Scoring functions used by MRMScoring. More...

Classes
struct	XCorrArrayType

Typedefs
Type defs and helper structures
typedef std::pair< int, double >	XCorrEntry
	Cross Correlation array contains (lag,correlation) pairs. More...

Functions
Helper functions
OPENSWATHALGO_DLLAPI double	NormalizedManhattanDist (double x[], double y[], int n)
	Calculate the normalized Manhattan distance between two arrays. More...

OPENSWATHALGO_DLLAPI double	RootMeanSquareDeviation (double x[], double y[], int n)
	Calculate the RMSD (root means square deviation) More...

OPENSWATHALGO_DLLAPI double	SpectralAngle (double x[], double y[], int n)
	Calculate the Spectral angle (acosine of the normalized dotproduct) More...

OPENSWATHALGO_DLLAPI XCorrArrayType	calcxcorr_legacy_mquest_ (std::vector< double > &data1, std::vector< double > &data2, bool normalize)

OPENSWATHALGO_DLLAPI XCorrArrayType	normalizedCrossCorrelation (std::vector< double > &data1, std::vector< double > &data2, const int maxdelay, const int lag)

OPENSWATHALGO_DLLAPI XCorrArrayType	normalizedCrossCorrelationPost (std::vector< double > &normalized_data1, std::vector< double > &normalized_data2, const int maxdelay, const int lag)
	Calculate crosscorrelation on std::vector data that is already normalized. More...

OPENSWATHALGO_DLLAPI XCorrArrayType	calculateCrossCorrelation (const std::vector< double > &data1, const std::vector< double > &data2, const int maxdelay, const int lag)
	Calculate crosscorrelation on std::vector data without normalization. More...

OPENSWATHALGO_DLLAPI XCorrArrayType::const_iterator	xcorrArrayGetMaxPeak (const XCorrArrayType &array)
	Find best peak in an cross-correlation (highest apex) More...

OPENSWATHALGO_DLLAPI void	standardize_data (std::vector< double > &data)
	Standardize a vector (subtract mean, divide by standard deviation) More...

OPENSWATHALGO_DLLAPI void	normalize_sum (double x[], unsigned int n)
	Divide each element of x by the sum of the vector. More...

OPENSWATHALGO_DLLAPI void	computeAndAppendRank (const std::vector< double > &v, std::vector< unsigned int > &ranks)
	Compute rank of vector elements and append it to `ranks`. More...

OPENSWATHALGO_DLLAPI void	computeRankVector (const std::vector< std::vector< double >> &intensity, std::vector< std::vector< unsigned int >> &ranks)
	Compute rank of vector elements for each row in a 2D array. More...

OPENSWATHALGO_DLLAPI double	rankedMutualInformation (std::vector< unsigned int > &ranked_data1, std::vector< unsigned int > &ranked_data2)
	Estimate mutual information between two vectors of ranks. More...

Detailed Description

Scoring functions used by MRMScoring.

Many helper functions to calculate cross-correlations between data

Typedef Documentation

◆ XCorrEntry

typedef std::pair<int, double> XCorrEntry

Cross Correlation array contains (lag,correlation) pairs.

Function Documentation

◆ calculateCrossCorrelation()

OPENSWATHALGO_DLLAPI XCorrArrayType OpenSwath::Scoring::calculateCrossCorrelation	(	const std::vector< double > &	data1,
		const std::vector< double > &	data2,
		const int	maxdelay,
		const int	lag
	)

Calculate crosscorrelation on std::vector data without normalization.

◆ calcxcorr_legacy_mquest_()

OPENSWATHALGO_DLLAPI XCorrArrayType OpenSwath::Scoring::calcxcorr_legacy_mquest_	(	std::vector< double > &	data1,
		std::vector< double > &	data2,
		bool	normalize
	)

Calculate crosscorrelation on std::vector data - Deprecated! Legacy code, this is a 1:1 port of the function from mQuest

◆ computeAndAppendRank()

OPENSWATHALGO_DLLAPI void OpenSwath::Scoring::computeAndAppendRank	(	const std::vector< double > &	v,
		std::vector< unsigned int > &	ranks
	)

Compute rank of vector elements and append it to ranks.

