
kooperberg(RG, a=TRUE, layout=RG$printer, verbose=TRUE)
read.maimages
, with other.columns=c("F635 SD","B635 SD","F532 SD","B532 SD","B532 Mean","B635 Mean","F Pixels","B Pixels")
.TRUE
, the 'a' parameters in the model (equation 3 and 4) are estimated for each slide. If FALSE
the 'a' parameters are set to unity.ngrid.r
, ngrid.c
, nspot.r
and nspot.c
. Defaults to RG$printer
.TRUE
, progress is reported to standard output.RGList
containing the components
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).
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
# 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:
# # get the names of the GenePix image analysis output files in the current directory
# genepixFiles <- dir(pattern="*\\.gpr$")
# 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)
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