getSigRegionsCompKC         package:KCsmart         R Documentation

_K_C_s_m_a_r_t _C_o_m_p_a_r_a_t_i_v_e _c_a_l_c_u_l_a_t_e _t_h_e _s_i_g_n_f_i_c_a_n_t _r_e_g_i_o_n_s

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

     Extract the significant regions from a compKC object for a given
     false discovery rate (FDR).

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

     getSigRegionsCompKC(compKc, fdr=.01, maxRegionGap=10)

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

  compKc: A compKc object as created by the 'compareSpmCollection'
          function 

     fdr: The false discovery rate to be used to calculate the
          significantly different regions from the compKc object 

maxRegionGap: The maximum number of sample points that is allowed to
          fall under the threshold in a continuous significant region

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

     The false discovery rate that is set is used to determine the
     significant regions. When the compKc object was created by the
     siggenes method the corresponding cutoff is looked up in the
     siggenes results table, otherwise it is calculated from the
     permuted data. The maxRegionGap determines how many sample points
     can be under this threshold in a continuous significant region.

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

     Returns a compKcSigRegions object that contains the significant
     regions for the given FDR in the 'regionTable' slot. The method
     used to determine the cutoff, the fdr and the cutoff itself are
     stored in their corresponding slots. Use 'plot' to visualize the
     results.

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

     Jorma de Ronde

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

     'compareSpmCollection',  'getSigRegionsCompKC'

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

     data(hsSampleData)
     data(hsMirrorLocs)

     spmc1mb <- calcSpmCollection(hsSampleData, hsMirrorLocs, cl=c(rep(0,10),rep(1,10)))
     spmcc1mb <- compareSpmCollection(spmc1mb, nperms=3)
     spmcc1mbSigRegions <- getSigRegionsCompKC(spmcc1mb)

     plot(spmcc1mb, sigRegions=spmcc1mbSigRegions)

