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:

1. ...

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, ...)

Arguments

Fields and Methods

Methods:

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

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="*", ...)

Arguments

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:

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

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

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.

Non-documented objects

Description

This page contains aliases for all "non-documented" objects that R CMD check detects in this package.

Almost all of them are generic functions that have specific document for the corresponding method coupled to a specific class. Other functions are re-defined by setMethodS3() to default methods. Neither of these two classes are non-documented in reality. The rest are deprecated methods.

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)

Arguments

Fields and Methods

Methods:

Methods inherited from ParametersInterface:
getParameterSets, getParameters, getParametersAsString

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

References

[1] ...

See Also

...

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)

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)

Arguments

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

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

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)

Arguments

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

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

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)

Arguments

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

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

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")

Arguments

Fields and Methods

Methods:

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

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

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="*", ...)

Arguments

Fields and Methods

Methods:

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, 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(...)

Arguments

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

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 regions that are in allelic balance

Description

Calls regions that are in allelic balance from the allele B fractions (BAF).

Usage

## S3 method for class 'PairedPSCBS'
callAllelicBalanceByBAFs(fit, maxScore="auto", ..., force=FALSE, cache=FALSE,
  verbose=FALSE)

Arguments

Value

Returns a PairedPSCBS fit object where columns for allelic imbalance scores and p-values as well as allelic balance calls are added.

Author(s)

Henrik Bengtsson, Pierre Neuvial

See Also

Internally, testAllelicBalanceByBAFs() is used.

Note that this AB caller differs from the default one in the PSCBS package, cf. callAB.PairedPSCBS.

Examples

if (Sys.getenv("_R_CHECK_FULL_") != "" && require("PSCBS")) {

# Load example ASCN data
data <- PSCBS::exampleData("paired.chr01");

# PSCBS segmentation
fit <- segmentByPairedPSCBS(data, verbose=-10);

# Normalize
fitN <- normalizeBAFsByRegions(fit, verbose=-10);

fitN2 <- callAllelicBalanceByBAFs(fitN, verbose=-10);
print(fitN2);

} # if (require("PSCBS"))

Calls regions that are copy neutral

Description

Calls regions that are copy neutral from the allele B fractions (BAF).

Usage

## S3 method for class 'PairedPSCBS'
callCopyNeutralRegions(fit, ...)

Arguments

Value

Returns a PairedPSCBS fit object where a column with the copy-neutral call.

Author(s)

Henrik Bengtsson

Examples

if (Sys.getenv("_R_CHECK_FULL_") != "" && require("PSCBS")) {

# Load example ASCN data
data <- PSCBS::exampleData("paired.chr01");

# PSCBS segmentation
fit <- segmentByPairedPSCBS(data, verbose=-10);

fit <- bootstrapTCNandDHByRegion(fit, verbose=-10);

fitC <- callCopyNeutralRegions(fit, verbose=-10);
print(fitC);

# Normalize
fitN <- normalizeBAFsByRegions(fitC, verbose=-10);

} # if (require("PSCBS"))

Calls the peaks in peaks-and-valley estimates

Description

Calls the peaks in peaks-and-valley estimates to a set of known state.

Usage

## S3 method for class 'PeaksAndValleys'
callPeaks(fit, expected=c(-1/2, -1/4, 0, +1/4, +1/2) * pi, flavor=c("decreasing", "all"),
  verbose=FALSE, ...)

Arguments

Value

Returns a Kx(C+2) data.frame.

Flavors

If flavor == "all", each peak is called to the state with the closest expected value. If flavor == "decreasing", the strongest peak is called to the state with the closest expected value, then the second strongest peak is called analogously to one of the remaining states, and so on.

Author(s)

Henrik Bengtsson, Pierre Neuvial

See Also

To get peaks-and-valley estimates, use findPeaksAndValleys.

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)

Arguments

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

See Also

Internally findPeaksAndValleys is used to identify the thresholds.

Simple creation of a CopyNumberRegions object

Description

Simple creation of a CopyNumberRegions object containing a single region.

Usage

cnr(start, stop=NULL, mean=1, chromosome=1, xScale=1e+06, ...)

Arguments

Value

Return an CopyNumberRegions object.

Author(s)

Henrik Bengtsson

Examples

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Build up a tumor CN profile
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
pT <- cnr(1,1000, 2) +
     cnr(400,500) +
     cnr(600,800) +
     cnr(600,700) +
     cnr(100,200) - cnr(850,900);
print(pT);


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Simulate CN signals from this profile
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
cn <- simulateRawCopyNumbers(pT, n=2000, sd=1/2);
print(cn);


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Plot
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
plot(cn, col="#aaaaaa", ylim=c(0,5));
drawLevels(pT, col="#ff0000", lwd=3);

Gets a list of data files for a particular name across several data sets

Description

Gets a list of data files for a particular name across several data sets.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
extractTupleOfDataFiles(this, dsList, name, ..., na.rm=FALSE, verbose=FALSE)

Arguments

Value

Returns a list of K AromaUnitTotalCnBinarySet:s.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Finds all possible atomic regions

Description

Finds all possible atomic regions of a certain length.

