selectStatsBonferroni       package:goCluster       R Documentation

_P_e_r_f_o_r_m_s _B_o_n_f_e_r_r_o_n_i _c_o_r_r_e_c_t_i_o_n _a_n_d _s_u_b_s_e_q_u_e_n_t_l_y _s_e_l_e_c_t_s _r_e_l_e_v_a_n_t
_a_n_n_o_t_a_t_i_o_n _t_e_r_m_s.

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

     This function corrects the p-values for the annotation terms in
     the given tree by taking the total number of elements in the tree
     into account. It removes all annotation terms with a p-value
     higher than the given threshold after the values have been
     corrected.

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

     selectStatsBonferroni(X, threshold, tests)

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

       X: The tree of annotation terms as retrieved by the statistical
          analysis function (e.g. evalClusterHyper)

threshold: The selection threshold for the p-values. 

   tests: The number of annotation terms tested. 

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

     The function will walk through all nodes of the tree specified as
     X and remove all annotation terms with a p-value lower than the
     given threshold divided by the number of tests (Bonferroni
     correction). The resulting tree will have the same structure as
     before but it can contain nodes that are empty.

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

     A reduced tree with annotation elements having a significant
     p-value. This type of multiple testing correction assumes that the
     annotation terms are independant and should thus not be used for
     annotation data sets as for exmple the gene ontology.

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

     Gunnar Wrobel, <URL: work@gunnarwrobel.de>, <URL:
     http://www.gunnarwrobel.de>.

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

     'clusterSignifBonferroni-class'

_E_x_a_m_p_l_e_s:

     set.seed(1000)

     data(benomylsetupsmall)

     a <- new("goCluster")

     execute(a) <- benomylsetupsmall

     ## The statistics are saved in the "statset" slot of
     ## the "stat" object that is itself a child of the "sign"
     ## object of a "goCluster" object
     ## We select all items with a p-value below 0.05 after
     ## Bonferroni correction. 
     selectStatsBonferroni(a@sign@stat@statset,0.05,length(unlist(a@sign@stat@statset)))

