=selectPrototypes(n=50) # --> returns a character vector of 50 genes with the highest number of annotations
      getGeneSimPrototypes(c("207","208","360"),prototypes=proto, similarityTerm="Resnik")
     ## End(Not run)

