Package 'aroma.cn' reference manual

Title:	Copy-Number Analysis of Large Microarray Data Sets
Description:	Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.
Authors:	Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [aut]
Maintainer:	Henrik Bengtsson <[email protected]>
License:	LGPL (>= 2.1)
Version:	1.7.1
Built:	2024-07-16 04:26:04 UTC
Source:	https://github.com/HenrikBengtsson/aroma.cn

Package aroma.cn

Description

Methods for analyzing DNA copy-number data. Specifically, this package implements the multi-source copy-number normalization (MSCN) method for normalizing copy-number data obtained on various platforms and technologies. It also implements the TumorBoost method for normalizing paired tumor-normal SNP data.

This package should be considered to be in an alpha or beta phase. You should expect the API to be changing over time.

Installation and updates

To install this package, call install.packages("aroma.cn").

To get started

To get started, see:

License

The releases of this package is licensed under LGPL version 2.1 or newer.

The development code of the packages is under a private licence (where applicable) and patches sent to the author fall under the latter license, but will be, if incorporated, released under the "release" license above.

Author(s)

Henrik Bengtsson, Pierre Neuvial

References

Please cite PSCBS using one or more of the following references:

A.B. Olshen, H. Bengtsson, P. Neuvial, P.T. Spellman, R.A. Olshen, V.E. Seshan. Parent-specific copy number in paired tumor-normal studies using circular binary segmentation, Bioinformatics, 2011

Please cite PSCBS using one or more of the following references:

H. Bengtsson, P. Neuvial and T.P. Speed. TumorBoost: Normalization of allele-specific tumor copy numbers from a single pair of tumor-normal genotyping microarrays, BMC Bioinformatics, 2010

H. Bengtsson, A. Ray, P. Spellman and T.P. Speed. A single-sample method for normalizing and combining full-resolutioncopy numbers from multiple platforms, labs and analysis methods, Bioinformatics, 2009

H. Bengtsson; K. Simpson; J. Bullard; K. Hansen. aroma.affymetrix: A generic framework in R for analyzing small to very large Affymetrix data sets in bounded memory, Tech Report 745, Department of Statistics, University of California, Berkeley, February 2008

H. Bengtsson, R. Irizarry, B. Carvalho, & T.P. Speed. Estimation and assessment of raw copy numbers at the single locus level, Bioinformatics, 2008

To see these entries in BibTeX format, use 'print(<citation>, bibtex=TRUE)', 'toBibtex(.)', or set 'options(citation.bibtex.max=999)'.

The AbstractCurveNormalization class

Description

Package: aroma.cn
Class AbstractCurveNormalization

Object
~~|
~~+--AbstractCurveNormalization

Directly known subclasses:
PrincipalCurveNormalization, XYCurveNormalization

public abstract static class AbstractCurveNormalization
extends Object

Usage

AbstractCurveNormalization(dataSet=NULL, targetSet=NULL, subsetToFit=NULL, tags="*",
  copyTarget=TRUE, ...)
AbstractCurveNormalization(dataSet=NULL, targetSet=NULL, subsetToFit=NULL, tags="*",
  copyTarget=TRUE, ...)

Arguments

`dataSet`	An `AromaUnitTotalCnBinarySet` of "test" samples to be normalized.
`targetSet`	An `AromaUnitTotalCnBinarySet` of paired target samples.
`subsetToFit`	The subset of loci to be used to fit the normalization functions. If `NULL`, loci on chromosomes 1-22 are used, but not on ChrX and ChrY.
`tags`	(Optional) Sets the tags for the output data sets.
`copyTarget`	If `TRUE`, target arrays are copied to the output data set, otherwise not.
`...`	Not used.

Fields and Methods

Methods:

	`getFullName`	-
	`getInputDataSet`	-
	`getName`	-
	`getOutputDataSet`	-
	`getTags`	-
	`getTargetDataSet`	-
	`process`	-
	`setTags`	-

Methods inherited from Object:
$, $<-, [[, [[<-, as.character, attach, attachLocally, clearCache, clearLookupCache, clone, detach, equals, extend, finalize, getEnvironment, getFieldModifier, getFieldModifiers, getFields, getInstantiationTime, getStaticInstance, hasField, hashCode, ll, load, names, objectSize, print, save, asThis

Author(s)

Henrik Bengtsson

Calls XX or XY from ChrX allele B fractions of a normal sample

Description

Calls XX or XY from ChrX allele B fractions of a normal sample.

