discriminantFuzzyPattern         package:DFP         R Documentation

_D_i_s_c_r_i_m_i_n_a_n_t _F_u_z_z_y _P_a_t_t_e_r_n _t_o _f_i_l_t_e_r _g_e_n_e_s

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

     _discriminantFuzzyPattern_ discovers significant genes based on
     the construction of _Fuzzy Patterns_ (FPs).  The _Fuzzy Patterns_
     are built by means of applying 3 _Membership Functions_ to the
     gene expression values in the matrix _rmadataset_.

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

     discriminantFuzzyPattern(rmadataset, skipFactor = 3, zeta = 0.5, overlapping = 2, piVal = 0.9)

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

rmadataset: 'ExpressionSet' with numeric values containing gene
          expression values (rows) of samples belonging to different
          categories (columns).
            The 'ExpressionSet' also contains an 'AnnotatedDataFrame'
          with metadata regarding the classes to which each sample
          belongs.

skipFactor: Numeric value to omit odd values (a way of normalization).
            Higher values imply that less samples of a gene are
          considered as odd. If 'skipFactor=0' do *NOT* skip.
           'Default value = 3'. 'Range[0,)'. 

    zeta: Threshold value which controls the activation of a linguistic
          label ('Low', 'Medium' or 'High').
            The lower, the less posibilities of having genes with more
          than one assigned linguistic label.
           'Default value = 0.5'. 'Range[0,1]'. 

overlapping: Modifies the number of membership functions used in the
          discretization process.
            Possible values: 

             1.  Low, Medium, High.

             2.  Low, Low-Medium, Medium, Medium-High, High.

             3.  Low, Low-Medium, Low-Medium-High, Medium,
                Medium-High, High.

          'Default value = 2'. 

   piVal: Controls the degree of exigency for selecting a gene as a
          member of a _Fuzzy Pattern_.
           'Default value = 0.9'. 'Range[0,1]'. 

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

     The 'discriminantFuzzyPattern' function works in a 4-step process:

        1.  Calculates the _Membership Functions_. These functions are
           used in the next step to discretize gene expression data.

        2.  Discretizes the gene expression data (float values) into
           Low, Medium or High labels.

        3.  Calculates a _Fuzzy Pattern_ for each category. To do this,
           a given percentage of the samples belonging to a category
           must have the same label (Low, Medium or High).

        4.  Calculates the _Discriminant Fuzzy Pattern_ (DFP) that
           includes those genes present in two or more FPs with
           different assigned labels.

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

membership.functions: _Membership functions_ to determine the discret
          value corresponding to a given gene expression level. 

discrete.values: Discrete values according to the overlapping parameter
          after discretizing the gene expression values.
            Includes an attribute types which determines the category
          of each sample. 

fuzzy.patterns: Genes belonging to each _Fuzzy Patterns_. There are one
          FP for each class.
           Includes an attribute ifs with the _Impact Factor_ for each
          category.  

discriminant.fuzzy.pattern: Genes belonging to the final DFP.
            Includes an attribute ifs with the _Impact Factor_ for each
          category. 

  params: The parameters used to tune the algorithm (as arguments in
          the function). 

_A_u_t_h_o_r(_s):

     Rodrigo Alvarez-Gonzalez
      Daniel Glez-Pena
      Fernando Diaz
      Florentino Fdez-Riverola
      Maintainer: Rodrigo Alvarez-Gonzalez <rodrigo.djv@uvigo.es>

_R_e_f_e_r_e_n_c_e_s:

     F. Diaz; F. Fdez-Riverola; D. Glez-Pena; J.M. Corchado. Using
     Fuzzy Patterns for Gene Selection and Data Reduction on Microarray
     Data. 7th International Conference on Intelligent Data Engineering
     and Automated Learning: IDEAL 2006, (2006) pp. 1095-1102

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

     #########################################
     ############ Get sample data ############
     #########################################
     library(DFP)
     data(rmadataset)

     #########################################
     # Filters the most representative genes #
     #########################################
     res <- discriminantFuzzyPattern(rmadataset)
     summary(res)

