oin                   package:OLIN                   R Documentation

_O_p_t_i_m_i_s_e_d _i_n_t_e_n_s_i_t_y-_d_e_p_e_n_d_e_n_t _n_o_r_m_a_l_i_s_a_t_i_o_n _o_f _t_w_o-_c_o_l_o_u_r _m_i_c_r_o_a_r_r_a_y_s

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

     This functions performs optimised  intensity-dependent
     normalisation (OLIN).

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

     oin(object,alpha=seq(0.1,1,0.1),weights=NA,bg.corr="subtract",...)

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

  object: object of class "marrayRaw" or "marrayNorm"

   alpha: vector of alpha parameters that are tested in the GCV
          procedure

 weights: matrix of weights for  local regression.  Rows correspond to
          the spotted probe sequences, columns to arrays in the batch.
          These may be derived from the matrix of  spot quality weights
          as defined  for "marrayRaw" objects.

 bg.corr: backcorrection method (for "marrayRaw" objects)  : "none" or
          "subtract"(default).

     ...: Further arguments for 'locfit' function.

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

     The function 'oin' is  based on iterative local regression of
     logged fold changes in respect to average logged spot intensities.
     It incorporates optimisation of the smoothing parameter  'alpha' 
     that controls the neighbourhood size _h_ of local fitting. The
     parameter 'alpha'  specifies the fraction of points that are
     included in the neighbourhood and thus has a value between 0 and
     1. Larger 'alpha' values lead to smoother fits. 

     If the normalisation should be based on set of genes assumed to be
     not differentially expressed (_house-keeping genes_), weights can
     be used for local regression. In this case, all weights should be 
     set to zero except for the house-keeping genes for which weights
     are set to one. In order to achieve a reliable regression, it is
     important, however, that there is a sufficient number of
     house-keeping genes that are distributed over the whole expression
     range and spotted accross the whole array.

     In contrast to OLIN and OSLIN, the OIN scheme does not correct for
     spatial dye  bias. It can, therefore, be used if the assumption of
     random spotting does not hold.

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

     Object of class "marrayNorm" with normalised logged ratios

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

     Matthias E. Futschik (<URL:
     http://itb.biologie.hu-berlin.de/~futschik>)

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

     'maNorm', 'locfit', 'gcv', 'olin' ,'lin', 'ino'

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

     # LOADING DATA
       data(sw)

     # OPTIMISED INTENSITY-DEPENDENT NORMALISATION
      norm.oin <- oin(sw)

     # MA-PLOT OF NORMALISATION RESULTS OF FIRST ARRAY
      plot(maA(norm.oin)[,1],maM(norm.oin)[,1],main="OIN")
      
     # CORRESPONDING MXY-PLOT
       mxy.plot(maM(norm.oin)[,1],Ngc=maNgc(norm.oin),Ngr=maNgr(norm.oin),
                     Nsc=maNsc(norm.oin),Nsr=maNsr(norm.oin),main="OIN")

     #

