findAllOutliers          package:beadarray          R Documentation

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

     Function to find all beads which are outliers for their particular
     bead type on a given strip/array using Illumina's  standard
     outlier detection method.

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

     findAllOutliers(BLData, array, log=FALSE, n=3, what="G", usewts=FALSE)

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

  BLData: 'BeadLevelList'

   array: integer specifying which strip/array we want to find outliers
          on

     log: if TRUE the intensities will be calculated on the log2 scale.
          Otherwise un-logged data is used.

       n: numeric value defining a cut-off for the number of  median
          absolute deviations (MADs) from the median to use for 
          determining outliers.  The default value is 3.

    what: character string specifying which intensities to use. See
          'getArrayData' for a list of possibilities.

  usewts: if TRUE, then beads with weights below 1 will be discarded
          prior to analysis.

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

     We find the outliers for each bead type on the array in turn using
     the 'findBeadStatus' function and store the indices of the
     outliers found. By default, outliers for a particular bead type
     are determined using a 3 MAD cut-off from the median.

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

     numeric vector giving the row indices of 'BLData' (in the range 1
     to total number of beads on the array) of all beads  that are
     outliers for their bead type.

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

     Mark Dunning

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

     'findBeadStatus'

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

     data(BLData)
     # how many outliers are there on the original scale?
     length(findAllOutliers(BLData, 1))
     # how many outliers are there on the log2-scale?
     length(findAllOutliers(BLData, 1, log=TRUE)) #
     # how many outliers are there using a 4 MAD 
     # cut-off from the median?
     length(findAllOutliers(BLData, 1, n=4))

