readCSV                 package:DFP                 R Documentation

_C_r_e_a_t_e_s _a_n _E_x_p_r_e_s_s_i_o_n_S_e_t _w_i_t_h _a_n _A_n_n_o_t_a_t_e_d_D_a_t_a_F_r_a_m_e _f_r_o_m _C_S_V _f_i_l_e_s

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

     This function creates an 'ExpressionSet' with an
     'AnnotatedDataFrame'. To do this, it requires two CSV files in a
     predefined format:

        1.  'exprsData' with the expression values of genes (in rows)
           of different samples (in columns).

        2.  'pData' with the samples (in columns) and the metadata
           class (the most important for the algorithm
           'discriminantFuzzyPattern'), age and sex.

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

     readCSV(fileExprs, filePhenodata)

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

fileExprs: The path to the 'exprsData' file. 

filePhenodata: The path to the 'pData' file. 

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

     An 'ExpressionSet' object with an 'AnnotatedDataFrame' storing
     class, age and sex information.

_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:

     dataDir <- system.file("extdata", package="DFP"); dataDir
     fileExprs <- file.path(dataDir, "exprsData.csv"); fileExprs
     filePhenodata <- file.path(dataDir, "pData.csv"); filePhenodata
     rmadataset <- readCSV(fileExprs, filePhenodata); rmadataset
     pData(phenoData(rmadataset))
     exprs(rmadataset)[1:10,1:5]

