RankingSam           package:GeneSelector           R Documentation

_R_a_n_k_i_n_g _b_a_s_e_d _o_n _t_h_e _S_A_M _s_t_a_t_i_s_t_i_c

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

     A wrapper function to the 'samr' package.
      For 'S4' method information, see RankingSam-methods.

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

     RankingSam(x, y, type = c("unpaired", "paired", "onesample"), pvalues = TRUE, 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.


 pvalues: Should p-values be computed ? Default is 'TRUE'.

gene.names: An optional vector of gene names.

     ...: Further arguments to be passed to 'samr'. Consult the help of
          the 'samr' package for details.

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

     An object of class GeneRanking.

_N_o_t_e:

     The computing is relatively high, due to the fact that permutation
     statistics are generated.

_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:

     Tusher, V.G., Tibshirani, R., and Chu, G. (2001).
       Significance analysis of microarrays applied to the ionizing
     radiation  response. _PNAS, 98, 5116-5121._

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

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

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

