A variant of QT clustering for the detection of feature groups. More...

#include <OpenMS/ANALYSIS/MAPMATCHING/QTClusterFinder.h>

Inheritance diagram for QTClusterFinder:

Public Member Functions
	QTClusterFinder ()
	Constructor. More...

	~QTClusterFinder () override
	Destructor. More...

void	run (const std::vector< ConsensusMap > &input_maps, ConsensusMap &result_map) override
	Runs the algorithm on consensus maps. More...

void	run (const std::vector< FeatureMap > &input_maps, ConsensusMap &result_map)
	Runs the algorithm on feature maps. More...

Public Member Functions inherited from BaseGroupFinder
	BaseGroupFinder ()
	Default constructor. More...

	~BaseGroupFinder () override
	Destructor. More...

Public Member Functions inherited from DefaultParamHandler
	DefaultParamHandler (const String &name)
	Constructor with name that is displayed in error messages. More...

	DefaultParamHandler (const DefaultParamHandler &rhs)
	Copy constructor. More...

virtual	~DefaultParamHandler ()
	Destructor. More...

virtual DefaultParamHandler &	operator= (const DefaultParamHandler &rhs)
	Assignment operator. More...

virtual bool	operator== (const DefaultParamHandler &rhs) const
	Equality operator. More...

void	setParameters (const Param &param)
	Sets the parameters. More...

const Param &	getParameters () const
	Non-mutable access to the parameters. More...

const Param &	getDefaults () const
	Non-mutable access to the default parameters. More...

const String &	getName () const
	Non-mutable access to the name. More...

void	setName (const String &name)
	Mutable access to the name. More...

const std::vector< String > &	getSubsections () const
	Non-mutable access to the registered subsections. More...

Public Member Functions inherited from ProgressLogger
	ProgressLogger ()
	Constructor. More...

	~ProgressLogger ()
	Destructor. More...

	ProgressLogger (const ProgressLogger &other)
	Copy constructor. More...

ProgressLogger &	operator= (const ProgressLogger &other)
	Assignment Operator. More...

void	setLogType (LogType type) const
	Sets the progress log that should be used. The default type is NONE! More...

LogType	getLogType () const
	Returns the type of progress log being used. More...

void	startProgress (SignedSize begin, SignedSize end, const String &label) const
	Initializes the progress display. More...

void	setProgress (SignedSize value) const
	Sets the current progress. More...

void	endProgress () const
	Ends the progress display. More...

void	nextProgress () const
	increment progress by 1 (according to range begin-end) More...

Static Public Member Functions
static const String	getProductName ()
	Returns the name of the product. More...

static BaseGroupFinder *	create ()
	Returns an instance of this class. More...

Static Public Member Functions inherited from BaseGroupFinder
static void	registerChildren ()
	Register all derived classes here. More...

Protected Types
enum	{ RT = Peak2D::RT, MZ = Peak2D::MZ }

Private Types
typedef OpenMSBoost::unordered_map< std::pair< OpenMS::GridFeature , OpenMS::GridFeature >, double >	PairDistances
	Distances between pairs of grid features. More...

typedef OpenMSBoost::unordered_map< OpenMS::GridFeature , std::vector< QTCluster > >	ElementMapping
	Map to store which grid features are next to which clusters. More...

typedef HashGrid< OpenMS::GridFeature * >	Grid

Private Member Functions
double	getDistance_ (const OpenMS::GridFeature left, const OpenMS::GridFeature right)
	Calculates the distance between two grid features. More...

void	setParameters_ (double max_intensity, double max_mz)
	Sets algorithm parameters. More...

void	makeConsensusFeature_ (std::list< QTCluster > &clustering, ConsensusFeature &feature, ElementMapping &element_mapping, Grid &)
	Generates a consensus feature from the best cluster and updates the clustering. More...

void	computeClustering_ (Grid &grid, std::list< QTCluster > &clustering)
	Computes an initial QT clustering of the points in the hash grid. More...

template<typename MapType >
void	run_ (const std::vector< MapType > &input_maps, ConsensusMap &result_map)
	Runs the algorithm on feature maps or consensus maps. More...

template<typename MapType >
void	run_internal_ (const std::vector< MapType > &input_maps, ConsensusMap &result_map, bool do_progress)
	Runs the algorithm on feature maps or consensus maps (internal) More...

void	addClusterElements_ (int x, int y, const Grid &grid, QTCluster &cluster, const OpenMS::GridFeature *center_feature)
	Adds elements to the cluster based on the elements hashed in the grid. More...

Private Attributes
Size	num_maps_
	Number of input maps. More...

bool	use_IDs_
	Consider peptide identifications for grouping? More...

double	max_diff_rt_
	Maximum RT difference. More...

double	max_diff_mz_
	Maximum m/z difference. More...

int	nr_partitions_
	Maximum m/z difference. More...