Usage

## S3 method for class 'CopyNumberRegions'
findAtomicAberrations(cnr, data, H=1, alpha=0.02, ..., verbose=FALSE)

Arguments

Details

An aberration of length H is defined as any H consecutive segments. Each aberrations has two flanking segments on each side. Regardless of the content of the aberration, it is possible to test the null-hypothesis that the two flanking segments are equal or not. The two flanking regions are said to be equal, if the null-hypothesis of being equal is not rejected. If the two flanking regions are called equal, then the contained abberation (of length H) is called atomic, otherwise not.

For consistency one may also define atomic aberrations of length H=0. Consider that an imaginary aberration of zero length splits a single segment into to flanking segments. Then by construction those two segments are equal. The case where H=0 is still not implemented.

Value

Returns a data.frame with K rows, where K >= 0 is the number of atomic aberrations found.

Author(s)

Henrik Bengtsson

See Also

...

Examples

library("aroma.cn")

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Local functions
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
xAxisBar <- function(x0, x1=NULL, y=par("usr")[3], lwd=4, col=par("col"),
                     length=0.04, xpd=FALSE, ...) {
  if (is.null(x1)) {
    x1 <- x0[2]
    x0 <- x0[1]
  }
  arrows(x0=x0, x1=x1, y0=y, code=3, angle=90, length=length, lwd=lwd,
         col=col, xpd=xpd, lend=2, ...)
} # xAxisBar()


verbose <- Arguments$getVerbose(-8, timestamp=TRUE)

# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Simulating copy-number data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Build up CN profile generation by generation
pT <- cnr(1,2000, 2) +
     cnr(1000,1500) +
     cnr(1000,1250) +
     cnr(1650,1800) +
     cnr(200,300) - cnr(650,800)
print(pT)

# Simulate data from the track
cn <- simulateRawCopyNumbers(pT, n=2000, sd=1/2)
print(cn)


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Find atomic aberrations
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
subplots(4, ncol=1)
par(mar=c(2,4,1,1)+0.1)

# Plot observed and true signals
plot(cn, col="#aaaaaa", ylim=c(0,5))
drawLevels(pT, col="white", lwd=4, lty=1)
drawLevels(pT, col="black", lwd=3, lty=6)
stext(side=3, pos=1, line=-1, "\"H=0\"", cex=1.5)

for (H in 1:3) {
  plot(cn, col="#aaaaaa", ylim=c(0,5))
  drawLevels(pT, col="white", lwd=4, lty=1)
  drawLevels(pT, col="black", lwd=3, lty=6)
  col <- H+1
  stext(side=3, pos=1, line=-1, sprintf("H=%d", H), cex=1.5, col=col)
  par <- par("usr")
  y0 <- par("usr")[3]
  y1 <- par("usr")[4]
  dy <- 0.05*(y1-y0)

  fit <- findAtomicAberrations(cnr=pT, data=cn, H=H, verbose=verbose)
  df <- fit$res

  for (kk in seq_len(nrow(df))) {
    dfKK <- df[kk,]
    segments <- as.integer(dfKK[,c("firstRegion", "lastRegion")])
    segments <- segments[1]:segments[2]
    xRange <- as.double(dfKK[,c("start", "stop")])
    cnrKK <- subset(pT, subset=segments)
    drawLevels(cnrKK, col=col, lwd=3)
    x <- xRange/1e6
    y <- y0 + 0.2*dy
    xAxisBar(x0=x[1], x1=x[2], y=y, col=col)
    box()
  }
} # for (H ...)

Fits the multi-source model for one sample

Description

Fits the multi-source model for one sample. The model is fitted on the subset of units returned by *getSubsetToFit().

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
fitOne(this, dfList, ..., force=FALSE, .retData=FALSE, verbose=FALSE)

Arguments

Value

Returns a list of transforms.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Gets the names of all unique samples across all sources

Description

Gets the names of all unique samples across all sources.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
getAllNames(this, ...)

Arguments

Value

Returns a character vector.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Gets the UGP file specifying the common set of loci to normalize at

Description

Gets the UGP file specifying the common set of loci to normalize at.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
getFitAromaUgpFile(this, ...)

Arguments

Value

Returns a AromaUgpFile.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Gets the list of data sets to be normalized

Description

Gets the list of data sets to be normalized.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
getInputDataSets(this, ...)

Arguments

Value

Returns a list.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Gets the data sets smoothed toward the UGP file

Description

Gets the data sets smoothed toward the UGP file.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
getSmoothedDataSets(this, ..., verbose=FALSE)

Arguments

Details

This method smooth each data set, each array, and each chromosome toward the target (smoothing) UGP file independently of everything else.

The resulting data sets are stored in a separate location where they will be located automatically in future sessions.

Value

Returns a list of K AromaUnitTotalCnBinarySet:s.

Author(s)

Henrik Bengtsson

See Also

*getFitAromaUgpFile(). For more information see MultiSourceCopyNumberNormalization.

