GeneSelector          package:GeneSelector          R Documentation

_E_x_c_l_u_d_e _g_e_n_e_s _f_r_o_m _b_e_i_n_g _c_a_n_d_i_d_a_t_e_s _f_o_r _d_i_f_f_e_r_e_n_t_i_a_l _e_x_p_r_e_s_s_i_o_n

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

     'GeneRankings' and 'AggregatedRankings' from several statistics
     are unified. According to a user-defined or adaptively determined
     threshold via multiple testing procedures, all genes are checked
     whether they fall below this threshold _consistenly_ in all
     statistics used. If  this criterion is not met, then the gene is
     selected.
      A final order of the genes is defined by the following criteria

     _1. A user-defined ranking of the used statistics, i.e. the user
          decides which statistic is most important

     _2. 'Selection', i.e. falling below the threshold yes/no

     _3. The obtained ranks. The rank from the most important statistic
          is considered, then that from the second most important, and
          so on. 

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

     GeneSelector(Rlist, ind = NULL, indstatistic = 1:length(Rlist), 
                 threshold = c("user", "BH", "qvalue", "Bonferroni", "Holm", 
                 "Hochberg", "SidakSS", "SidakSD", "BY"), 
                 maxrank = NULL, maxpval = 0.05)

_A_r_g_u_m_e_n_t_s:

   Rlist: A list of objects of class 'RepeatedRanking' or
          'AggregatedRanking', all based on the same data.

     ind: Indices of genes to be considered. Defaults to all.

indstatistic: An index vector defining the importance of the elements
          of 'Rlist' (typically this is the importance of the used
          statistics). For instance, if 'RList' consists of five
          elements, then  'indstatistic=c(2,4,1,3,5)' would give most
          importance to the second statistic.

threshold: How the threshold is determined. Can be either '"user"'
          (then the threshold is specified via 'maxrank') or a multiple
          testing procedure (s. AdjustPvalues).  In this case, the
          p-values of that element of  'Rlist' attributed most
          importance (s. 'indtstatistic') are adjusted and the number
          of p-values falling below 'maxpval' is used as threshold
          rank. If the most important statistic provides no p-values,
          then the ones of the second most are used (if available), and
          so on.

 maxrank: Specified if 'threshold="user"'. A positive integer that is
          regarded as threshold rank.

 maxpval: Specified if 'threshold' is _not_ 'user'

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

     An object of class CombinedRanking.

_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:

     GeneRanking, AggregatedRanking

_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,]
     ### Get Rankings from five different statistics
     ordinaryT <- RankingTstat(xx, yy, type="unpaired")
     baldilongT <- RankingBaldiLong(xx, yy, type="unpaired")
     samT <- RankingSam(xx, yy, type="unpaired")
     wilc <- RankingWilcoxon(xx, yy, type="unpaired")
     wilcebam <- RankingWilcEbam(xx, yy, type="unpaired")
     ### form a list
     LL <- list(ordinaryT, baldilongT, samT, wilc, wilcebam)
     ### order statistics (assign importance)
     ordstat <-  c(3,4,2,1,5)
     ### start GeneSelector, threshold set to rank 50
     gk50 <- GeneSelector(LL, indstatistic=ordstat, maxrank=50)
     ### start GeneSelector, using adaptive threshold based on p-values,
     ### here using the multiple testing procedure of Hochberg
     gkpval <- GeneSelector(LL, indstatistic=ordstat, threshold = "BH", maxpval=0.05)
     ### show results
     show(gkpval)
     str(gkpval)
     toplist(gkpval)
     ### which genes have been selected ?
     SelectedGenes(gkpval)
     ### relative distance plot
     plot(gkpval, top=5)
     ### Detailed information about gene 4
     GeneInfoScreen(gkpval, which=4)

