bg.adjust.gcrma: GCRMA background adjust (internal function)
In gcrma: Background Adjustment Using Sequence Information

Description Usage Arguments Details Value Author(s) Examples

This function performs background adjustment (optical noise and non-specific binding on an AffyBatch project and returns an AffyBatch object in which the PM intensities are adjusted.

bg.adjust.gcrma(object,affinity.info=NULL,
      affinity.source=c("reference","local"),
      NCprobe=NULL,
      type=c("fullmodel","affinities","mm","constant"),
      k=6*fast+0.5*(1-fast),stretch=1.15*fast+1*(1-fast),correction=1,
      GSB.adjust=TRUE,
      rho=.7,optical.correct=TRUE,verbose=TRUE,fast=TRUE)

`object`	an `AffyBatch`
`affinity.info`	`NULL` or an `AffyBatch` containing the affinities in the `exprs` slot. This object can be created using the function `compute.affinities`.
`affinity.source`	`reference`: use the package internal Non-specific binding data or `local`: use the experimental data in `object`. If `local` is chosen, either MM probes or a user-defined list of probes (see `NCprobes`) are used to estimate affinities.
`NCprobe`	Index of negative control probes. When set as `NULL`,the MM probes will be used. These probes are used to estimate parameters of non-specific binding on each array. These will be also used to estimate probe affinity profiles when affinity.info is not provided.
`type`	"fullmodel" for sequence and MM model. "affinities" for sequence information only. "mm" for using MM without sequence information.
`k`	A tuning factor.
`stretch`	.
`correction`	.
`GSB.adjust`	Logical value. If `TRUE`, probe effects in specific binding will be adjusted.
`rho`	correlation coefficient of log background intensity in a pair of pm/mm probes. Default=.7
`optical.correct`	Logical value. If `TRUE`, optical background correction is performed.
`verbose`	Logical value. If `TRUE` messages about the progress of the function is printed.
`fast`	Logical value. If `TRUE` a faster ad hoc algorithm is used.

The returned value is an AffyBatch object, in which the PM probe intensities have been background adjusted. The rest is left the same as the starting AffyBatch object.

The tunning factor k will have different meainngs if one uses the fast (ad hoc) algorithm or the empirical bayes approach. See Wu et al. (2003)

An AffyBatch.

Rafeal Irizarry

 if(require(affydata) & require(hgu95av2probe) & require(hgu95av2cdf)){
          data(Dilution)
          ai <- compute.affinities(cdfName(Dilution))
          Dil.adj<-bg.adjust.gcrma(Dilution,affinity.info=ai,type="affinities")
     }