BASHDiffuse            package:beadarray            R Documentation

_B_A_S_H - _D_i_f_f_u_s_e _D_e_f_e_c_t _A_n_a_l_y_s_i_s

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

     Creates a list of probes marked as being in diffuse defects.

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

     BASHDiffuse(BLData, array, neighbours = NULL, E = NULL, n = 3, compact = NULL, sig = 0.0001, invasions = 10, cutoff = 8, cinvasions = 10, twotail = FALSE)

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

  BLData: 'BeadLevelList'

   array: integer specifying which strip/array to plot

neighbours: A Neighbours matrix. Optional - if left NULL, it will be
          computed, using default 'generateNeighbours' settings.

       E: Numerical vector - The error image to use. Optional - if left
          blank, it will be computed, using 'generateE' using 'bgfilter
          = "median"'.

       n: Specify a cut-off for outliers as n median absolute
          deviations (MADs) from the median. The default value is 3

 compact: Vector - Optional. BeadIDs of beads in compact defects to
          remove from the analysis.

     sig: Numerical - Significance level of binomial test.

invasions: Integer - Number of invasions to use to find the kernel (see
          below).

  cutoff: Integer - Size a cluster must be to be labelled a diffuse
          defect.

cinvasions: Integer - Number of invasions used when closing the image.

 twotail: Logical - If TRUE, then we analyse positive and negative
          outliers separately, and then combine the diffuse defect
          images at the end.

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

     'BASHDiffuse' finds "diffuse defects" on an array. A diffuse
     defect is defined as a region containing an unusually large number
     of (not necessarily connected) outliers.

     Firstly, we consider the error image 'E', and find outlier beads
     on this image. Outliers for a particular bead type are determined
     using a 3 MAD cut-off from the median.

     We now consider an area around each bead (known as the "kernel").
     The kernel is found by an invasion process using the neighbours
     matrix - we choose the beads which can be reached from the central
     bead in 'cinvasions' steps.

     We count how many beads are in the kernel, and how many of these
     are marked as outliers. Using a binomial test, we work out if
     there are significantly more outliers in the kernel than would be
     expected if the outliers were equally distributed over the entire
     array. If so, then the central bead is marked as a diffuse defect.

     Lastly, we run a clustering algorithm and a closing algorithm
     similar to those in 'BASHCompact'.

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

     A vector consisting of the BeadIDs of beads considered diffuse
     defects.

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

     Jonathan Cairns

_R_e_f_e_r_e_n_c_e_s:

     Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkwosky and
     Marcelo O. Magnasco (2007). Harshlight: A "corrective make-up"
     program for microarray chips. R package version 1.8.0.
     http://asterion.rockefeller.edu/Harshlight/

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

     'BASHCompact', 'generateE', 'generateNeighbours',

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

     data(BLData)
     o <- BASHDiffuse(BLData, 1)
     o <- BASHDiffuse(BLData, 1, sig = 0.00001) ##stricter significance value, perhaps more useful on a BeadChip.
     o <- BASHDiffuse(BLData, 1, cutoff = 12) ##only larger defects will be found with this setting

