A feature grouping algorithm for unlabeled data. More...

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

Inheritance diagram for FeatureGroupingAlgorithmKD:

Collaboration diagram for FeatureGroupingAlgorithmKD:

Public Member Functions
	FeatureGroupingAlgorithmKD ()
	Default constructor. More...

	~FeatureGroupingAlgorithmKD () override
	Destructor. More...

void	group (const std::vector< FeatureMap > &maps, ConsensusMap &out) override
	Applies the algorithm to feature maps. More...

void	group (const std::vector< ConsensusMap > &maps, ConsensusMap &out) override
	Applies the algorithm to consensus maps. More...

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

	~FeatureGroupingAlgorithm () override
	Destructor. More...

void	transferSubelements (const std::vector< ConsensusMap > &maps, ConsensusMap &out) const
	Transfers subelements (grouped features) from input consensus maps to the result consensus map. 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...

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...

virtual	~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	setLogger (ProgressLoggerImpl *logger)
	Sets the logger to be used for progress logging. 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 (UInt64 bytes_processed=0) const

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

Private Member Functions
	FeatureGroupingAlgorithmKD (const FeatureGroupingAlgorithmKD &)
	Copy constructor intentionally not implemented -> private. More...

FeatureGroupingAlgorithmKD &	operator= (const FeatureGroupingAlgorithmKD &)
	Assignment operator intentionally not implemented -> private. More...

template<typename MapType >
void	group_ (const std::vector< MapType > &input_maps, ConsensusMap &out)
	Applies the algorithm to feature or consensus maps. More...

void	runClustering_ (const KDTreeFeatureMaps &kd_data, ConsensusMap &out)
	Run the actual clustering algorithm. More...

void	updateClusterProxies_ (std::set< ClusterProxyKD > &potential_clusters, std::vector< ClusterProxyKD > &cluster_for_idx, const std::set< Size > &update_these, const std::vector< Int > &assigned, const KDTreeFeatureMaps &kd_data)
	Update maximum possible sizes of potential consensus features for indices specified in `update_these`. More...

ClusterProxyKD	computeBestClusterForCenter_ (Size i, std::vector< Size > &cf_indices, const std::vector< Int > &assigned, const KDTreeFeatureMaps &kd_data) const
	Compute the current best cluster with center index `i` (mutates `proxy` and `cf_indices`) More...

void	addConsensusFeature_ (const std::vector< Size > &indices, const KDTreeFeatureMaps &kd_data, ConsensusMap &out) const
	Construct consensus feature and add to out map. More...

Private Attributes
SignedSize	progress_
	Current progress for logging. More...

double	rt_tol_secs_
	RT tolerance. More...

double	mz_tol_
	m/z tolerance More...

bool	mz_ppm_
	m/z unit ppm? More...

FeatureDistance	feature_distance_
	Feature distance functor. More...

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

Static Public Member Functions inherited from DefaultParamHandler
static void	writeParametersToMetaValues (const Param &write_this, MetaInfoInterface &write_here, const String &key_prefix="")
	Writes all parameters to meta values. More...

Protected Member Functions inherited from FeatureGroupingAlgorithm
template<class MapType >
void	postprocess_ (const std::vector< MapType > &maps, ConsensusMap &out)

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...

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 feature grouping algorithm for unlabeled data.

The algorithm takes a number of feature or consensus maps and searches for corresponding (consensus) features across different maps.

Parameters of this class are:

Name	Type	Default	Restrictions	Description
mz_unit	string	ppm	ppm, Da	Unit of m/z tolerance
nr_partitions	int	100	min: 1	Number of partitions in m/z space
warp:enabled	string	true	true, false	Whether or not to internally warp feature RTs using LOWESS transformation before linking (reported RTs in results will always be the original RTs)
warp:rt_tol	float	100.0	min: 0.0	Width of RT tolerance window (sec)
warp:mz_tol	float	5.0	min: 0.0	m/z tolerance (in ppm or Da)
warp:max_pairwise_log_fc	float	0.5		Maximum absolute log10 fold change between two compatible signals during compatibility graph construction. Two signals from different maps will not be connected by an edge in the compatibility graph if absolute log fold change exceeds this limit (they might still end up in the same connected component, however). Note: this does not limit fold changes in the linking stage, only during RT alignment, where we try to find high-quality alignment anchor points. Setting this to a value < 0 disables the FC check.
warp:min_rel_cc_size	float	0.5	min: 0.0 max: 1.0	Only connected components containing compatible features from at least max(2, (warp_min_occur * number_of_input_maps)) input maps are considered for computing the warping function
warp:max_nr_conflicts	int	0	min: -1	Allow up to this many conflicts (features from the same map) per connected component to be used for alignment (-1 means allow any number of conflicts)
link:rt_tol	float	30.0	min: 0.0	Width of RT tolerance window (sec)
link:mz_tol	float	10.0	min: 0.0	m/z tolerance (in ppm or Da)
link:charge_merging	string	With_charge_zero	Identical, With_charge_zero, Any	whether to disallow charge mismatches (Identical), allow to link charge zero (i.e., unknown charge state) with every charge state, or disregard charges (Any).
link:adduct_merging	string	Any	Identical, With_unknown_adducts, Any	whether to only allow the same adduct for linking (Identical), also allow linking features with adduct-free ones, or disregard adducts (Any).
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: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	1.0	min: 0.0	Final intensity distances are weighted by this factor
distance_intensity:log_transform	string	enabled	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))
LOWESS:span	float	0.666666666666667	min: 0.0 max: 1.0	Fraction of datapoints (f) to use for each local regression (determines the amount of smoothing). Choosing this parameter in the range .2 to .8 usually results in a good fit.
LOWESS:num_iterations	int	3	min: 0	Number of robustifying iterations for lowess fitting.
LOWESS:delta	float	-1.0		Nonnegative parameter which may be used to save computations (recommended value is 0.01 of the range of the input, e.g. for data ranging from 1000 seconds to 2000 seconds, it could be set to 10). Setting a negative value will automatically do this.
LOWESS:interpolation_type	string	cspline	linear, cspline, akima	Method to use for interpolation between datapoints computed by lowess. 'linear': Linear interpolation. 'cspline': Use the cubic spline for interpolation. 'akima': Use an akima spline for interpolation
LOWESS:extrapolation_type	string	four-point-linear	two-point-linear, four-point-linear, global-linear	Method to use for extrapolation outside the data range. 'two-point-linear': Uses a line through the first and last point to extrapolate. 'four-point-linear': Uses a line through the first and second point to extrapolate in front and and a line through the last and second-to-last point in the end. 'global-linear': Uses a linear regression to fit a line through all data points and use it for interpolation.

