findSigLevelTrad           package:KCsmart           R Documentation

_F_i_n_d _s_i_g_n_i_f_i_c_a_n_c_e _l_e_v_e_l

_D_e_s_c_r_i_p_t_i_o_n:

     Method to find the cutoff at which gains and losses are considered
     significant using permutations

_U_s_a_g_e:

     findSigLevelTrad(data, observedSpm, n = 1, p = 0.05, maxmem = 1000)

_A_r_g_u_m_e_n_t_s:

    data: aCGH data in the same format as used for 'calcSpm' 

observedSpm: A sample point matrix as produced by 'calcSpm'

       n: Number of permutations 

       p: Alpha level for significance 

  maxmem: This parameter controls memory usage, set to lower value to
          lower memory consumption 

_D_e_t_a_i_l_s:

     The number of permutations needed for reliable results depends on
     the data and can not be determined beforehand. As a general
     rule-of-thumb around 100 permutations should be used for 'quick
     checks' and around 2000 permutations for more rigorous testing.

     p is the uncorrected alpha level, the method corrects for multiple
     testing internally using simple Bonferroni correction. See the
     referenced publication for more details.

_V_a_l_u_e:

     A list with the cutoffs corresponding to the given alpha level

    pos : The cutoff for the gains

    neg : The cutoff for the losses'

_A_u_t_h_o_r(_s):

     Jorma de Ronde

_S_e_e _A_l_s_o:

     'plotScaleSpace'

_E_x_a_m_p_l_e_s:

     data(hsSampleData)
     data(hsMirrorLocs)

     spm1mb <- calcSpm(hsSampleData, hsMirrorLocs)

     sigLevel1mb <- findSigLevelTrad(hsSampleData, spm1mb, n=3)

     plot(spm1mb, sigLevels=sigLevel1mb)
     plotScaleSpace(list(spm1mb), list(sigLevel1mb), type='g')