Usage

## S3 method for class 'numeric'
callXXorXY(betaX, betaY=NULL, flavor=c("density"), adjust=1.5, ...,
  censorAt=c(-0.5, +1.5), verbose=FALSE)
## S3 method for class 'numeric'
callXXorXY(betaX, betaY=NULL, flavor=c("density"), adjust=1.5, ...,
  censorAt=c(-0.5, +1.5), verbose=FALSE)

Arguments

`betaX`	A `numeric` `vector` containing ChrX allele B fractions.
`betaY`	A optional `numeric` `vector` containing ChrY allele B fractions.
`flavor`	A `character` string specifying the type of algorithm used.
`adjust`	A postive `double` specifying the amount smoothing for the empirical density estimator.
`...`	Additional arguments passed to `findPeaksAndValleys`.
`censorAt`	A `double` `vector` of length two specifying the range for which values are considered finite. Values below (above) this range are treated as -`Inf` (+`Inf`).
`verbose`	A `logical` or a `Verbose` object.

Value

Returns a ...

Missing and non-finite values

Missing and non-finite values are dropped before trying to call XX or XY.

Author(s)

Henrik Bengtsson, Pierre Neuvial

The MultiSourceCopyNumberNormalization class

Description

Package: aroma.cn
Class MultiSourceCopyNumberNormalization

Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--MultiSourceCopyNumberNormalization

Directly known subclasses:

public static class MultiSourceCopyNumberNormalization
extends ParametersInterface

The multi-source copy-number normalization (MSCN) method [1] is a normalization method that normalizes copy-number estimates measured by multiple sites and/or platforms for common samples. It normalizes the estimates toward a common scale such that for any copy-number level the mean level of the normalized data are the same.

Usage

MultiSourceCopyNumberNormalization(dsList=NULL, fitUgp=NULL, subsetToFit=NULL,
  targetDimension=1, align=c("byChromosome", "none"), tags="*", ...)
MultiSourceCopyNumberNormalization(dsList=NULL, fitUgp=NULL, subsetToFit=NULL,
  targetDimension=1, align=c("byChromosome", "none"), tags="*", ...)

Arguments

`dsList`	A `list` of K `AromaUnitTotalCnBinarySet`:s.
`fitUgp`	An `AromaUgpFile` that specifies the common set of loci used to normalize the data sets at.
`subsetToFit`	The subset of loci (as mapped by the `fitUgp` object) to be used to fit the normalization functions. If `NULL`, loci on chromosomes 1-22 are used, but not on ChrX and ChrY.
`targetDimension`	A `numeric` index specifying the data set in `dsList` to which each platform in standardize towards. If `NULL`, the arbitrary scale along the fitted principal curve is used. This always starts at zero and increases.
`align`	A `character` specifying type of alignment applied, if any. If `"none"`, no alignment is done. If `"byChromosome"`, the signals are shifted chromosome by chromosome such the corresponding smoothed signals have the same median signal across sources. For more details, see below.
`tags`	(Optional) Sets the tags for the output data sets.
`...`	Not used.

Details

The multi-source normalization method is by nature a single-sample method, that is, it normalizes arrays for one sample at the time and independently of all other samples/arrays.

However, the current implementation is such that it first generates smoothed data for all samples/arrays. Then, it normalizes the sample one by one.

Fields and Methods

Methods:

	`getAllNames`	-
	`getAsteriskTags`	-
	`getInputDataSets`	-
	`getOutputDataSets`	-
	`getTags`	-
	`nbrOfDataSets`	-
	`process`	-

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

Different preprocessing methods normalize ChrX & ChrY differently

Some preprocessing methods estimate copy numbers on sex chromosomes differently from the autosomal chromosomes. The way this is done may vary from method to method and we cannot assume anything about what approach is. This is the main reason why the estimation of the normalization function is by default based on signals from autosomal chromosomes only; this protects the estimate of the function from being biased by specially estimated sex-chromosome signals. Note that the normalization function is still applied to all chromosomes.

This means that if the transformation applied by a particular preprocessing method is not the same for the sex chromosomes as the autosomal chromosomes, the normalization applied on the sex chromosomes is not optimal one. This is why multi-source normalization sometimes fails to bring sex-chromosome signals to the same scale across sources. Unfortunately, there is no automatic way to handle this. The only way would be to fit a specific normalization function to each of the sex chromosomes, but that would require that there exist copy-number abberations on those chromosomes, which could be a too strong assumption.

