kooperberg               package:limma               R Documentation

_K_o_o_p_e_r_b_e_r_g _M_o_d_e_l-_B_a_s_e_d _B_a_c_k_g_r_o_u_n_d _C_o_r_r_e_c_t_i_o_n

_D_e_s_c_r_i_p_t_i_o_n:

     This function uses a Bayesian model to background correct data
     from a series of microarray experiments. It currently works only
     with GenePix data.

_U_s_a_g_e:

     kooperberg(names, fg="mean", bg="median", a=FALSE, layout)

_A_r_g_u_m_e_n_t_s:

   names: character vector giving the names of data.frames containing
          GenePix data

      fg: character string giving foreground estimator. Choices are
          '"mean"' or '"median"'.

      bg: character string giving foreground estimator. Choices are
          '"mean"' or '"median"'.

       a: logical.  If 'TRUE', the 'a' parameters in the model
          (equation 3 and 4) are estimated for each slide.  If 'FALSE'
          the 'a' parameters are set to unity.

  layout: list containing print layout with components 'ngrid.r',
          'ngrid.c', 'nspot.r' and 'nspot.c'

_D_e_t_a_i_l_s:

     This function is for use with Genepix data and is designed to cope
     with the problem of large numbers of negative intensities and
     hence missing values on the log-intensity scale. It avoids missing
     values in most cases and at the same time dampens down the
     variability of log-ratios for low intensity spots. See Kooperberg
     et al (2003) for more details.

     'kooperberg' serially extracts the foreground and background
     intensities, standard deviations and number of pixels from GenePix
     data frames. This information is used to compute empirical
     estimates of the model parameters as described in equation 2 of
     Kooperberg et al (2003).

     The foreground and background estimates extracted from the Genepix
     files may be based on means or medians of pixel values. Setting
     'fg="mean"' uses the Genepix column 'F635.Mean' for red foreground
     and the Genepix column 'F532.Mean' for green foreground. Setting
     'fg="median"' uses columns 'F635.Median' and 'F532.Median'.
     Similarly for the background, 'bg="mean"' uses columns 'B635.Mean'
     and 'B532.Mean' while 'bg="median"' uses columns 'B635.Median' and
     'B532.Median'.

_V_a_l_u_e:

     A list containing the components 

       R: matrix containing the background adjusted intensities for the
          red channel for each spot for each array

       G: matrix containing the background adjusted intensities for the
          green channel for each spot for each array

_A_u_t_h_o_r(_s):

     Matthew Ritchie

_R_e_f_e_r_e_n_c_e_s:

     Kooperberg, C., Fazzio, T. G., Delrow, J. J., and Tsukiyama, T.
     (2002) Improved background correction for spotted DNA microarrays.
     _Journal of Computational Biology_ *9*, 55-66.

_S_e_e _A_l_s_o:

     4.Normalization gives an overview of normalization and background
     correction functions defined in the LIMMA package.

_E_x_a_m_p_l_e_s:

     #  This is example code for reading and background correcting GenePix data
     #  given GenePix Results (gpr) files in the working directory (data not
     #  provided).
     ## Not run: 
     genepixFiles <- dir(pattern="\\.gpr") # get the names of the GenePix image analysis output files in the current directory
     read.series(genepixFiles, suffix=NULL, skip=26, sep="\t") # read in GenePix files
     layout <- list(ngrid.r=12, ngrid.c=4, nspot.r=26, nspot.c=26) # specify array layout
     RGmodel <- kooperberg(genepixFiles, layout=layout) # model-based background correction
     MA <- normalizeWithinArrays(RGModel, layout) # normalize the data
     ## End(Not run)

