lin                   package:OLIN                   R Documentation

_L_o_c_a_l _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 local intensity-dependent normalisation
     (LIN)

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

     lin(object,X=NA,Y=NA,alpha=0.3,iter=2,scale=TRUE,weights=NA,bg.corr="subtract",...)

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

  object: object of class "marrayRaw"

       X: matrix with x-coordinates of spots. If X=NA, columns on array
          are used as proxies for the location in x-direction

       Y: matrix with y-coordinates of spots. If Y=NA, rows on array
          are used as proxies for the location in y-direction

   alpha: smooting parameter for local regression

    iter: number of iterations in the LIN procedure

   scale: scale parameter for smooting in Y-direction of the array in
          respect to smoothing in  X-direcction. If 'scale=TRUE',
          standard deviations are used. 

 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 "maRaw" objects.

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

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

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

     LIN  is based on the same normalisation scheme as OLIN, but does
     not  incorporate  optimisation of  model  parameters. The function
     'lin' can serve for comparison. Alternatively, it can be used to
     enforce a conservative model fit.

     The smoothing parameter  'alpha' controls the neighbourhood size
     _h_ of local fitting.  It 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 cover the whole expression range and are
     spotted accross the whole array.

_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>)

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

        1.  M.Futschik and T.Crompton (2004) _Model selection and
           efficiency testing for normalization of cDNA microarray
           data_, *Genome Biology*, 5:R60

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

     'maNorm', 'locfit',  'olin','oin'

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

     # LOADING DATA
       data(sw)
       data(sw.xy)

     # LOCAL INTENSITY-DEPENDENT NORMALISATION 
      norm.lin <- lin(sw,X=sw.xy$X,Y=sw.xy$Y)

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