A more conservative approach is to normalize the signals such that afterward the median of the smoothed copy-number levels are the same across sources for any particular chromosome. This is done by setting argument align="byChromosome".

Author(s)

Henrik Bengtsson

References

[1] H. Bengtsson, A. Ray, P. Spellman & T.P. Speed, A single-sample method for normalizing and combining full-resolution copy numbers from multiple platforms, labs and analysis methods, Bioinformatics 2009.

The PairedPscbsModel class

Description

Package: aroma.cn
Class PairedPscbsModel

Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--PairedPscbsModel

Directly known subclasses:

public static class PairedPscbsModel
extends ParametersInterface

This class represents the Paired PSCBS method [1], which segments matched tumor-normal parental copy-number data into piecewise constant segments.

Usage

PairedPscbsModel(dsT=NULL, dsN=NULL, tags="*", ..., dropTcnOutliers=TRUE,
  gapMinLength=1e+06, seed=NULL)
PairedPscbsModel(dsT=NULL, dsN=NULL, tags="*", ..., dropTcnOutliers=TRUE,
  gapMinLength=1e+06, seed=NULL)

Arguments

`dsT`, `dsN`	The tumor and the normal `AromaUnitPscnBinarySet`.
`tags`	Tags added to the output data sets.
`...`	(Optional) Additional arguments passed to `segmentByPairedPSCBS`.
`dropTcnOutliers`	If `TRUE`, then TCN outliers are dropped using `dropSegmentationOutliers`.
`gapMinLength`	Genomic regions with no data points that are of this length and greater are considered to be "gaps" and are ignored in the segmentation. If +`Inf`, no gaps are identified.
`seed`	An optional `integer` specifying the random seed to be used in the segmentation. Seed needs to be set for exact numerical reproducibility.

Fields and Methods

Methods:

	`fit`	-
	`getChipType`	-
	`getChromosomes`	-
	`getDataSets`	-
	`getFullName`	-
	`getName`	-
	`getNormalDataSet`	-
	`getOutputDataSet`	-
	`getTags`	-
	`getTumorDataSet`	-
	`indexOf`	-
	`nbrOfFiles`	-
	`setTags`	-

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

References

[1] ...

Examples

## Not run: 
  dataSet <- "GSE12702"
tags <- "ASCRMAv2"
chipType <- "Mapping250K_Nsp"
ds <- AromaUnitPscnBinarySet$byName(dataSet, tags=tags, chipType=chipType)
print(ds)

# Extract tumors and normals
idxs <- seq(from=1, to=nbrOfFiles(ds), by=2)
dsT <- extract(ds, idxs);
idxs <- seq(from=2, to=nbrOfFiles(ds), by=2)
dsN <- extract(ds, idxs);

# Setup Paired PSCBS model
seg <- PairedPscbsModel(dsT=dsT, dsN=dsN)
print(seg)

# Segment all tumor-normal pairs
fit(seg, verbose=-10)


## End(Not run)## Not run: 
  dataSet <- "GSE12702"
tags <- "ASCRMAv2"
chipType <- "Mapping250K_Nsp"
ds <- AromaUnitPscnBinarySet$byName(dataSet, tags=tags, chipType=chipType)
print(ds)

# Extract tumors and normals
idxs <- seq(from=1, to=nbrOfFiles(ds), by=2)
dsT <- extract(ds, idxs);
idxs <- seq(from=2, to=nbrOfFiles(ds), by=2)
dsN <- extract(ds, idxs);

# Setup Paired PSCBS model
seg <- PairedPscbsModel(dsT=dsT, dsN=dsN)
print(seg)

# Segment all tumor-normal pairs
fit(seg, verbose=-10)


## End(Not run)

The PrincipalCurveNormalization class

Description

Package: aroma.cn
Class PrincipalCurveNormalization

Object
~~|
~~+--AbstractCurveNormalization
~~~~~~~|
~~~~~~~+--PrincipalCurveNormalization

Directly known subclasses:

public static class PrincipalCurveNormalization
extends AbstractCurveNormalization

Usage

PrincipalCurveNormalization(..., subset=1/20)
PrincipalCurveNormalization(..., subset=1/20)

Arguments

`...`	Arguments passed to `AbstractCurveNormalization`.
`subset`	A `double` in (0,1] specifying the fraction of the `subsetToFit` to be used for fitting. Since the fit function for this class is rather slow, the default is to use a 1/20:th of the default data points.

