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• CanvasDiploidCaller

C# Класс CanvasDiploidCaller.CanvasDiploidCaller

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Открытые свойства

Свойство	Тип	Описание
CoverageWeighting	double
IsDbsnpVcf	bool
TempFolder	string

Защищенные свойства (Protected)

Свойство	Тип	Описание
MeanCoverage	float
MedianHetSnpsDistance	int
MinimumVariantFrequenciesForInformativeSegment	int

Открытые методы

Метод	Описание
AggregateCounts ( List &segments ) : float[]
AggregateVariantCoverage ( List &segments ) : int
CallVariants ( string variantFrequencyFile, string inFile, string outFile, string ploidyBedPath, string referenceFolder, string sampleName, string truthDataPath ) : int
InitializePloidies ( ) : void	Setup: Model various copy ploidies.

Защищенные методы

Метод	Описание
GetKnownCNForSegment ( CanvasSegment segment ) : int	Check whether we know the CN for this segment. Look for a known-CN interval that covers (at least half of) this segment. Return -1 if we don't know its CN.
GetProjectedMeanCoverage ( double diploidCoverage ) : double[]	Compute the expected bin counts for each copy number, given a specified bin count for CN=2 regions
InitializeModelPoints ( CoverageModel model ) : List

Приватные методы

Метод	Описание
AssignPloidyCallsDistance ( CoverageModel model, List segments, int medianVariantCoverage ) : void
AssignPloidyCallsGaussianMixture ( ) : void	Assign a SegmentPloidy to each CanvasSegment, based on which model matches this segment best:
FitGaussians ( CoverageModel model, List segments, string debugPath = null ) : double	Fit a Gaussian mixture model. Fix the means to the model MAF and Coverage and run the EM algorithm until convergence. Compute the empirical MAF and Coverage. Fix the means to the empirical MAF and Coverage and run the EM algorithm again until convergence. Always estimate the full covariance matrix?
GenerateReportVersusKnownCN ( ) : void	Generate a table listing segments (and several features), and noting which are accurate (copy number exactly matches truth set) or directionally accurate (copy number and truth set are both <2, both =2, or both >2) This table will become our collection of feature vectors for training q-scores!

Описание методов

Описание свойств