RankingEbam           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 approach of Efron and colleagues is based on a mixture model
     for subpopulations: genes that are differentially expressed and
     those that are not. The posterior probability for differential
     expression serves as statistic. The function described below is
     merely a wrapper for the function 'z.ebam' from the package
     'siggenes'.
      For 'S4' method information, see RankingEbam-methods.

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

     RankingEbam(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'.
           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 passed to the function 'z.ebam'. Can be
          used to influence the _fudge factor_ to the stabilize the
          variance. Currently, the 90 percent quantile is used.

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

     To find a better value for the fudge factor, the function 
     'find.a0' (package 'siggenes') can be used.

_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., Storey, J.D., Tusher, V. (2001).
       Empirical Bayes Analysis of a Microarray Experiment,  _JASA, 96,
     1151-1160_.

     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,
      RankingWilcEbam, 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 RankingEbam
     Ebam <- RankingEbam(xx, yy, type="unpaired")