Fields and Methods

Methods:
No methods defined.

Methods inherited from AbstractCurveNormalization:
as.character, backtransformOne, fitOne, getAsteriskTags, getDataSets, getFullName, getInputDataSet, getName, getOutputDataSet, getPairedDataSet, getPath, getRootPath, getSubsetToFit, getTags, getTargetDataSet, nbrOfFiles, process, setTags

Author(s)

Henrik Bengtsson

The TotalCnBinnedSmoothing class

Description

Package: aroma.cn
Class TotalCnBinnedSmoothing

Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--AromaTransform
~~~~~~~~~~~~|
~~~~~~~~~~~~+--TotalCnSmoothing
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--TotalCnBinnedSmoothing

Directly known subclasses:

public static class TotalCnBinnedSmoothing
extends TotalCnSmoothing

Usage

TotalCnBinnedSmoothing(..., robust=FALSE)
TotalCnBinnedSmoothing(..., robust=FALSE)

Arguments

`...`	Arguments passed to `TotalCnSmoothing`.
`robust`	If `TRUE`, a robust smoother is used, otherwise not.

Details

Note that dsS <- TotalCnBinnedSmoothing(ds, targetUgp=ugp) where ugp <- getAromaUgpFile(ds) returns a data set with an identical set of loci as the input data set and identical signals as the input ones, except for loci with duplicated positions. If all loci have unique positions, the the output is identical to the input.

Fields and Methods

Methods:
No methods defined.

Methods inherited from TotalCnSmoothing:
getAsteriskTags, getOutputDataSet0, getOutputFileClass, getOutputFileExtension, getOutputFileSetClass, getOutputFiles, getParameters, getPath, getRootPath, getTargetPositions, getTargetUgpFile, process, smoothRawCopyNumbers

Methods inherited from AromaTransform:
as.character, findFilesTodo, getAsteriskTags, getExpectedOutputFiles, getExpectedOutputFullnames, getFullName, getInputDataSet, getName, getOutputDataSet, getOutputDataSet0, getOutputFiles, getPath, getRootPath, getTags, isDone, process, setTags

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

Author(s)

Henrik Bengtsson

The TotalCnKernelSmoothing class

Description

Package: aroma.cn
Class TotalCnKernelSmoothing

Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--AromaTransform
~~~~~~~~~~~~|
~~~~~~~~~~~~+--TotalCnSmoothing
~~~~~~~~~~~~~~~~~|
~~~~~~~~~~~~~~~~~+--TotalCnKernelSmoothing

Directly known subclasses:

public static class TotalCnKernelSmoothing
extends TotalCnSmoothing

Usage

TotalCnKernelSmoothing(..., kernel=c("gaussian", "uniform"), bandwidth=50000, censorH=3,
  robust=FALSE)
TotalCnKernelSmoothing(..., kernel=c("gaussian", "uniform"), bandwidth=50000, censorH=3,
  robust=FALSE)

Arguments

`...`	Arguments passed to `TotalCnSmoothing`.
`kernel`	A `character` string specifying the type of kernel to be used.
`bandwidth`	A `double` specifying the bandwidth of the smoothing.
`censorH`	A positive `double` specifying the bandwidth threshold where values outside are ignored (zero weight).
`robust`	If `TRUE`, a robust smoother is used, otherwise not.

Fields and Methods

Methods:
No methods defined.

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

Author(s)

Henrik Bengtsson

The abstract TotalCnSmoothing class

Description

Package: aroma.cn
Class TotalCnSmoothing

Object
~~|
~~+--ParametersInterface
~~~~~~~|
~~~~~~~+--AromaTransform
~~~~~~~~~~~~|
~~~~~~~~~~~~+--TotalCnSmoothing

Directly known subclasses:
TotalCnBinnedSmoothing, TotalCnKernelSmoothing

public abstract static class TotalCnSmoothing
extends AromaTransform

Usage

TotalCnSmoothing(dataSet=NULL, ..., targetUgp=NULL,
  .reqSetClass="AromaUnitTotalCnBinarySet")
TotalCnSmoothing(dataSet=NULL, ..., targetUgp=NULL,
  .reqSetClass="AromaUnitTotalCnBinarySet")

