calmate (version 0.13.0)

CalMaTeCalibration: The CalMaTeCalibration class

Description

Package: calmate Class CalMaTeCalibration

Object ~~| ~~+--ParametersInterface ~~~~~~~| ~~~~~~~+--CalMaTeCalibration

Directly known subclasses:

public static class CalMaTeCalibration extends ParametersInterface

This class represents the CalMaTe method [1], which corrects for SNP effects in allele-specific copy-number estimates (ASCNs).

Usage

CalMaTeCalibration(data=NULL, tags="*", references=NULL, flavor=c("v2", "v1"), ...)

Arguments

data

A named list with data set named "total" and "fracB" where the former should be of class AromaUnitTotalCnBinarySet and the latter of class AromaUnitFracBCnBinarySet. The two data sets must be for the same chip type, have the same number of samples and the same sample names.

tags

Tags added to the output data sets.

references

An optional numeric vector specifying which samples should be as reference samples for estimating the model parameters. If NULL, all samples are used.

flavor

A character string specifying which flavor of the CalMaTe algorithm to use for fitting the model. See fitCalMaTeInternal for details.

...

Additional arguments passed to calmateByTotalAndFracB().

Fields and Methods

Methods:

	findUnitsTodo	-
	getDataSets	-
	getFullName	-
	getName	-
	getOutputDataSets	-
	getPath	-
	getReferences	-
	getRootPath	-
	getTags	-
	nbrOfFiles	-
	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

Reference samples

In order to estimate the calibration parameters, the model assumes that, for any given SNP, there are a majority of samples that are diploid at that SNP. Note that it does not have to be the same set of samples for all SNPs.

By using argument references, it is possible so specify which samples should be used when estimating the calibration parameters. This is useful when for instance there are several tumor samples with unknown properties as well as a set of normal samples that can be assumed to be diploid.

Theoretical, a minimum of three reference samples are needed in order for the model to be identifiable. If less, an error is thrown. However, in practice more reference samples should be used, that is, in the order of at least 6-10 reference samples with a diverse set of genotypes.

Flavors

For backward compatibility, we try to keep all major versions of the CalMaTe algorithm available. Older versions can be used by specifying argument flavor. For more information about the different flavors, see fitCalMaTeInternal.

References

[1] M. Ortiz-Estevez, A. Aramburu, H. Bengtsson, P. Neuvial and A. Rubio, CalMaTe: A method and software to improve allele-specific copy number of SNP arrays for downstream segmentation, Bioinformatics, 2012 [PMC3381965].

See Also

Low-level versions of the CalMaTe method is available via the calmateByThetaAB() and calmateByTotalAndFracB() methods.

For further information on the internal fit functions, see fitCalMaTeInternal.

Examples

# NOT RUN {
 
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# CRMAv2 - Preprocess raw Affymetrix data
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
library("aroma.affymetrix");  # Needed for CRMAv2
dataSet <- "Affymetrix_2006-TumorNormal";
chipType <- "Mapping250K_Nsp";
dsList <- doCRMAv2(dataSet, chipType=chipType, combineAlleles=FALSE,
                                             plm="RmaCnPlm", verbose=-10);
print(dsList);


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# CalMaTe - Post-calibration of ASCNs estimates
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
asn <- CalMaTeCalibration(dsList);
print(asn);

# For speed issues, we will here only process loci on Chromosome 17.
chr <- 17;
ugp <- getAromaUgpFile(dsList$total);
units <- getUnitsOnChromosome(ugp, chr);

dsNList <- process(asn, units=units, verbose=verbose);
print(dsNList);


# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Plot allele B fractions (before and after)
# - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
# Sample #1 and Chromosome 17
ii <- 1;

# Extract raw (TCN,BAF)
df <- getFile(dsList$total, ii);
dfR <- getAverageFile(dsList$total, verbose=verbose);
gamma <- extractRawCopyNumbers(df, logBase=NULL, chromosome=chr);
gammaR <- extractRawCopyNumbers(dfR, logBase=NULL, chromosome=chr);
gamma <- 2*divideBy(gamma, gammaR);
df <- getFile(dsList$fracB, ii);
beta <- extractRawAlleleBFractions(df, chromosome=chr);

# Extract calibrated (TCN,BAF)
dfN <- getFile(dsNList$fracB, ii);
betaN <- extractRawAlleleBFractions(dfN, chromosome=chr);
dfN <- getFile(dsNList$total, ii);
gammaN <- extractRawCopyNumbers(dfN, logBase=NULL, chromosome=chr);

# Plot
subplots(4, ncol=2, byrow=FALSE);
plot(beta);
title(sprintf("%s", getName(beta)));
plot(gamma);
plot(betaN);
title(sprintf("%s (CalMaTe)", getName(betaN)));
plot(gammaN);

# }