MRMRTNormalizer Class Reference

The MRMRTNormalizer will find retention time peptides in data. More...

#include <OpenMS/ANALYSIS/OPENSWATH/MRMRTNormalizer.h>

List of all members.

Static Public Member Functions
static int	outlier_candidate (std::vector< double > &x, std::vector< double > &y)
	This function computes a candidate outlier peptide by iteratively leaving one peptide out to find the one which results in the maximum R^2 of a first order linear regression of the remaining ones. The datapoints are submitted as two vectors of doubles (x- and y-coordinates).
static std::vector< std::pair < double, double > >	rm_outliers (std::vector< std::pair< double, double > > &pairs, double rsq_limit, double coverage_limit)
	This function removes potential outliers from a set of paired points. Two thresholds need to be defined, first a lower R^2 limit to accept the regression for the RT normalization and second, the lower limit of peptide coverage. The algorithms then selects candidate outlier peptides and applies the Chauvenet's criterion on the assumption that the residuals are normal distributed to determine whether the peptides can be removed. This is done iteratively until both limits are reached.
static double	chauvenet_probability (std::vector< double > &residuals, int pos)
	This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted.
static bool	chauvenet (std::vector< double > &residuals, int pos)
	This function computes Chauvenet's criterion for a vector and a value whose position is submitted.

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Detailed Description

The MRMRTNormalizer will find retention time peptides in data.

This tool will take a description of RT peptides and their normalized retention time to write out a transformation file on how to transform the RT space into the normalized space.

The principle is adapted from Escher et al. Escher, C. et al. (2012), Using iRT, a normalized retention time for more targeted measurement of peptides. Proteomics, 12: 1111-1121.

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Member Function Documentation

static bool chauvenet	(	std::vector< double > &	residuals,
		int	pos
	)			[static]

This function computes Chauvenet's criterion for a vector and a value whose position is submitted.

Returns:

TRUE, if Chauvenet's criterion is fullfilled and the outlier can be removed.

static double chauvenet_probability	(	std::vector< double > &	residuals,
		int	pos
	)			[static]

This function computes Chauvenet's criterion probability for a vector and a value whose position is submitted.

Returns:

Chauvenet's criterion probability

static int outlier_candidate	(	std::vector< double > &	x,
		std::vector< double > &	y
	)			[static]

This function computes a candidate outlier peptide by iteratively leaving one peptide out to find the one which results in the maximum R^2 of a first order linear regression of the remaining ones. The datapoints are submitted as two vectors of doubles (x- and y-coordinates).

Returns:

The position of the candidate outlier peptide as supplied by the vector is returned.

Exceptions:

Exception::UnableToFit

is thrown if fitting cannot be performed

static std::vector<std::pair<double, double> > rm_outliers	(	std::vector< std::pair< double, double > > &	pairs,
		double	rsq_limit,
		double	coverage_limit
	)			[static]

This function removes potential outliers from a set of paired points. Two thresholds need to be defined, first a lower R^2 limit to accept the regression for the RT normalization and second, the lower limit of peptide coverage. The algorithms then selects candidate outlier peptides and applies the Chauvenet's criterion on the assumption that the residuals are normal distributed to determine whether the peptides can be removed. This is done iteratively until both limits are reached.

Returns:

A vector of pairs is returned if the R^2 limit was reached without reaching the coverage limit. If the limits are reached, an exception is thrown.

Exceptions:

Exception::UnableToFit

is thrown if fitting cannot be performed