bg.adjust.affinities {gcrma} | R Documentation |
An internal function to be used by gcrma
.
bg.adjust.fullmodel(pms,mms,ncs=NULL,apm,amm,anc=NULL,index.affinities,k=k,rho=.7,fast) bg.adjust.affinities(pms,ncs,apm,anc,index.affinities,k=k,fast)
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 ncs=NULL , the MM probes
are considered the negative control probes. |
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 ncs=NULL . |
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 TRUE a faster add-hoc algorithm is
used. |
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 emprical bayes estimate. this requiers a
complicated numerical integration. An add-hoc method tries to immitate
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