kooperberg {limma}R Documentation

Kooperberg Model-Based Background Correction

Description

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

Usage

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

Arguments

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

Details

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.

Value

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

Author(s)

Matthew Ritchie

References

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.

See Also

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

Examples

#  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)

[Package limma version 1.6.7 Index]