FeatureDistance	feature_distance_
	Feature distance functor. More...

std::set< OpenMS::GridFeature * >	already_used_
	Set of features already used. More...

Additional Inherited Members
Public Types inherited from ProgressLogger
enum	LogType { CMD, GUI, NONE }
	Possible log types. More...

Protected Member Functions inherited from BaseGroupFinder
void	checkIds_ (const std::vector< ConsensusMap > &maps) const
	Checks if all file descriptions have disjoint map identifiers. More...

Protected Member Functions inherited from DefaultParamHandler
virtual void	updateMembers_ ()
	This method is used to update extra member variables at the end of the setParameters() method. More...

void	defaultsToParam_ ()
	Updates the parameters after the defaults have been set in the constructor. More...

Static Protected Member Functions inherited from ProgressLogger
static String	logTypeToFactoryName_ (LogType type)
	Return the name of the factory product used for this log type. More...

Protected Attributes inherited from DefaultParamHandler
Param	param_
	Container for current parameters. More...

Param	defaults_
	Container for default parameters. This member should be filled in the constructor of derived classes! More...

std::vector< String >	subsections_
	Container for registered subsections. This member should be filled in the constructor of derived classes! More...

String	error_name_
	Name that is displayed in error messages during the parameter checking. More...

bool	check_defaults_
	If this member is set to false no checking if parameters in done;. More...

bool	warn_empty_defaults_
	If this member is set to false no warning is emitted when defaults are empty;. More...

Protected Attributes inherited from ProgressLogger
LogType	type_

time_t	last_invoke_

ProgressLoggerImpl *	current_logger_

Static Protected Attributes inherited from ProgressLogger
static int	recursion_depth_

Detailed Description

A variant of QT clustering for the detection of feature groups.

The algorithm accumulates all features from all input maps, then applies a variant of QT clustering to find groups of corresponding features. In more detail, every feature from every input map is considered as a potential cluster center. For every center, its nearest neighbors from the other input maps are detected and added to the potential cluster. Iteratively, the cluster with the highest quality is extracted and the clustering is updated.

Properties affecting the grouping

To be included in a particular cluster, a feature has to fulfill the following conditions:

differences in RT and m/z from the cluster center must be below user-defined thresholds (distance_RT:max_difference and distance_MZ:max_difference),

the charge state must match that of the cluster center (unless ignore_charge is set),

if use_identifications is set and both the feature and the cluster center are annotated with peptide identifications, the identifications have to match.

Every cluster contains at most one feature from each input map - namely the feature closest to the cluster center that meets the criteria and does not belong to a better cluster.

The notion of "closeness" for features is defined by the distance function implemented in FeatureDistance, the parameters of which can be set by the user.

The quality of a cluster is computed from the number of elements in it and their distances to the cluster center. For more details see QTCluster.

Optimization

This algorithm includes a number of optimizations to reduce run-time:

two-dimensional hashing of features,
a look-up table for feature distances,
a variant of QT clustering that requires only one round of clustering.

See also: FeatureGroupingAlgorithmQT

Parameters of this class are:

Name	Type	Default	Restrictions	Description
use_identifications	string	false	true, false	Never link features that are annotated with different peptides (only the best hit per peptide identification is taken into account).
nr_partitions	int	100	min: 1	How many partitions in m/z space should be used for the algorithm (more partitions means faster runtime and more memory efficient execution )
ignore_charge	string	false	true, false	false [default]: pairing requires equal charge state (or at least one unknown charge '0'); true: Pairing irrespective of charge state
ignore_adduct	string	true	true, false	true [default]: pairing requires equal adducts (or at least one without adduct annotation); true: Pairing irrespective of adducts
distance_RT:max_difference	float	100.0	min: 0.0	Never pair features with a larger RT distance (in seconds).
distance_RT:exponent	float	1.0	min: 0.0	Normalized RT differences ([0-1], relative to 'max_difference') are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)
distance_RT:weight	float	1.0	min: 0.0	Final RT distances are weighted by this factor
distance_MZ:max_difference	float	0.3	min: 0.0	Never pair features with larger m/z distance (unit defined by 'unit')
distance_MZ:unit	string	Da	Da, ppm	Unit of the 'max_difference' parameter
distance_MZ:exponent	float	2.0	min: 0.0	Normalized ([0-1], relative to 'max_difference') m/z differences are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)
distance_MZ:weight	float	1.0	min: 0.0	Final m/z distances are weighted by this factor
distance_intensity:exponent	float	1.0	min: 0.0	Differences in relative intensity ([0-1]) are raised to this power (using 1 or 2 will be fast, everything else is REALLY slow)
distance_intensity:weight	float	0.0	min: 0.0	Final intensity distances are weighted by this factor
distance_intensity:log_transform	string	disabled	enabled, disabled	Log-transform intensities? If disabled, d = \|int_f2 - int_f1\| / int_max. If enabled, d = \|log(int_f2 + 1) - log(int_f1 + 1)\| / log(int_max + 1))

Note:

If a section name is documented, the documentation is displayed as tooltip.
Advanced parameter names are italic.

