olin                  package:OLIN                  R Documentation

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

     This functions performs optimised local intensity-dependent
     normalisation (OLIN) and  optimised scaled intensity-dependent
     normalisation (OSLIN).

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

     olin(object,X=NA,Y=NA,alpha=seq(0.1,1,0.1),iter=3,
                 scaling=FALSE,scale=c(0.05,0.1,0.5,1,2,10,20),weights=NA)

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

    iter: number of iterations in the OLIN procedure

 scaling: If scaling=TRUE, a subsequent optimised scaling is performed

   scale: vector of scale parameters that are tested in a 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 "maRaw" objects.

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

     OLIN and OSLIN are based on iterative local regression and
     incorporate optimisation of model parameters. Local regression is
     performed using LOCFIT, which requires the user to choose a
     specific 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.  Additionally, the setting of scale
     parameters  controls for distinct amount of smoothing in 
     Y-direction compared to smoothing in X-direction. The parameter
     'scale' can be of arbitrary value.  The choice of model parameters
     'alpha' and 'scale' for local regression is crucial for the
     efficiency and  quality of normalization. To optimize the model
     parameters, a general cross-validation procedure (GCV) is applied.
     The arguments 'alpha' and 'scale' define the parameters values
     which are tested in the GCV.  Detailed information about OLIN and
     OSLIN can be found in the package documentation and in the 
     reference stated below.  The weights argument specifies the
     influence of the single spots on the local regression. To exclude 
     spots being used for the local regression (such as control spots),
     set their corresponding weight to zero.  Note that OLIN and OSLIN
     are based on the assumptions that most genes are not
     differentially expressed (or up- and down-regulation is balanced)
     and that genes are randomly spotted across the array. If these
     assumptions are not valid, local regression can lead to an
     underestimation of differential expression.  OSLIN is especially
     sensitive to violations  of these assumptions. However, this
     sensitivity can be decreased if the minimal 'alpha'-value is
     increased. Minimal 'alpha' defines the  smallest scale used for
     local regression. Increasing 'alpha' can reduce the influence of
     localised  artifacts as a larger fraction of  data points is
     included. 

     It is also important to note that OLIN/OSLIN is fairly efficient
     in removing intensity- and spatial-dependent dye bias, so that
     normalised  data will look quite "good" after normalisation
     independently of the true underlying data quality. Normalisation
     by local regression assumes smoothness of bias. Therefore,
     localised artifacts such as scratches, edge effects or bubbles
     should be avoided. Spots of these areas should be flagged (before
     normalisation is applied) to ensure data integrity. To stringently
     detect artifacts, the OLIN functions 'fdr.int, fdr.spatial, p.int'
     and 'p.spatial' can be used.

_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

        2.  OLIN web-page: <URL:
           http://itb.biologie.hu-berlin.de/~futschik/software/R/OLIN>
           .inen -3 

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

     'maNorm', 'locfit', 'gcv'

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

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

     # OPTIMISED LOCAL INTENSITY-DEPENDENT NORMALISATION OF FIRST ARRAY
      norm.olin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1])

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

     # OPTIMISED SCALED LOCAL INTENSITY-DEPENDENT NORMALISATION
       norm.oslin <- olin(sw[,1],X=sw.xy$X[,1],Y=sw.xy$Y[,1],scaling=TRUE)
     # MA-PLOT
       plot(maA(norm.oslin),maM(norm.oslin),main="OSLIN")
     # MXY-PLOT
       Mtmp <- mxy.plot(maM(norm.oslin)[,1],Ngc=maNgc(norm.oslin),Ngr=maNgr(norm.oslin),
                      Nsc=maNsc(norm.oslin),Nsr=maNsr(norm.oslin),main="OSLIN")