Arguments

`dataSet`	An `AromaUnitTotalCnBinarySet`.
`...`	Arguments passed to `AromaTransform`.
`targetUgp`	An `AromaUgpFile` specifying the target loci for which smoothed copy-number are generated.
`.reqSetClass`	(internal only)

Fields and Methods

Methods:

	`getTargetUgpFile`	-
	`process`	-

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

Author(s)

Henrik Bengtsson

The TumorBoostNormalization class

Description

Package: aroma.cn
Class TumorBoostNormalization

Object
~~|
~~+--TumorBoostNormalization

Directly known subclasses:

public static class TumorBoostNormalization
extends Object

TumorBoost is normalization method that normalizes the allele B fractions of a tumor sample given the allele B fractions and genotype calls for a matched normal. The method is a single-sample (single-pair) method. It does not require total copy number estimates. The normalization is done such that the total copy number is unchanged afterwards.

Usage

TumorBoostNormalization(dsT=NULL, dsN=NULL, gcN=NULL, flavor=c("v4", "v3", "v2", "v1"),
  preserveScale=TRUE, collapseHomozygous=FALSE, tags="*", ...)
TumorBoostNormalization(dsT=NULL, dsN=NULL, gcN=NULL, flavor=c("v4", "v3", "v2", "v1"),
  preserveScale=TRUE, collapseHomozygous=FALSE, tags="*", ...)

Arguments

`dsT`	An `AromaUnitFracBCnBinarySet` of tumor samples.
`dsN`	An `AromaUnitFracBCnBinarySet` of match normal samples.
`gcN`	An `AromaUnitGenotypeCallSet` of genotypes for the normals.
`flavor`	A `character` string specifying the type of correction applied.
`preserveScale`	If `TRUE`, SNPs that are heterozygous in the matched normal are corrected for signal compression using an estimate of signal compression based on the amount of correction performed by TumorBoost on SNPs that are homozygous in the matched normal.
`collapseHomozygous`	If `TRUE`, SNPs that are homozygous in the matched normal are also called homozygous in the tumor, that is, it's allele B fraction is collapsed to either 0 or 1. If `FALSE`, the homozygous values are normalized according the model. [NOT USED YET]
`tags`	(Optional) Sets the tags for the output data sets.
`...`	Not used.

Fields and Methods

Methods:

	`getFullName`	-
	`getInputDataSet`	-
	`getName`	-
	`getNormalDataSet`	-
	`getNormalGenotypeCallSet`	-
	`getOutputDataSet`	-
	`getTags`	-
	`nbrOfFiles`	-
	`process`	-
	`setTags`	-

Author(s)

Henrik Bengtsson, Pierre Neuvial

The XYCurveNormalization class

Description

Package: aroma.cn
Class XYCurveNormalization

Object
~~|
~~+--AbstractCurveNormalization
~~~~~~~|
~~~~~~~+--XYCurveNormalization

Directly known subclasses:

public static class XYCurveNormalization
extends AbstractCurveNormalization

Usage

XYCurveNormalization(...)
XYCurveNormalization(...)

Arguments

...

Arguments passed to AbstractCurveNormalization.

Fields and Methods

Methods:
No methods defined.

Author(s)

Henrik Bengtsson

Package 'aroma.cn'

Help Index

Package aroma.cn

Description

Installation and updates

To get started

License

Author(s)

References

The AbstractCurveNormalization class

Description

Usage

Arguments

Fields and Methods

Author(s)

Calls XX or XY from ChrX allele B fractions of a normal sample

Description

Usage

Arguments

Value

Missing and non-finite values

Author(s)

See Also

The MultiSourceCopyNumberNormalization class

Description

Usage

Arguments

Details

Fields and Methods

Different preprocessing methods normalize ChrX & ChrY differently

Author(s)

References

The PairedPscbsModel class

Description

Usage

Arguments

Fields and Methods

References

See Also

Examples

The PrincipalCurveNormalization class

Description

Usage

Arguments

Fields and Methods

Author(s)

The TotalCnBinnedSmoothing class

Description

Usage

Arguments

Details

Fields and Methods

Author(s)

The TotalCnKernelSmoothing class

Description

Usage

Arguments

Fields and Methods

Author(s)

The abstract TotalCnSmoothing class

Description

Usage

Arguments

Fields and Methods

Author(s)

The TumorBoostNormalization class

Description

Usage

Arguments

Fields and Methods

Author(s)

The XYCurveNormalization class

Description

Usage

Arguments

Fields and Methods

Author(s)