Package 'gcrma' reference manual

Title:	Background Adjustment Using Sequence Information
Description:	Background adjustment using sequence information
Authors:	Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald <[email protected]> Jeff Gentry
Maintainer:	Z. Wu <[email protected]>
License:	LGPL
Version:	2.79.0
Built:	2024-11-29 08:11:53 UTC
Source:	https://github.com/bioc/gcrma

Spline coefficients for estimation of affinity from probe sequence

Description

Spline coefficients for estimation of affinity from probe sequence

Usage

data(affinity.spline.coefs)data(affinity.spline.coefs)

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)
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

`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.
`nomm`	Logical value indicating if MM intensities are available and will to be used to estimate background.

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.

Author(s)

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)
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.
`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.

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(s)

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

Arguments

`x`	PM or MM intensities after optical background correction, before non-specific-binding correction.
`affinities`	Probe affinities for probes with known sequences.Used to estimate the function between non-specific binding and affinities.
`affinities2`	Probe affinities for the probes whoes expected non-specific binding intensity is to be predicted.
`affinities3`	Probe affinities for another extra group of probes whoes expected non-specific binding intensity is to be predicted.
`span`	The span parameter passed to loess function

Value

a vector of same length as x.

Author(s)

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

Arguments

`cdfname`	Object of class `character` representing the name of `CDF` file associated with the arrays in the `AffyBatch`.
`probepackage`	`character` representing the name of the package with the probe sequence information.
`verbose`	Logical value. If `TRUE` messages about the progress of the function is printed.

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.

Author(s)

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.

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,...)
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,...)

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.
`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.
`subset`	a character vector with the the names of the probesets to be used in expression calculation.
`normalize`	logical value. If 'TRUE' normalize data using quantile normalization.
`...`	further arguments to be passed (not currently implemented - stub for future use).

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(s)

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")
}
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)
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

`pms`	The matrix of PM intensities
`mms`	The matrix of MM intensities
`ncs`	The matrix of negative control probe intensities. When left as`NULL`, the MMs are considered the negative control probes.
`pm.affinities`	The vector of PM probe affinities. Note: This can be shorter than the number of rows in `pms` when some probes do not have sequence information provided.
`mm.affinities`	The vector of MM probe affinities.
`anc`	The vector of Negative Control probe affinities. This is ignored if MMs are used as negative controls (`ncs=NULL`)
`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
`verbose`	Logical value. If `TRUE` messages about the progress of the function is printed.
`fast`	Logicalvalue. If `TRUE` a faster add-hoc algorithm is used.

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.

Author(s)

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)
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

`object`	an `AffyBatch`. Note: this is an internal function. Optical noise should have been corrected for.
`pmIndex`	Index of PM probes.This will be computed within the function if left `NULL`
`mmIndex`	Index of MM probes.This will be computed within the function if left `NULL`
`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.
`affinity.info`	`NULL` or an `AffyBatch` containing the affinities in the `exprs` slot. This object can be created using the function `compute.affinities`.
`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
`verbose`	Logical value. If `TRUE` messages about the progress of the function is printed.
`fast`	Logicalvalue. If `TRUE` a faster add-hoc algorithm is used.

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.

Author(s)

Rafeal Irizarry & Zhijin Wu

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(..., 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(..., 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)
just.gcrma(..., 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(..., 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

`...`	file names separated by comma.
`filenames`	file names in a character vector.
`widget`	a logical specifying if widgets should be used.
`compress`	are the CEL files compressed?
`phenoData`	a `AnnotatedDataFrame` object.
`description`	a `MIAME` object.
`notes`	notes.
`affinity.info`	`NULL` or a list of three components: apm,amm and index, for PM probe affinities, MM probe affinities, the index of probes with known sequence, respectively.
`type`	"fullmodel" for sequence and MM model. "affinities" for sequence information only. "mm" for using MM without sequence information.
`k`	A tuning factor.
`rho`	correlation coefficient of log background intensity in a pair of pm/mm probes. Default=.7.
`stretch`	.
`correction`	.
`normalize`	Logical value. If `TRUE`, then normalize data using quantile normalization.
`optical.correct`	Logical value. If `TRUE`, then optical background correction is performed.
`verbose`	Logical value. If `TRUE`, then messages about the progress of the function is printed.
`fast`	Logical value. If `TRUE`, then a faster add-hoc algorithm is used.
`optimize.by`	"speed" will use a faster algorithm but more RAM, and "memory" will be slower, but require less RAM.
`bgversion`	integer value indicating which RMA background to use 1: use background similar to pure R rma background given in affy version 1.0 - 1.0.2 2: use background similar to pure R rma background given in affy version 1.1 and above.
`minimum`	.
`celfile.path`	a character denoting the path 'ReadAffy' should look for cel files.
`sampleNames`	a character vector of sample names to be used in the 'AffyBatch'.
`cdfname`	Used to specify the name of an alternative cdf package. If set to `NULL`, the usual cdf package based on Affymetrix' mappings will be used. Note that the name should not include the 'cdf' on the end, and that the corresponding probe package is also required to be installed. If either package is missing an error will result.
`read.verbose`	Logical value. If `TRUE`, then messages will be printed as each celfile is read in.

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(s)

James W. MacDonald

Package 'gcrma'

Help Index

Spline coefficients for estimation of affinity from probe sequence

Description

Usage

See Also

Background adjustment with sequence information (internal function)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

GCRMA background adjust (internal function)

Description

Usage

Arguments

Details

Value

Author(s)

Examples

Estimation of non-specific Binding Background Parameters

Description

Usage

Arguments

Value

Author(s)

See Also

Probe Affinity computation

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Robust Multi-Array expression measure using sequence information

Description

Usage

Arguments

Details

Value

Author(s)

Examples

GCRMA background adjust engine(internal function)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

GCRMA background adjust engine(internal function)

Description

Usage

Arguments

Details

Value

Author(s)

See Also

Compute GCRMA Directly from CEL Files

Description

Usage

Arguments

Details

Value

Author(s)