normalize.qspline            package:affy            R Documentation

_N_o_r_m_a_l_i_z_e _a_r_r_a_y_s

_D_e_s_c_r_i_p_t_i_o_n:

     normalizes arrays in an AffyBatch each other or to a set of target
     intensities

_U_s_a_g_e:

     normalize.AffyBatch.qspline(abatch,type=c("together", "pmonly", "mmonly",
                      "separate"), ...)

     normalize.qspline(x, target = NULL, samples = NULL, 
                       fit.iters = 5, min.offset = 5, 
                       spline.method = "natural", smooth = TRUE,
                       spar = 0, p.min = 0, p.max = 1.0, 
                       incl.ends = TRUE, converge = FALSE, 
                       verbose = TRUE, na.rm = FALSE)

_A_r_g_u_m_e_n_t_s:

       x: a 'data.matrix' of intensities

  abatch: an 'AffyBatch'

  target: numerical vector of intensity values to normalize to. (could
          be the name for one of the celfiles in 'abatch')

 samples: numerical, the number of quantiles to be used for spline. if
          (0,1], then it is a sampling rate

fit.iters: number of spline interpolations to average

min.offset: minimum span between quantiles (rank difference) for the
          different fit iterations

spline.method: specifies the type of spline to be used. Possible values
          are  `"fmm"', `"natural"', and `"periodic"'.

  smooth: logical, if `TRUE', smoothing splines are used on the
          quantiles

    spar: smoothing parameter for `splinefun', typically in (0,1].

   p.min: minimum percentile for the first quantile

   p.max: maximum percentile for the last quantile

incl.ends: include the minimum and maximum values from the normalized
          and target arrays in the fit

converge: (currently unimplemented)

 verbose: logical, if `TRUE' then normalization progress is reported

   na.rm: logical, if `TRUE' then handle NA values (by ignoring them)

    type: A string specifying how the normalization should be applied.
          See details for more.

     ...: Optional parameters to be passed through

_D_e_t_a_i_l_s:

     This normalization method uses the quantiles from each array and
     the  target to fit a system of cubic splines to normalize the
     data. The  target should be the mean (geometric) or median of each
     probe but could  also be the name of a particular chip in the
     'abatch' object.

     Parameters setting can be of much importance when using this
     method. The parameter 'fit.iter' is used as a starting point to
     find a more appropriate value. Unfortunately the algorithm used do
     not converge in some cases. If this happens, the 'fit.iter' value
     is used and a warning is thrown. Use of different settings for the
     parameter 'samples' was reported to give good results. More
     specifically, for about 200   data points use 'samples = 0.33',
     for about 2000  data points use 'samples = 0.05', for about 10000
     data points use 'samples = 0.02' (thanks to Paul Boutros).

     The 'type' argument should be one of
     '"separate","pmonly","mmonly","together"' which indicates whether
     to normalize only one probe type (PM,MM) or both together or
     separately.

_V_a_l_u_e:

     a normalized 'AffyBatch'.

_A_u_t_h_o_r(_s):

     Laurent and Workman C.

_R_e_f_e_r_e_n_c_e_s:

     Christopher Workman, Lars Juhl Jensen, Hanne Jarmer, Randy Berka,
     Laurent Gautier, Henrik Bj{\o}rn Nielsen, Hans-Henrik Saxild,
     Claus Nielsen, S{\o}ren Brunak, and Steen Knudsen. A new
     non-linear normal- ization method for reducing variability in dna
     microarray experiments. Genome Biology, accepted, 2002