classifySVM            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

_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 Support Vector Machinne method.
     This function uses exhaustive search.

_U_s_a_g_e:

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

_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.

_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 running 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 'classifySVMsc'. This
     method uses the function 'svm' from package _e1071_ to search
     classifiers by Support Vector Machines. The functions
     'classifyLDA' and 'classifyKNN' were also dedined to construct
     classifiers by Fisher's linear discriminant analysis ans
     k-neighbours, 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:

     'svm', 'classifySVMsc', 'classifyLDA' and 'classifyKNN'.

_E_x_a_m_p_l_e_s:

     ## Loading the dataset
     data(gastro)

     ## Doing SVM classifier with 2 genes for the 6th gene group comparing
     ## the 2 categories from 'Type' sample label.
     gastro.class = classifySVM(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 = classifySVM(gastro.summ, sLabelID="Tissue",
       gNameID="GeneName", nGenes=3, geneGrp=6,
       facToClass=list(Norm=c("Neso","Nest"), Ade=c("Aeso","Aest")))