Note:

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

Constructor & Destructor Documentation

◆ FeatureGroupingAlgorithmKD() [1/2]

FeatureGroupingAlgorithmKD ( )

Default constructor.

◆ ~FeatureGroupingAlgorithmKD()

~FeatureGroupingAlgorithmKD ( )

override

Destructor.

◆ FeatureGroupingAlgorithmKD() [2/2]

FeatureGroupingAlgorithmKD ( const FeatureGroupingAlgorithmKD & )

private

Copy constructor intentionally not implemented -> private.

Member Function Documentation

◆ addConsensusFeature_()

void addConsensusFeature_	(	const std::vector< Size > &	indices,
		const KDTreeFeatureMaps &	kd_data,
		ConsensusMap &	out
	)		const

private

Construct consensus feature and add to out map.

◆ computeBestClusterForCenter_()

ClusterProxyKD computeBestClusterForCenter_	(	Size	i,
		std::vector< Size > &	cf_indices,
		const std::vector< Int > &	assigned,
		const KDTreeFeatureMaps &	kd_data
	)		const

private

Compute the current best cluster with center index i (mutates proxy and cf_indices)

◆ group() [1/2]

void group	(	const std::vector< ConsensusMap > &	maps,
		ConsensusMap &	out
	)

overridevirtual

Applies the algorithm to consensus maps.

Exceptions

IllegalArgument is thrown if less than two input maps are given.

Reimplemented from FeatureGroupingAlgorithm.

◆ group() [2/2]

void group	(	const std::vector< FeatureMap > &	maps,
		ConsensusMap &	out
	)

overridevirtual

Applies the algorithm to feature maps.

Exceptions

IllegalArgument is thrown if less than two input maps are given.

Implements FeatureGroupingAlgorithm.

◆ group_()

void group_	(	const std::vector< MapType > &	input_maps,
		ConsensusMap &	out
	)

private

Applies the algorithm to feature or consensus maps.

Exceptions

IllegalArgument is thrown if less than two input maps are given.

◆ operator=()

FeatureGroupingAlgorithmKD& operator= ( const FeatureGroupingAlgorithmKD & )

private

Assignment operator intentionally not implemented -> private.

◆ runClustering_()

void runClustering_	(	const KDTreeFeatureMaps &	kd_data,
		ConsensusMap &	out
	)

private

Run the actual clustering algorithm.

◆ updateClusterProxies_()

void updateClusterProxies_	(	std::set< ClusterProxyKD > &	potential_clusters,
		std::vector< ClusterProxyKD > &	cluster_for_idx,
		const std::set< Size > &	update_these,
		const std::vector< Int > &	assigned,
		const KDTreeFeatureMaps &	kd_data
	)

private

Update maximum possible sizes of potential consensus features for indices specified in update_these.

Member Data Documentation

◆ feature_distance_

FeatureDistance feature_distance_

private

Feature distance functor.

◆ mz_ppm_

bool mz_ppm_

private

m/z unit ppm?

◆ mz_tol_

double mz_tol_

private

m/z tolerance

◆ progress_

SignedSize progress_

private

Current progress for logging.

◆ rt_tol_secs_

double rt_tol_secs_

private

RT tolerance.

Public Member Functions

Private Member Functions

Private Attributes

Additional Inherited Members

Detailed Description

Constructor & Destructor Documentation

◆ FeatureGroupingAlgorithmKD() [1/2]

◆ ~FeatureGroupingAlgorithmKD()

◆ FeatureGroupingAlgorithmKD() [2/2]

Member Function Documentation

◆ addConsensusFeature_()

◆ computeBestClusterForCenter_()

◆ group() [1/2]

◆ group() [2/2]

◆ group_()

◆ operator=()

◆ runClustering_()

◆ updateClusterProxies_()

Member Data Documentation

◆ feature_distance_

◆ mz_ppm_

◆ mz_tol_

◆ progress_

◆ rt_tol_secs_