classifyKNN            package:maigesPack            R Documentation

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_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 neighbourhood method.
     This function uses exhaustive search.

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

     classifyKNN(obj=NULL, sLabelID="Classification", facToClass=NULL,
                 gNameID="GeneName", geneGrp=1, path=NULL, nGenes=3, 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.

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.

      kn: number of neighbours for the _knn_ method.

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

     Pay attention with the arguments 'geneGrp' and 'path', if both of
     them is NULL an exhaustive search for all dataset will be done,
     and this search may be extremely computational intensive, which
     may result in a process during some weeks or months depending on
     the number of genes in your dataset.

     If you want to construct classifiers from a group of several
     genes, the _search and choose_ (SC) method may be an interesting
     option. It is implemented in the function 'classifyKNNsc'. This
     function uses the function 'knn.cv' from package _class_ to
     construct k-nearest neighbour classifiers. It possible to use
     functions 'classifyLDA' or 'classifySVM' to construct classifiers
     using Fisher's linear discriminant analysis or support vector
     machines methods, 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>

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

     'knn.cv', 'classifyKNNsc', 'classifyLDA', 'classifySVM'.

_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 = classifyKNN(gastro.summ, sLabelID="Type",
       gNameID="GeneName", nGenes=2, geneGrp=6)
     gastro.class

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

