seqWithinNorm            package:stepNorm            R Documentation

_S_e_q_u_e_n_t_i_a_l _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
     seqential 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:

       seqWithinNorm(marraySet, y = "maM", subset = TRUE, loss.fun = square,
       A = c("loess", "rlm", "median", "none"),
       PT = c("median", "rlm", "loess", "none"),
       PL = c("median", "rlm", "loess", "none"),
       Spatial2D = c("none", "aov2D", "rlm2D", "loess2D", "spatialMedian"),
       criterion = c("BIC", "AIC"))

_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.

       y: Name of accessor method for spot statistics, usually the
          log-ratio 'maM'.

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

loss.fun: The loss function used in calucating deviance, the default
          uses squared sum of residuals; for absolute sum of residuals,
          use 'abs'

       A: A character string specifying the normalization model for the
          adjustment of intensity or A bias:

          _l_o_e_s_s:  global intensity or A-dependent robust nonlinear
               normalization using the 'loess' function

          _r_l_m:  global intensity or A-dependent robust linear
               normalization using the 'rlm' function

          _m_e_d_i_a_n:  global median location normalization

          _n_o_n_e:  no normalization for the A bias

          If not specified, 'loess' normalization will be applied. 

      PT: A character string specifying the normalization model for the
          adjustment of print-tip or PT bias:

          _m_e_d_i_a_n:  within-print-tip-group median normalization

          _r_l_m:  within-print-tip-group robust linear normalization
               using the 'rlm' function

          _l_o_e_s_s:  within-print-tip-group robust nonlinear normalization
               using the 'loess' function

          _n_o_n_e:  no normalization for the PT bias

          If not specified, 'median' normalization within print-tip
          will be applied. 

      PL: A character string specifying the normalization model for the
          adjustment of well-plate or PL bias:

          _m_e_d_i_a_n:  within-well-plate median normalization

          _r_l_m:  within-well-plate robust linear normalization using the
               'rlm' function

          _l_o_e_s_s:  within-well-plate robust nonlinear normalization
               using the 'loess' function

          _n_o_n_e:  no normalization for the PL bias

          If not specified, 'median' normalization within well-plate
          will be applied. 

Spatial2D: A character string specifying the normalization model for
          the adjustment of spatial 2D bias:

          _n_o_n_e:  no normalization for the spatial 2D bias

          _a_o_v_2_D:  spatial bivariate location normalization using ANOVA

          _r_l_m_2_D:  spatial bivariate location normalization using the
               'rlm' function

          _l_o_e_s_s_2_D:  spatial bivariate location normalization using the
               'loess' function

          _s_p_a_t_i_a_l_M_e_d_i_a_n:  spatial location normalization using a
               spatial median approach (see Wilson et al. (2003) in
               reference)

          If not specified, no normalization will be carried out in
          this step. 

criterion: Character string specifying the criterion:

          _A_I_C: the AIC criterion is used; see 'calcAIC'.

          _B_I_C: the BIC criterion is used; see 'calcBIC'.

          If no specification, 'BIC' is used. Note that here we don't
          use the criterion to choose normalization model in each step.
          Criterion is calculated solely for informaion purpose.

_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 (Spatial 2D). The sequential
     normalization procedure in 'seqWithinNorm' normalizes a slide in a
     sequential fashion: A -> PT -> PL -> Spatial2D. In each step one
     kind of variation is targeted for correction, and the user chooses
     the normalization method as desired. We calculate the AIC/BIC
     criterion along the normalization steps, but they are not used for
     selection of models.

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

     An object of class "list": 

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

     res: a list of the sequential normalization result for each slide
          within the marray dataset. Each list component is also a list
          containing the name of the biases, deviance, equivalent
          number of parameters, AIC/BIC value for a certain slide.

_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:

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

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

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

     # Apply sequential normalization for the first slide
     # default: loess(A)->median(PT)->median(PL)-> none (Spatial2D)
     ## Not run: 
     res.swirl1 <- seqWithinNorm(swirl[,1])

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

     # sequential normalization information
     step.swirl <- res.swirl1[[2]]

     ## End(Not run)
     # median(A)->median(PT)->median(PL)->none(Spatial2D)
     res.swirl <- seqWithinNorm(swirl[,1], A="median",PT="median",PL="median",Spatial2D="none")

