Spline coefficients for estimation of affinity from probe sequence

Description

Spline coefficients for estimation of affinity from probe sequence

Usage

data(affinity.spline.coefs)

Seealso

compute.affinities

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=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)

Arguments

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 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).

Value

a vector of same length as x.

Seealso

gcrma

Author

Rafeal Irizarry, Zhijin(Jean) Wu

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

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)

Value

An AffyBatch .

Author

Rafeal Irizarry

Examples

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")
}

Estimation of non-specific Binding Background Parameters

Description

An internal function to be used by gcrma

Usage

bg.parameters.ns(x,affinities,affinities2=NULL,affinities3=NULL,span=.2)

Arguments

Value

a vector of same length as x.

Seealso

gcrma

Author

Rafeal Irizarry, Zhijin (Jean) Wu

Probe Affinity computation

Description

An internal function to calculate probe affinities from their sequences.

Usage

compute.affinities(cdfname,verbose=TRUE)
compute.affinities2(cdfname,verbose=TRUE)
check.probes(probepackage,cdfname)

Arguments

Details

The affinity of a probe is described as the sum of position-dependent base affinities. Each base at each position contributes to the total affinity of a probe in an additive fashion. For a given type of base, the positional effect is modeled as a spline function with 5 degrees of freedom.

Use compute.affinities2 if there are no MM probes.

check.probes makes sure things are matching as they should.

Value

compute.affinities returns an AffyBatch with the affinities for PM probes in the pm locations and the affinities for MM probes in the mm locations. NA will be added for probes with no sequence information.

Seealso

gcrma

Author

Rafeal Irizarry

References

Hekstra, D., Taussig, A. R., Magnasco, M., and Naef, F. (2003) Absolute mRNA concentrations from sequence-specific calibration of oligonucleotide array. Nucleic Acids Research, 31. 1962-1968.

Internal functions for justGCRMA

Description

These are internal functions for justGCRMA that are called based on memory or speed constraints.

Usage

fast.bkg(filenames, pm.affinities, mm.affinities, index.affinities,
type, minimum, optical.correct, verbose, k, rho, correction, stretch,
fast, cdfname, read.verbose)
mem.bkg(filenames, pm.affinities, mm.affinities, index.affinities, type,
minimum, optical.correct, verbose, k, rho, correction, stretch, fast,
cdfname, read.verbose)

Arguments

Details

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

The tuning factor 'k' will have different meanings if one uses the fast (add-hoc) algorithm or the empirical Bayes approach. See Wu et al. (2003)

Value

An ExpressionSet .

Seealso

gcrma

Author

James W. MacDonald jmacdon@med.umich.edu

Robust Multi-Array expression measure using sequence information

Description

This function converts an AffyBatch into an ExpressionSet using the robust multi-array average (RMA) expression measure with help of probe sequence.

Usage

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,
      subset=NULL,normalize=TRUE,list())

Arguments

Details

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

The tuning factor k will have different meanings if one uses the fast (add-hoc) algorithm or the empirical Bayes approach. See Wu et al. (2003)

Value

An ExpressionSet .

Author

Rafeal Irizarry

Examples

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

GCRMA background adjust engine(internal function)

Description

This function adjust for non-specific binding when all arrays in the dataset share the same probe affinity information. It takes matrices of PM probe intensities, MM probe intensities, other negative control probe intensities(optional) and the associated probe affinities, and return one matrix of non-specific binding corrected PM probe intensities.

Usage

gcrma.engine(pms,mms,ncs=NULL,
                         pm.affinities=NULL,mm.affinities=NULL,anc=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=0.7,
                         verbose=TRUE,fast=FALSE)

Arguments

Details

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

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

Value

A matrix of PM intensties.

Seealso

gcrma.engine2

Author

Rafeal Irizarry & Zhijin Wu

GCRMA background adjust engine(internal function)

Description

This function adjust for non-specific binding when each array has its own probe affinity information. It takes an AffyBatch object of probe intensities and an AffyBatch of probe affinity, returns one matrix of non-specific binding corrected PM probe intensities.

Usage

gcrma.engine2(object,pmIndex=NULL,mmIndex=NULL,
              NCprobe=NULL,affinity.info,
              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=0.7,
              verbose=TRUE,fast=TRUE)

Arguments

Details

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

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

Value

A matrix of PM intensties.

Seealso

gcrma.engine

Author

Rafeal Irizarry & Zhijin Wu

Functions for Automatic Download of Packages

Description

These are internal functions that are called by justGCRMA and GCRMA in order to automatically download and install cdf environments and probe packages.

Usage

getCDF(cdfname, lib = .libPaths()[1], verbose = TRUE)
getProbePackage(probepackage, lib = .libPaths()[1], verbose = TRUE)

Arguments

Value

Nothing is returned. These functions are called simply to install environments.

Seealso

getCDFinfo %code{link[affy]{getCDFinfo}} there is no such function in affy %called getCDFinfo

Author

James W. MacDonald, based on getCDFinfo , written by Jeff Gentry.

Compute GCRMA Directly from CEL Files

Description

This function converts CEL files into an ExpressionSet using the robust multi-array average (RMA) expression measure with help of probe sequences.

Usage

just.gcrma(list(), filenames=character(0),
                       phenoData=new("AnnotatedDataFrame"),
                       description=NULL,
                       notes="", compress=getOption("BioC")$affy$compress.cel,
                       normalize=TRUE, bgversion=2, affinity.info=NULL,
                       type=c("fullmodel","affinities","mm","constant"),
                       k=6*fast+0.5*(1-fast), stretch=1.15*fast+1*(1-fast),
                       correction=1, rho=0.7, optical.correct=TRUE,
                       verbose=TRUE, fast=TRUE, minimum=1, optimize.by =
                       c("speed","memory"), 
                       cdfname = NULL, read.verbose = FALSE)
            justGCRMA(list(), filenames=character(0),
                     widget=getOption("BioC")$affy$use.widgets,
                     compress=getOption("BioC")$affy$compress.cel,
                     celfile.path=getwd(),
                     sampleNames=NULL,
                     phenoData=NULL,
                     description=NULL,
                     notes="",
                     normalize=TRUE, 
                     bgversion=2, affinity.info=NULL,
                     type=c("fullmodel","affinities","mm","constant"),
                     k=6*fast+0.5*(1-fast), stretch=1.15*fast+1*(1-fast),
                     correction=1, rho=0.7, optical.correct=TRUE,
                     verbose=TRUE, fast=TRUE, minimum=1,
                     optimize.by = c("speed","memory"),
                     cdfname = NULL, read.verbose = FALSE)

Arguments

Details

This method should require much less RAM than the conventional method of first creating an AffyBatch and then running gcrma .

This is a simpler version than gcrma , so some of the arguments available in gcrma are not available here. For example, it is not possible to use the MM probes to estimate background. Instead, the internal NSB estimates are used (which is also the default for gcrma ).

Note that this expression measure is given to you in log base 2 scale. This differs from most of the other expression measure methods.

The tuning factor k will have different meanings if one uses the fast (add-hoc) algorithm or the empirical Bayes approach. See Wu et al. (2003)

fast.bkg and mem.bkg are two internal functions.

Value

An ExpressionSet object.

Author

James W. MacDonald