Gets subset of (smoothing) units for fitting the model

Description

Gets subset of (smoothing) units for fitting the model.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
getSubsetToFit(this, ..., verbose=FALSE)

Arguments

Value

Returns an integer vector of unit indices.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Normalizes allele B fractions (BAFs) based on region-based PSCN estimates

Description

Normalizes allele B fractions (BAFs) based on region-based PSCN estimates as given by the PSCBS segmentation method.

Usage

## S3 method for class 'PairedPSCBS'
normalizeBAFsByRegions(fit, by=c("betaTN", "betaT"), ..., force=FALSE, cache=TRUE,
  verbose=FALSE)

Arguments

Details

Note that his normalization method depends on the segmentation results. Hence, it recommended not to resegment the normalized signals returned by this, because such a segmentation will be highly dependent on the initial segmentation round.

Value

Returns a PairedPSCBS fit object where the region-level decrease-in-heterozygosity (DH) means have been normalized, as well as the locus-specific tumor allele B fractions.

Author(s)

Henrik Bengtsson, Pierre Neuvial

See Also

Internally normalizeMirroredBAFsByRegions is used.

Examples

library("aroma.cn")

if (Sys.getenv("_R_CHECK_FULL_") != "" && require("PSCBS")) {

# Load example ASCN data
data <- PSCBS::exampleData("paired.chr01")
R.oo::attachLocally(data)

# AD HOC: Robustification
CT[CT < 0] <- 0
CT[CT > 30] <- 30

# PSCBS segmentation
fit <- segmentByPairedPSCBS(CT, betaT=betaT, betaN=betaN, x=x, verbose=-10)

# Normalize
fitN <- normalizeBAFsByRegions(fit, verbose=-10)

devSet("tracks")
subplots(10, ncol=2, byrow=FALSE)
par(mar=c(1,3.5,1,0.5)+1)
plot(fit, subplots=TRUE)
plot(fitN, subplots=TRUE)


devSet("C1C2")
Clim <- c(0,4)
subplots(4, ncol=2, byrow=TRUE)
par(mar=c(1,3.5,1,0.5)+1)
plotC1C2(fit, Clim=Clim)
linesC1C2(fit)
title(main="(C1,C2)")

plotC1C2(fitN, Clim=Clim)
linesC1C2(fitN)
title(main="(C1,C2) - adjusted")

plotC1C2(fit, col="gray", Clim=Clim)
linesC1C2(fit, col="gray")
pointsC1C2(fitN)
linesC1C2(fitN)
title(main="(C1,C2) - both")

} # if (require("PSCBS"))

Normalizes region-level mirrored allele B fractions (mBAFs)

Description

Normalizes region-level mirrored allele B fractions (mBAFs) for heterozygous and homozygous SNPs by rescaling both equally much such that the normalized homozygous mBAFs become exactly or close to their expected values (which is at zero and one).

Usage

## S3 method for class 'matrix'
normalizeMirroredBAFsByRegions(data, flavor=c("plain", "total"), ..., verbose=FALSE)

Arguments

Details

The mBAFs for heterozygous SNPs are also known as the Decrease in Heterozygosity signals (DHs).

Value

A numeric matrix of the same dimensions as argument data.

Author(s)

Henrik Bengtsson, Pierre Neuvial

Normalizes data in K dimensions using principal curves

Description

Normalizes data in K dimensions using principal curves such that afterward the data cluster (approximately linearly) along the diagonal (in K dimensions).

Usage

## S3 method for class 'matrix'
normalizePrincipalCurve(x, ..., center=TRUE, returnFit=FALSE)

Arguments

Value

Returns an NxK matrix.

Author(s)

Henrik Bengtsson

References

[1] Hastie, T. and Stuetzle, W, Principal Curves, JASA, 1989.

See Also

fitPrincipalCurve and backtransformPrincipalCurve.

Normalizes all samples

Description

Normalizes all samples.

Usage

## S3 method for class 'MultiSourceCopyNumberNormalization'
process(this, ..., force=FALSE, verbose=FALSE)

Arguments

Value

Returns a list of K AromaUnitTotalCnBinarySet:s.

Author(s)

Henrik Bengtsson

See Also

For more information see MultiSourceCopyNumberNormalization.

Tests for allelic balance in a genomic region

Description

Tests for allelic balance in a genomic region.

Usage

## Default S3 method:
testAllelicBalanceByBAFs(betaT, muN, flavor=c("var", "mean"), ..., verbose=FALSE)

Arguments

Value

A list of class "htest".

Author(s)

Henrik Bengtsson, Pierre Neuvial

Examples

if (Sys.getenv("_R_CHECK_FULL_") != "" && require("PSCBS")) {

# Load example ASCN data
data <- PSCBS::exampleData("paired.chr01");

# PSCBS segmentation
fit <- segmentByPairedPSCBS(data, verbose=-10);

# Normalize
fitN <- normalizeBAFsByRegions(fit, verbose=-10);

fitN2 <- callAllelicBalanceByBAFs(fitN, verbose=-10);
print(fitN2);

} # if (require("PSCBS"))