DiffScore             package:beadarray             R Documentation

_I_l_l_u_m_i_n_a'_s _D_E _s_t_a_t_i_s_t_i_c

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

     Function to compute the DiffScore statistic to assess the
     probability of differential expression of genes between a
     reference and condition array.

     NB At present this function does not compute the same values as
     output by BeadStudio

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

     DiffScore(BSData, QC=NULL, cond, ref)

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

  BSData: ExpressionSetIllumina object representing bead summary data

      QC: quality control information, usually created automatically by
          the readBeadSummaryData function

    cond: index for the condition array

     ref: index for the reference array

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

     The model assumes that bead variation is comprised or nonspecific
     biological variation and technical error. Technical error is
     estimated using a robust least squares fit of the standard error
     and mean for beads on the reference and condition arrays
     respectively. Biological  variation is estimated using the average
     intensity of the negative controls on the reference and condition
     arrays (found in the QC object). A p-value for the statistic is
     computed by dividing the absolute difference in expression on
     reference and condition arrays by the sum of the technical and
     biological varation on  condition and reference arrays. The
     DiffScore is then

     $10*sign(mu_cond - mu_ref)log_10(p)$

     Note that this statistic is intended to be used on unlogged
     intensities. For log$_2$ data, the lmFit function from limma can
     be easily used on the exprs matrix.

     For more details see the BeadStudio manual and "A model of
     Technical Variation of Microarray Signal" - Chudin et al. Journal
     of Comp Bio. Vol 13, 4 (2006)

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

     vector of DiffScore values, one for each gene in the BSData
     object.

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

     Mark Dunning

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

     'lmFit'

