dyebias.application.subset {dyebias} | R Documentation |
Convenience function returning a subset of reporters that can be
expected to be corrected reasonably well. Often, the logical AND of
this set and that of maW(data.norm) == 1.0
is used. The
resulting subset is passed as the application.subset
-argument
to dyebias.apply.correction
.
dyebias.application.subset(data.raw=NULL, min.SNR=1.5, use.background=FALSE, maxA=15)
data.raw |
A |
min.SNR |
The minimum signal to noise ratio to require. It is loosely defined here as the foreground over the background signal. The background signal may not be real; see below. |
use.background |
Logical indicating whether or not to use the background signals |
maxA |
The maximum signal that is still allowed. |
This routine requires an marrayRaw
object since only that
contains the background intensities. If you only have normalized data,
use something like
bg <- matrix(0.5, nrow=maNspots(data.norm), ncol=maNsamples(data.norm)) data.raw <- new("marrayRaw", maRf=maR(data.norm), maGf=maG(data.norm), maRb=bg, maGb=bg, maW=maW(data.norm))
A matrix of logicals with the same dimensions as those of
maRf{data.raw}
is returned.
Philip Lijnzaad p.lijnzaad@umcutrecht.nl
Margaritis, T., Lijnzaad, P., van Leenen, D., Bouwmeester, D., Kemmeren, P., van Hooff, S.R and Holstege, F.C.P. (2009) Adaptable gene-specific dye bias correction for two-channel DNA microarrays. Molecular Systems Biology, 5:266, 2009. doi: 10.1038/msb.2009.21.
## First load data and estimate the iGSDBs ## (see dyebias.estimate.iGSDBs) ### choose the estimators and which spots to correct: estimator.subset <- dyebias.umcu.proper.estimators(maInfo(maGnames(data.norm))) ### choose which genes to dye bias correct. Typically, this is based ### both on flagged spots and intensity application.subset <- maW(data.norm) == 1 & dyebias.application.subset(data.raw=data.raw, use.background=TRUE) summary(application.subset)