Member Typedef Documentation

◆ ElementMapping

typedef OpenMSBoost::unordered_map< OpenMS::GridFeature*, std::vector<QTCluster*> > ElementMapping

private

Map to store which grid features are next to which clusters.

◆ Grid

typedef HashGrid<OpenMS::GridFeature*> Grid

private

◆ PairDistances

typedef OpenMSBoost::unordered_map< std::pair<OpenMS::GridFeature*, OpenMS::GridFeature*>, double> PairDistances

private

Distances between pairs of grid features.

Member Enumeration Documentation

◆ anonymous enum

anonymous enum

protected

Enumerator
RT
MZ

Constructor & Destructor Documentation

◆ QTClusterFinder()

QTClusterFinder ( )

Constructor.

◆ ~QTClusterFinder()

~QTClusterFinder ( )

override

Destructor.

Member Function Documentation

◆ addClusterElements_()

void addClusterElements_	(	int	x,
		int	y,
		const Grid &	grid,
		QTCluster &	cluster,
		const OpenMS::GridFeature *	center_feature
	)

private

Adds elements to the cluster based on the elements hashed in the grid.

◆ computeClustering_()

void computeClustering_	(	Grid &	grid,
		std::list< QTCluster > &	clustering
	)

private

Computes an initial QT clustering of the points in the hash grid.

◆ create()

static BaseGroupFinder* create ( )

inlinestatic

Returns an instance of this class.

◆ getDistance_()

double getDistance_	(	const OpenMS::GridFeature *	left,
		const OpenMS::GridFeature *	right
	)

private

Calculates the distance between two grid features.

◆ getProductName()

static const String getProductName ( )

inlinestatic

Returns the name of the product.

◆ makeConsensusFeature_()

void makeConsensusFeature_	(	std::list< QTCluster > &	clustering,
		ConsensusFeature &	feature,
		ElementMapping &	element_mapping,
		Grid &
	)

private

Generates a consensus feature from the best cluster and updates the clustering.

◆ run() [1/2]

void run	(	const std::vector< ConsensusMap > &	input_maps,
		ConsensusMap &	result_map
	)

overridevirtual

Runs the algorithm on consensus maps.

Precondition: The data ranges of the input maps have to be up-to-date (use ConsensusMap::updateRanges).

Exceptions

Exception::IllegalArgument is thrown if the input data is not valid.

Implements BaseGroupFinder.

◆ run() [2/2]

void run	(	const std::vector< FeatureMap > &	input_maps,
		ConsensusMap &	result_map
	)

Runs the algorithm on feature maps.

Precondition: The data ranges of the input maps have to be up-to-date (use FeatureMap::updateRanges).

Exceptions

Exception::IllegalArgument is thrown if the input data is not valid.

◆ run_()

void run_	(	const std::vector< MapType > &	input_maps,
		ConsensusMap &	result_map
	)

private

Runs the algorithm on feature maps or consensus maps.

◆ run_internal_()

void run_internal_	(	const std::vector< MapType > &	input_maps,
		ConsensusMap &	result_map,
		bool	do_progress
	)

private

Runs the algorithm on feature maps or consensus maps (internal)

◆ setParameters_()

void setParameters_	(	double	max_intensity,
		double	max_mz
	)

private

Sets algorithm parameters.

Member Data Documentation

◆ already_used_

std::set<OpenMS::GridFeature*> already_used_

private

Set of features already used.

◆ feature_distance_

FeatureDistance feature_distance_

private

Feature distance functor.

◆ max_diff_mz_

double max_diff_mz_

private

Maximum m/z difference.

◆ max_diff_rt_

double max_diff_rt_

private

Maximum RT difference.

◆ nr_partitions_

int nr_partitions_

private

Maximum m/z difference.

◆ num_maps_

Size num_maps_

private

Number of input maps.

◆ use_IDs_

bool use_IDs_

private

Consider peptide identifications for grouping?

Public Member Functions

Static Public Member Functions

Protected Types

Private Types

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Member Typedef Documentation

◆ ElementMapping

◆ Grid

◆ PairDistances

Member Enumeration Documentation

◆ anonymous enum

Constructor & Destructor Documentation

◆ QTClusterFinder()

◆ ~QTClusterFinder()

Member Function Documentation

◆ addClusterElements_()

◆ computeClustering_()

◆ create()

◆ getDistance_()

◆ getProductName()

◆ makeConsensusFeature_()

◆ run() [1/2]

◆ run() [2/2]

◆ run_()

◆ run_internal_()

◆ setParameters_()

Member Data Documentation

◆ already_used_

◆ feature_distance_

◆ max_diff_mz_

◆ max_diff_rt_

◆ nr_partitions_

◆ num_maps_

◆ use_IDs_