RankingWilcEbam         package:GeneSelector         R Documentation

_R_a_n_k_i_n_g _b_a_s_e_d _o_n _t_h_e _e_m_p_i_r_i_c_a_l _b_a_y_e_s _a_p_p_r_o_a_c_h _o_f _E_f_r_o_n

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

     The function is a wrapper for the function 'wilc.ebam' from the
     package 'siggenes' that implements an empirical bayes mixture
     model approach in combination with the Wilcoxon statistic.
      For 'S4' method information, see RankingWilcEbam-methods.

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

     RankingWilcEbam(x, y, type = c("unpaired", "paired", "onesample"), gene.names = NULL, ...)

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

       x: A 'matrix' of gene expression values with rows corresponding
          to genes and columns corresponding to observations or
          alternatively an object of class 'ExpressionSet'
          alternatively an object of class 'ExpressionSet'.
           If 'type = paired', the first half of the columns
          corresponds to  the first measurements and the second half to
          the second ones.  For instance, if there are 10 observations,
          each measured twice, stored in an expression matrix 'expr', 
          then 'expr[,1]' is paired with 'expr[,11]', 'expr[,2]' with
          'expr[,12]', and so on.

       y: If 'x' is a matrix, then 'y' may be a 'numeric' vector or a
          factor with at most two levels.
           If 'x' is an 'ExpressionSet', then 'y' is a character
          specifying the phenotype variable in the output from 'pData'.
           If 'type = paired', take care that the coding is analogously
          to the requirement concerning 'x'


          "_u_n_p_a_i_r_e_d": two-sample test.

          "_p_a_i_r_e_d": paired test. Take care that the coding of 'y' is
               correct (s. above)

          "_o_n_e_s_a_m_p_l_e": 'y' has only one level.  Test whether the true
               mean is different from zero.


gene.names: An optional vector of gene names.

     ...: Further arguments to be passed to 'wilc.ebam', s. package
          'siggenes'.

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

     An object of class 'GeneRanking'.

_N_o_t_e:

     p-values are _not_ computed - the statistic is a posterior
     probabiliy.

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

     Martin Slawski martin.slawski@campus.lmu.de 
      Anne-Laure Boulesteix <URL: http://www.slcmsr.net/boulesteix>

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

     Efron, B., Tibshirani, R. (2002). 
      Empirical Bayes Methods and False Discovery Rates for Microarrays
     _Genetic Epidemiology, 23, 70-86_

     Schwender, H., Krause, A. and Ickstadt, K. (2003).
      Comparison of the Empirical Bayes and the Significance  Analysis
     of Microarrays.  _Techical Report, University of Dortmund._

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

     GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT,
     RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic, RankingLimma,
      RankingEbam,  RankingSam,  RankingBstat, RankingShrinkageT,
     RankingSoftthresholdT,  RankingPermutation, RankingGap

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

     ### Load toy gene expression data
     data(toydata)
     ### class labels
     yy <- toydata[1,]
     ### gene expression
     xx <- toydata[-1,]
     ### run RankingWilcEbam
     WilcEbam <- RankingWilcEbam(xx, yy, type="unpaired")

