RankingFC            package:GeneSelector            R Documentation

_R_a_n_k_i_n_g _b_a_s_e_d _o_n _t_h_e (_l_o_g) _f_o_l_d_c_h_a_n_g_e

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

     Naive ranking that only considers difference in means without
     taking variances into account.
      For 'S4' method information, see RankingFC-methods.

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

     RankingFC(x, y, type = c("unpaired", "paired", "onesample"), 
               pvalues = TRUE, gene.names = NULL, LOG = FALSE, ...)

_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 ? Defaults to 'TRUE'.

gene.names: An optional vector of gene names.

     LOG: By default, the data are assumed to be already logarithm-ed. 
          If not, this can be done by setting 'LOG=TRUE'

     ...: Currently unused argument.

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

     An object of class GeneRanking

_N_o_t_e:

     Take care that the _log_ foldchange is computed, therefore
     logarithmization might be necessary.
      The p-values for the difference in means are based on a standard
     normal assumption.

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

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

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

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

