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• CanvasDiploidCaller
• CanvasDiploidCaller

C# 클래스 CanvasDiploidCaller.CanvasDiploidCaller

파일 보기 프로젝트 열기: Illumina/canvas 1 사용 예제들

공개 프로퍼티들

프로퍼티	타입	설명
CoverageWeighting	double
IsDbsnpVcf	bool
TempFolder	string

보호된 프로퍼티들

프로퍼티	타입	설명
MeanCoverage	float
MedianHetSnpsDistance	int
MinimumVariantFrequenciesForInformativeSegment	int

공개 메소드들

메소드	설명
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.

보호된 메소드들

메소드	설명
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

비공개 메소드들

메소드	설명
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!

메소드 상세

프로퍼티 상세