classifyKNNsc           package:maigesPack           R Documentation

_F_u_n_c_t_i_o_n _t_o _d_o _d_i_s_c_r_i_m_i_n_a_t_i_o_n _a_n_a_l_y_s_i_s, _b_y _t_h_e _s_e_a_r_c_h _a_n_d _c_h_o_o_s_e _m_e_t_h_o_d

_D_e_s_c_r_i_p_t_i_o_n:

     Function to search by groups of few genes, also called cliques,
     that can discriminate (or classify) between two distinct
     biological sample types, using the k nearest neighbours method.
     This function uses the search and choose method.

_U_s_a_g_e:

     classifyKNNsc(obj=NULL, sLabelID="Classification", func="wilcox.test",
                   facToClass=NULL, gNameID="GeneName", geneGrp=1, path=NULL,
                   nGenes=3, cliques=100, kn=5)

_A_r_g_u_m_e_n_t_s:

     obj: object of class 'maiges' to search the classifiers.

sLabelID: character string with the identification of the sample label
          to be used.

    func: string specifying the function to be used to search by the
          initial one-dimensional classifiers, like 'wilcox.test' or
          't.test'.

facToClass: named list with 2 character vectors specifying the samples
          to be compared. If NULL (default) the first 2 types of
          sLabelID are used.

 gNameID: character string with the identification of gene label ID.

 geneGrp: character or integer specifying the gene group to be tested
          ('colnames' of 'GeneGrps' slot). If both 'geneGrp' and 'path'
          are NULL all genes are used. Defaults to 1 (first group).

    path: character or integer specifying the gene network to be tested
          ('names' of 'Paths' slot). If both 'geneGrp' and 'path' are
          NULL all genes are used. Defaults to NULL.

  nGenes: integer specifying the number of genes in the clique, or
          classifier.

 cliques: integer specifying the number of cliques or classifiers to be
          generated.

      kn: number of neighbours for the _knn_ method.

_D_e_t_a_i_l_s:

     This function implements the method known as Search and choose
     proposed by Cristo (2003). If you want to use an exhaustive search
     use the function 'classifyKNN'.

     This function uses the function 'knn.cv' from package _class_ to
     construct k-nearest neighbour classifiers. It is possible to use
     the functions 'classifyLDAsc' and 'classifySVMsc' to search by
     classifiers using Fisher's linear discriminant analysis and
     support vector machinnes, respectively.

_V_a_l_u_e:

     The result of this function is an object of class 'maigesClass'.

_A_u_t_h_o_r(_s):

     Elier B. Cristo, adapted by Gustavo H. Esteves
     <gesteves@vision.ime.usp.br>

_R_e_f_e_r_e_n_c_e_s:

     Cristo, E.B. Metodos Estatisticos na Analise de Experimentos de
     Microarray. Masther's thesis, Instituto de Matematica e
     Estatistica - Universidade de Sao Paulo, 2003 (in portuguese).

_S_e_e _A_l_s_o:

     'knn.cv', 'classifyKNN', 'classifyLDAsc' and 'classifySVMsc'.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing KNN classifier with 2 genes for the 6th gene group comparing
     ## the 2 categories from 'Type' sample label.
     gastro.class = classifyKNNsc(gastro.summ, sLabelID="Type",
       gNameID="GeneName", nGenes=2, geneGrp=1, cliques=10)
     gastro.class

     ## To do classifier with 3 genes for the 6th gene group comparing
     ## normal vs adenocarcinomas from 'Tissue' sample label
     gastro.class = classifyKNNsc(gastro.summ, sLabelID="Tissue",
       gNameID="GeneName", nGenes=3, geneGrp=1, cliques=10,
       facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

