findOutliers.func            package:aCGH            R Documentation

_F_u_n_c_t_i_o_n _t_o _i_d_e_n_t_i_f_y _o_u_t_l_i_e_r _c_l_o_n_e_s

_D_e_s_c_r_i_p_t_i_o_n:

     The function identified the clones that are outliers.

_U_s_a_g_e:

     findOutliers.func(thres = madGenome, factor = 4, statesres = states.bic)

_A_r_g_u_m_e_n_t_s:

   thres: Estimate of experimental variability, generally, madGenome

  factor: Factor indicating how many standard

statesres: The states output of the 'hmm.run.func'

_D_e_t_a_i_l_s:

     The outliers are the clones that are dissimilar enough from the
     clones assigned to the same state. Magnitude of the factor
     determines how many MADs away from a median a value needs to be to
     be declared an outlier. Outliers consitent over many samples may
     indicate technological artificat with that clone or possibly copy
     number polymorpism.

_V_a_l_u_e:

 outlier: Binary matrix with a row for each clone and column for each
          sample. "1" indicates outlier, 0 otherwise.

pred.obs.out: Matrix with a row for each clone and column for each
          sample. The entries are the median value for the state with
          outliers exceluded for all clones but outliers. The value if
          the observed value for the outliers.

pred.out: Matrix with a row for each clone and column for each sample.
          The entries are the median value for the state 

_A_u_t_h_o_r(_s):

     Jane Fridlyand

_R_e_f_e_r_e_n_c_e_s:

     Application of Hidden Markov Models to the analysis of the array
     CGH  data, Fridlyand et.al., _JMVA_, 2004

_S_e_e _A_l_s_o:

     'aCGH'

