withinNorm             package:stepNorm             R Documentation

_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 _f_o_r _c_D_N_A _s_p_o_t_t_e_d _m_i_c_r_o_a_r_r_a_y_s

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

     This function is a wrapper function around 'fitWtihin' and
     'fit2DWithin'. It allows the user to choose from a set of thirteen
     basic location normalization procedures. The function operates on
     an object of class 'marrayRaw' or 'marrayNorm' and returns an
     object of class 'Norm'.

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

     withinNorm(marraySet,  y = "maM", subset = TRUE, norm = c("none", 
         "median", "rlm", "loess", "medianPrintTip", "rlmPrintTip", 
         "loessPrintTip", "medianPlate", "rlmPlate", "loessPlate", 
         "aov2D", "rlm2D", "loess2D", "spatialMedian"), ...)

_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.

    norm: A character string specifying the normalization procedures:

          _n_o_n_e:  no normalization

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

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

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

          _m_e_d_i_a_n_P_r_i_n_t_T_i_p:  within-print-tip-group median normalization

          _r_l_m_P_r_i_n_t_T_i_p:  within-print-tip-group intensity or A-dependent
               robust linear normalization using the 'rlm' function

          _l_o_e_s_s_P_r_i_n_t_T_i_p:  within-print-tip-group intensity or
               A-dependent robust nonlinear normalization using the
               'loess' function

          _m_e_d_i_a_n_P_l_a_t_e:  within-well-plate-group median normalization

          _r_l_m_P_l_a_t_e:  within-well-plate-group intensity or A-dependent
               robust linear normalization using the 'rlm' function

          _l_o_e_s_s_P_l_a_t_e:  within-well-plate-group intensity or A-dependent
               robust nonlinear normalization using the 'loess'
               function

          _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)

     ...: Misc arguements for the specified 'norm' function

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

     The function 'withinNorm' dispatches to the function 'fitWithin'
     or 'fit2DWithin' with specified arguments according to the choice
     of 'norm'. For instance, when 'norm="loess"' for global intenstiy
     dependent robust nonlinear normalization, 'withinNorm' calls
     'fitWithin(fun="loess")' with the default span parameter set at
     0.4. If a different span is preferred, it should be input by
     'span=0.2' through the argument '...' in the 'withinNorm' function
     (see example below). For more details see 'fitWithin',
     'fit2DWithin' and individual fitting functions such as 'loessfit'.

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

     An object of class 'marrayNorm', containing the normalized
     intensity data.

_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', 'stepWithinNorm', 'fitWithin', 'fit2DWithin',
     'loessfit', 'rlmfit'.

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

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

     # Apply loess normalization for the first slide, span=0.4
     ## Not run: 
     res.swirl1 <- withinNorm(swirl[,1], norm="loess")
     ## End(Not run)

     # Apply loess normalization for the first slide, span=0.2
     ## Not run: 
     res.swirl1 <- withinNorm(swirl[,1], norm="loess", span=0.2)
     ## End(Not run)

