getGeneFeaturesPrototypes       package:GOSim       R Documentation

_G_e_t _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 _o_f _g_e_n_e_s.

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

     Computes the feature vectors for list of genes: Each gene is
     represented by its similarities to predefined prototype genes.

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

     getGeneFeaturesPrototypes(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: scale the feature vectors to norm 1

 verbose: print out additional information 

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

     If no prototypes are passed, the method calls the
     'selectPrototypes' function with no arguments. Hence, the
     prototypes in this case are the 250 genes with most known
     annotations.

     The PCA postprocessing determines the principal components that
     can explain at least 95% of the total variance in the feature
     space. 

     The method to calculate the functional similarity of a gene to a
     certain prototype can either be \begin{ldescription}

"_m_a_x" the maximum 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:

     List with items 

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

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

_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:

     'getGeneSimPrototypes', 'selectPrototypes', 'getGeneSim',
     'getTermSim', 'setOntology'

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

             # see selectPrototypes