Referenced by MRMTransitionGroupPicker::pickFragmentChromatograms().

◆ computeRankVector()

OPENSWATHALGO_DLLAPI void OpenSwath::Scoring::computeRankVector	(	const std::vector< std::vector< double >> &	intensity,
		std::vector< std::vector< unsigned int >> &	ranks
	)

Compute rank of vector elements for each row in a 2D array.

◆ normalize_sum()

OPENSWATHALGO_DLLAPI void OpenSwath::Scoring::normalize_sum	(	double	x[],
		unsigned int	n
	)

Divide each element of x by the sum of the vector.

◆ normalizedCrossCorrelation()

OPENSWATHALGO_DLLAPI XCorrArrayType OpenSwath::Scoring::normalizedCrossCorrelation	(	std::vector< double > &	data1,
		std::vector< double > &	data2,
		const int	maxdelay,
		const int	lag
	)

Calculate crosscorrelation on std::vector data (which is first normalized) NOTE: this replaces calcxcorr

Referenced by MRMTransitionGroupPicker::computeQuality_().

◆ normalizedCrossCorrelationPost()

OPENSWATHALGO_DLLAPI XCorrArrayType OpenSwath::Scoring::normalizedCrossCorrelationPost	(	std::vector< double > &	normalized_data1,
		std::vector< double > &	normalized_data2,
		const int	maxdelay,
		const int	lag
	)

Calculate crosscorrelation on std::vector data that is already normalized.

◆ NormalizedManhattanDist()

OPENSWATHALGO_DLLAPI double OpenSwath::Scoring::NormalizedManhattanDist	(	double	x[],
		double	y[],
		int	n
	)

Calculate the normalized Manhattan distance between two arrays.

Equivalent to the function "delta_ratio_sum" from mQuest to calculate similarity between library intensity and experimental ones.

The delta_ratio_sum is calculated as follows:

$d = \sqrt{\frac{1}{N} \sum_{i=0}^N |\frac{x_i}{\mu_x} - \frac{y_i}{\mu_y}|) }$

◆ rankedMutualInformation()

OPENSWATHALGO_DLLAPI double OpenSwath::Scoring::rankedMutualInformation	(	std::vector< unsigned int > &	ranked_data1,
		std::vector< unsigned int > &	ranked_data2
	)

Estimate mutual information between two vectors of ranks.

Referenced by MRMTransitionGroupPicker::pickFragmentChromatograms().

◆ RootMeanSquareDeviation()

OPENSWATHALGO_DLLAPI double OpenSwath::Scoring::RootMeanSquareDeviation	(	double	x[],
		double	y[],
		int	n
	)

Calculate the RMSD (root means square deviation)

The RMSD is calculated as follows:

$RMSD = \sqrt{\frac{1}{N} \sum_{i=0}^N (x_i - y_i)^2 }$

◆ SpectralAngle()

OPENSWATHALGO_DLLAPI double OpenSwath::Scoring::SpectralAngle	(	double	x[],
		double	y[],
		int	n
	)

Calculate the Spectral angle (acosine of the normalized dotproduct)

The spectral angle is calculated as follows:

$\theta = acos \left( \frac{\sum_{i=0}^N (x_i * y_i))}{\sqrt{\sum_{i=0}^N (x_i * x_i) \sum_{i=0}^N (y_i * y_i)} } \right)$

◆ standardize_data()

OPENSWATHALGO_DLLAPI void OpenSwath::Scoring::standardize_data ( std::vector< double > & data )

Standardize a vector (subtract mean, divide by standard deviation)

◆ xcorrArrayGetMaxPeak()

OPENSWATHALGO_DLLAPI XCorrArrayType::const_iterator OpenSwath::Scoring::xcorrArrayGetMaxPeak ( const XCorrArrayType & array )

Find best peak in an cross-correlation (highest apex)