bg.adjust.gcrma: GCRMA background adjust (internal function)

Description

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.

Usage

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)

Arguments

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.

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.

Value

An AffyBatch.

Details

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)

Examples

Run this code

 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")
     }