RecoveryScore          package:GeneSelector          R Documentation

_S_t_a_b_i_l_i_t_y _m_e_a_s_u_r_e_s _f_o_r _g_e_n_e _r_a_n_k_i_n_g_s

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

     Computes the Recovery Score of Pavlidis et al. (s. reference)
     below. The stability meausre is the proportion of genes that are 
     declared significant (using usually multiple testing procedures) 
     in both the original and the perturbed dataset.

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

     RecoveryScore(RR, method = c("raw", "BH", "qvalue", "Bonferroni", "Holm", 
                   "Hochberg", "SidakSS", "SidakSD", "BY"), maxpval = 0.05)

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

      RR: An object of class 'RepeatRanking'.

  method: The p-value adjustment method, s. AdjustPvalues. Can also be
          '"raw"'(default), then no adjustment will be done.

 maxpval: The maximum p-value at which a gene is still considered
          significantly differentially expressed (after adjustment).

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

     A numeric vector of recovery scores for each perturbed dataset.

_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:

     Pavlidis, P., Li, Q., Noble, W.S. (2003). 
      The effect of replication on gene expression microarray
     experiments. _Bioinformatics, 19, 1620-1627_

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

     GetStabilityLm, GetStabilityOverlap

_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 ranking 
     ordT <- RankingTstat(xx, yy, type="unpaired")
     ### Generate Leave-One-Out
     loo <- GenerateFoldMatrix(xx, yy, k=1)
     ### Repeat Ranking with t-statistic
     loor_ordT <- GetRepeatRanking(ordT, loo)
     ### Compute Recovery Score
     rs_ordT <- RecoveryScore(loor_ordT, method="BH")

