DFP-package               package:DFP               R Documentation

_D_F_P _P_a_c_k_a_g_e _O_v_e_r_v_i_e_w

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

     This package provides a supervised technique able to identify
     differentially expressed genes,  based on the construction of
     _Fuzzy Patterns_ (FPs).  The _Fuzzy Patterns_ are built by means
     of applying 3 _Membership Functions_ to discretized gene
     expression values.

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


       Package:  DFP
       Type:     Package
       Version:  1.0
       Date:     2008-07-03
       License:  GPL-2

     The main functionality of the package is provided by the
     'discriminantFuzzyPattern'  function, which 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.

     Additional data classes: 'ExpressionSet', 'AnnotatedDataFrame'.

_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)

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

     #########################################
     ###### Different result displays ########
     #########################################
     plotMembershipFunctions(rmadataset, res$membership.functions, featureNames(rmadataset)[1:2])
     showDiscreteValues(res$discrete.values, featureNames(rmadataset)[1:10], c("healthy", "AML-inv"))
     showFuzzyPatterns(res$fuzzy.patterns, "healthy")[21:50]
     plotDiscriminantFuzzyPattern(res$discriminant.fuzzy.pattern)

