bg.adjust.affinities          package:gcrma          R Documentation

_B_a_c_k_g_r_o_u_n_d _a_d_j_u_s_t_m_e_n_t _w_i_t_h _s_e_q_u_e_n_c_e _i_n_f_o_r_m_a_t_i_o_n (_i_n_t_e_r_n_a_l _f_u_n_c_t_i_o_n)

_D_e_s_c_r_i_p_t_i_o_n:

     An internal function to be used by 'gcrma'.

_U_s_a_g_e:

     bg.adjust.fullmodel(pms,mms,ncs=NULL,apm,amm,anc=NULL,index.affinities,k=k,rho=.7,fast)
     bg.adjust.affinities(pms,ncs,apm,anc,index.affinities,k=k,fast=FALSE,nomm=FALSE)

_A_r_g_u_m_e_n_t_s:

     pms: PM intensities after optical background correction, before
          non-specific-binding correction.

     mms: MM intensities after optical background correction, before
          non-specific-binding correction.

     ncs: Negative control probe intensities after optical background
          correction, before non-specific-binding correction. If
          'ncs=NULL', the MM probes are considered the negative control
          probes.

index.affinities: The index of pms with known sequences. (For some
          types of arrays the sequences of a small subset of probes are
          not provided by Affymetrix.)

     apm: Probe affinities for PM probes with known sequences.

     amm: Probe affinities for MM probes with known sequences.

     anc: Probe affinities for Negative control probes with known
          sequences. This is ignored when 'ncs=NULL'.

     rho: correlation coefficient of log background intensity in a pair
          of pm/mm probes. Default=.7

       k: A tuning parameter. See details.

    fast: Logical value. If 'TRUE' a faster add-hoc algorithm is used.

    nomm: 

_D_e_t_a_i_l_s:

     Assumes PM=background1+signal,mm=background2,
     (log(background1),log(background2))'  follow bivariate normal
     distribution, signal distribution follows power law. 
     'bg.parameters.gcrma' and 'sg.parameters.gcrma'  provide adhoc
     estimates of the parameters.

     the original gcrma uses an empirical Bayes estimate. this requires
     a complicated numerical integration. An add-hoc method tries to
     imitate the empirical Bayes estimate with a PM-B but values of
     PM-B<'k' going to 'k'. This can be thought as a shrunken MVUE. For
     more details see Wu et al. (2003).

_V_a_l_u_e:

     a vector of same length as x.

_A_u_t_h_o_r(_s):

     Rafeal Irizarry, Zhijin(Jean) Wu

_S_e_e _A_l_s_o:

     'gcrma'