kooperberg: Kooperberg Model-Based Background Correction for GenePix data
In richierocks/limma2: Linear Models for Microarray Data

Description Usage Arguments Details Value Author(s) References See Also Examples

This function uses a Bayesian model to background correct GenePix microarray data.

1	kooperberg(RG, a=TRUE, layout=RG$printer, verbose=TRUE)

`RG`	an RGList of GenePix data, read in using `read.maimages`, with `other.columns=c("F635 SD","B635 SD","F532 SD","B532 SD","B532 Mean","B635 Mean","F Pixels","B Pixels")`.
`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`. Defaults to `RG$printer`.
`verbose`	logical. If `TRUE`, progress is reported to standard output.

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 (2002) for more details.

kooperberg uses the foreground and background intensities, standard deviations and number of pixels to compute empirical estimates of the model parameters as described in equation 2 of Kooperberg et al (2002).

An RGList 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
`printer`	list containing print layout

Matthew Ritchie

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.

Ritchie, M. E., Silver, J., Oshlack, A., Silver, J., Holmes, M., Diyagama, D., Holloway, A., and Smyth, G. K. (2007). A comparison of background correction methods for two-colour microarrays. Bioinformatics 23, 2700-2707. http://bioinformatics.oxfordjournals.org/cgi/content/abstract/btm412

04.Background gives an overview of background correction functions defined in the LIMMA package.

#  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
RG <- read.maimages(genepixFiles, source="genepix", other.columns=c("F635 SD","B635 SD","F532 SD","B532 SD","B532 Mean","B635 Mean","F Pixels","B Pixels"))
RGmodel <- kooperberg(RG)
MA <- normalizeWithinArrays(RGmodel)

## End(Not run)