bg.adjust.affinities {gcrma}R Documentation

Background adjustment with sequence information (internal function)

Description

An internal function to be used by gcrma.

Usage

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)

Arguments

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.

Details

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).

Value

a vector of same length as x.

Author(s)

Rafeal Irizarry, Zhijin(Jean) Wu

See Also

gcrma


[Package gcrma version 2.2.0 Index]