findSigLevelFdr           package:KCsmart           R Documentation

_T_h_i_s _f_u_n_c_t_i_o_n _h_a_s _n_o_t _b_e_e_n _p_r_o_p_e_r_l_y _i_m_p_l_e_m_e_n_t_e_d _y_e_t

_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:

     findSigLevelFdr(data, observedSpm, n = 1, fdrTarget=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 

fdrTarget: Target False Discovery Rate (FDR) 

  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. 
     The FDR method is less conservatie than the p-value based approach
     since instead of controlling the family wise error rate (FWER,
     P(false positive > 1)) it controls the false discovery rate (FDR)
     (false positives / total number of called data points).

_V_a_l_u_e:

     A list with the cutoffs corresponding to the given FDR

    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')

