Description Usage Arguments Details Value Author(s) See Also
An internal function to be used by gcrma
.
1 2 3 4 | bg.adjust.fullmodel(pms,mms,ncs=NULL,apm,amm,anc=NULL,index.affinities,k=6
* fast + 0.25 * (1 - fast),rho=.7,fast=FALSE)
bg.adjust.affinities(pms,ncs,apm,anc,index.affinities,k=6
* fast + 0.25 * (1 - fast),fast=FALSE,nomm=FALSE)
|
pms |
PM intensities after optical background correction, before non-specific-binding correction. |
mms |
MM intensities after optical background correction, before non-specific-binding correction. |
ncs |
Negative control probe intensities after optical background correction, before
non-specific-binding correction. If |
index.affinities |
The index of pms with known sequences. (For some types of arrays the sequences of a small subset of probes are not provided by Affymetrix.) |
apm |
Probe affinities for PM probes with known sequences. |
amm |
Probe affinities for MM probes with known sequences. |
anc |
Probe affinities for Negative control probes with known
sequences. This is ignored when |
rho |
correlation coefficient of log background intensity in a pair of pm/mm probes. Default=.7 |
k |
A tuning parameter. See details. |
fast |
Logical value. If |
nomm |
Logical value indicating if MM intensities are available and will to be used to estimate background. |
Assumes PM=background1+signal,mm=background2,
(log(background1),log(background2))'
follow bivariate normal distribution, signal distribution follows power
law.
bg.parameters.gcrma
and sg.parameters.gcrma
provide adhoc estimates of the parameters.
the original gcrma uses an empirical Bayes estimate. this requires a
complicated numerical integration. An add-hoc method tries to imitate
the empirical Bayes estimate with a PM-B but values of PM-B<k
going to k
. This can be thought as a shrunken MVUE. For more
details see Wu et al. (2003).
a vector of same length as x.
Rafeal Irizarry, Zhijin(Jean) Wu
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