RankingBaldiLong        package:GeneSelector        R Documentation

_R_a_n_k_i_n_g _b_a_s_e_d _o_n _t_h_e _t-_s_t_a_t_i_s_t_i_c _o_f _B_a_l_d_i _a_n_d _L_o_n_g

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

     Performs bayesian t tests on a gene expression matrix.
      For 'S4' method information, see RankingBaldiLong-methods.

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

     RankingBaldiLong(x, y, type = c("unpaired", "paired", "onesample"), 
                       m = 100, conf = NULL, 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.


       m: Size of the sliding window that is used obtain the background
          variance from pooled similarly expressed genes. s. Details.

    conf: The number of 'pseudocounts' giving weight to the prior
          variance. s. Details.

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

gene.names: An optional vector of gene names.

     ...: Currently unused argument.

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

     The argument 'm' determines the width of the window used to
     provides an estimate of the average variability of  gene
     expression for those genes that show a similar expression level.
      The argument 'conf' is non-negative and indicates the weight give
     to the Bayesian prior estimate of within-treatment  variance.
     Baldi and Long report reasonable performance with this parameter
     set equal to approximately 3 times the number of  observations,
     when the number of experimental observations is small
     (approximately 4 or less).  If the number of replicate
     experimental observations is large then the confidence value can
     be lowered  to be equal to the number of observations (or even
     less).

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

     An object of class GeneRanking.

_N_o_t_e:

     Results can differ slighlty from the Cyber-T-Software of Baldi and
     Long.

_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:

     Baldi,P., Long, A.D. (2001). 
      A bayesian framework for the analysis of microarray data.
     _Bioinformatics, 17, 509-519_

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

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

