createBeadSummaryData       package:beadarray       R Documentation

_P_r_o_d_u_c_e _b_e_a_d _a_v_e_r_a_g_e_s

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

     Produce bead averages for each bead type used in an experiment on
     a specified set of arrays.

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

     createBeadSummaryData(BLData, log=FALSE, n=3, ignoreList=NULL, arrays=seq(1:length(BLData$R[1,])), design = rep(1,ncol(BLData)))

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

  BLData: an BeadLevelList object containing bead level data

     log: if TRUE then use the log2 intensities of each bead when
          removing outliers

       n: use n median absolute deviations from the mean as a cut-off
          for outliers

ignoreList: list of ProbeIDs to be omitted from the averaging
          procedure. These could be Illumina internal controls which
          are replicated many thousands of times on arrays

  arrays: numeric vector giving the indices of the arrays we want to
          average. Default is all arrays in the BeadLevelList

  design: Used to combine arrays of the same sample, but with different
          probes on each array.  Each value in design represents an
          array and arrays with the same value are row binded together

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

     'arrays' specifies which arrays in the BeadLevelList object we
     want to average. The default is to average all arrays in the
     BeadLevelList object but we might only want to average the  half
     of the arrays to save time. Objects which are created seperately
     by 'createBeadSummaryData' may be joined by using the 'cbind'
     function. 

     To average each bead type we first have to remove the outliers for
     the bead type on the array. The default method is the method used
     by Illumina which uses the raw (un-logged) intensities and
     categorises outliers as being those beads  which have intensity
     more than 3 median absolute deviations from the bead mean
     intensity. By setting the log parameter we can use the log2
     intensities instead of un-logged and by setting the 'n' parameter
     we choose beads with intensity 'n' mads from the mean.

     With outliers removed, we simply take an average of the red and
     green intensities of  the remaining beads and create $R and $G
     matrices. We also store the number of outliers that were detected
     for each bead type and the standard deviation of the  remaining
     beads in the '$nooutliers' and '$beadstdev' columns respectively.

     As the function runs it prints out the index of the array that is
     currently being processed.

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

     An BeadSummaryList (bead summary) object in which all components
     (R, Rb, G, ....) are matrices with number of rows equal to the
     number of bead types for the experiment and number of columns
     equal to the length of the 'arrays' object.

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

     Mark Dunning and Mike Smith

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

     'findBeadStatus'

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

     ##produce bead summaries for the first array in our BLData object

     #data(BLData)
     #BSData = createBeadSummaryData(BLData)
     #names(BSData)

