RankingLimma          package:GeneSelector          R Documentation

_R_a_n_k_i_n_g _b_a_s_e_d _o_n _t_h_e '_m_o_d_e_r_a_t_e_d' _t _s_t_a_t_i_s_t_i_c

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

     The 'moderated' t statistic is based on a bayesian hierarchical
     model which is estimated by an empirical bayes approach (Smyth et
     al,2003). The function is a wrapper to the function 'fitLm' and
     'eBayes' of the 'limma' package.
      For 'S4' method information, see RankingLimma-methods.

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

     RankingLimma(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 'eBayes', for
          instance the prior probability for differential expression.
          Consult the help of the 'limma' package for details

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

     An object of class GeneRanking.

_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:

     Smyth, G. K., Yang, Y.-H., Speed, T. P. (2003).
       Statistical issues in microarray data analysis.  _Methods in
     Molecular Biology 2:24, 111-136_.

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

     GetRepeatRanking, RankingTstat, RankingFC, RankingWelchT,
     RankingWilcoxon, RankingBaldiLong, RankingFoxDimmic,  RankingEbam,
     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 RankingLimma
     limma <- RankingLimma(xx, yy, type="unpaired")

