stepWithinNorm           package:stepNorm           R Documentation

_S_t_e_p_w_i_s_e _w_i_t_h_i_n-_s_l_i_d_e _n_o_r_m_a_l_i_z_a_t_i_o_n _f_u_n_c_t_i_o_n

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

     This function conducts cDNA microarray normalization in a stepwise
     fashion. In a two-color cDNA array setting, within-slide
     normalization calibrates signals from the two channels to remove
     non-biological variation introduced by various processing steps.

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

     stepWithinNorm(marraySet, subset=TRUE, wf.loc, criterion = c("BIC", "AIC"), loss.fun = square)

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

marraySet: Object of class 'marrayRaw' or class 'marrayNorm',
          containing intensity data for the batch of arrays to be
          normalized.

  subset: A "logical" or "numeric" vector indicating the subset of
          points used to compute the  normalization values.

  wf.loc: Object of class 'list', each component is a step for the
          removal of a particular systematic varation. Typically each
          step is also a list of several candidate models of different
          complexity, the best model will be chosen by the 'criterion'
          specified. For a user friendly way of constructing such a
          list, consult the function 'makeStepList'.If missing, the
          default procedure will be used, which we consider appropriate
          for most slides. See details for how to specify the list and
          how it is used.

criterion: Character string specifying the criterion used for the
          selection of the  best normalization procedure in each step.
          This argument can be specified using the  first letter of
          each method; if no specification is made, the default is
          'BIC':

          _A_I_C: the AIC criterion is used

          _B_I_C: the BIC criterion is used.

loss.fun: loss function; default set at using residual sum of squares.

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

     Typical systematic non-biological variations of a two-color cDNA
     microarray include the dependence of ratio measurements (M) on
     intensity (A), print-tip IDs (PT), plate IDs (PL) and spatial
     heterogeneity of the slide (SP). The stepwise normalization
     procedure normalizes a slide in a stepwise fashion. In each step
     one kind of variation is targeted for correction. Within each
     step, various candidate models are assessed for their adequacy
     with respect to the observed data. The assessment is made based on
     a common model selection criterion, AIC (see 'calcAIC') or BIC
     (see 'calcBIC'), and the best model is then chosen for the
     specified step.

     The argument 'wf.loc' is a list of steps. Each step is also a list
     of models. The user uses the function 'fitWithin' or 'fit2DWithin'
     to specify a model. Below is a table of how to do so:

       systematic variation  model                       function
       intenstiy (A)         median                      fitWithin(fun="medfit")
       A                     robust linear               fitWithin(fun="rlmfit")
       A                     robust nonlinear            fitWithin(fun="loessfit")
       print-tip (PT)        median                      fitWithin(z.fun="maPrintTip", fun="medfit")
       PT                    robust linear               fitWithin(z.fun="maPrintTip", fun="rlmfit")
       PT                    robust nonlinear            fitWithin(z.fun="maPrintTip",fun="loessfit")
       plate (PL)            median                      fitWithin(z.fun="maCompPlate", fun="medfit")
       PL                    robust linear               fitWithin(z.fun="maComplate", fun="rlmfit")
       PL                    robust nonlinear            fitWithin(z.fun="maCompPlate", fun="loessfit")
       spatial (SP)          robust linear               fit2DWithin(fun="rlm2Dfit")
       SP                    robust nonlinear(span=0.2)  fit2DWithin(fun="loess2Dfit", span=0.2)
       SP                    anova                       fit2DWithin(fun="aov2Dfit")
       SP                    spatial median (11X11)      fit2DWithin(fun="spatialMedfit", width=11)

     If the 'wf.loc' is not specified by the user, the default
     procedure conducts normalization in four steps: A -> PT -> PL ->
     SP and models are as described in the table above. The user can
     choose not to follow such a procedure by passing in a different
     list, however we advocate normalizing the intensity (A) variation
     first as it is usually the source of most variation in most
     slides. The list can be easier specified using the function
     'makeStepList' by inputing models as character strings, see
     'makeStepList' for details.

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

     An object of class "list": 

normdata: an object of class 'marrayNorm', containing the normalized
          intensity data.

     res: a dataframe of the stepwise normalization result, containing
          the name of the model chosen for each step, deviance,
          equivalent number of parameters, AIC/BIC value.

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

     Yuanyuan Xiao, yxiao@itsa.ucsf.edu, 
      Jean Yee Hwa Yang, jean@biostat.ucsf.edu

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

     Y. H. Yang, S. Dudoit, P. Luu, and T. P. Speed (2001).
     Normalization for cDNA microarray data. In M. L. Bittner, Y. Chen,
     A. N. Dorsel, and E. R. Dougherty (eds), _Microarrays: Optical
     Technologies and Informatics_, Vol. 4266 of _Proceedings of SPIE_.

     D. L. Wilson, M. J. Buckley, C. A. Helliwell and I. W. Wilson
     (2003). New normalization methods for cDNA microarray data.
     _Bioinformatics_, Vol. 19, pp. 1325-1332.

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

     'seqWithinNorm', 'withinNorm', 'fitWithin', 'fit2DWithin',
     'calcAIC', 'calcBIC'.

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

     # Examples use swirl dataset, for description type ? swirl
     data(swirl)

     # Apply stepwise normalization for the first slide
     res.swirl1 <- stepWithinNorm(swirl[,1])

     # normalized data
     norm.swirl <- res.swirl1[[1]]

     # stepwise procedure
     step.swirl <- res.swirl1[[2]]

     # using a stepwise procedure different than the default
     # corrects intensity (A) and print-tip (PT), this can be
     # carried out in two ways:
     # 1)
     steps <- list(
                 wholeChipA = list(med = fitWithin(fun="medfit"),
                                   rlm = fitWithin(fun="rlmfit"),
                                   loess = fitWithin(fun="loessfit")),
                 printTipA = list(med = fitWithin(z.fun="maPrintTip", fun="medfit"),
                                  rlm = fitWithin(z.fun="maPrintTip", fun="rlmfit"),
                                  loess = fitWithin(z.fun="maPrintTip",fun="loessfit")))
                                  
     #2)                          
     steps <- makeStepList(PL=NULL, Spatial2D=NULL)
     ## Not run: 
     res.swirl <- stepWithinNorm(swirl[,1], wf.loc=steps)
     ## End(Not run)

     # using AIC criterion for the first slide
     ## Not run: 
     res.swirl <- stepWithinNorm(swirl[,1], criterion="A")
     ## End(Not run)

