Simple seeding class that uses the strongest peak as next seed. More...

#include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/SimpleSeeder.h>

Inheritance diagram for SimpleSeeder< PeakType, FeatureType >:

Public Types
typedef FeaFiModule< PeakType, FeatureType >	Base
typedef MSExperiment< PeakType >	MapType
Public Member Functions
	SimpleSeeder (const MSExperiment< PeakType > map, FeatureMap< FeatureType > features, FeatureFinder *ff)
	Constructor.
virtual	~SimpleSeeder ()
	destructor
IndexPair	nextSeed ()
	return the next seed
Protected Member Functions
void	initialize_ ()
Protected Attributes
std::vector< IndexPair >	indices_
	contains the indizes
std::vector< IndexPair > ::const_iterator	current_peak_
	Points to the next peak in the peak vector.
bool	initialized_
	Flag that indicates of the indices are initialized.
Private Member Functions
	SimpleSeeder ()
	Not implemented.
SimpleSeeder &	operator= (const SimpleSeeder &)
	Not implemented.
	SimpleSeeder (const SimpleSeeder &)
	Not implemented.

Detailed Description

template<class PeakType, class FeatureType>
class OpenMS::SimpleSeeder< PeakType, FeatureType >

Simple seeding class that uses the strongest peak as next seed.

This class simply sorts the peaks according to intensity and proposes the highest peak, which is not yet included in a feature, as next seed.

Parameters of this class are:

Name	Type	Default	Restrictions	Description
min_intensity	float	0	min: 0	Absolute value for the minimum intensity required for a seed.
signal_to_noise	float	10	min: 0	Minimal required SignalToNoise (S/N) ratio for a seed.
SignalToNoiseEstimationParameter:max_intensity	int	-1	min: -1	maximal intensity considered for histogram construction. By default, it will be calculated automatically (see auto_mode). Only provide this parameter if you know what you are doing (and change 'auto_mode' to '-1')! All intensities EQUAL/ABOVE 'max_intensity' will be added to the LAST histogram bin. If you choose 'max_intensity' too small, the noise estimate might be too small as well. If chosen too big, the bins become quite large (which you could counter by increasing 'bin_count', which increases runtime). In general, the Median-S/N estimator is more robust to a manual max_intensity than the MeanIterative-S/N.
SignalToNoiseEstimationParameter:auto_max_stdev_factor	float	3	min: 0 max: 999	parameter for 'max_intensity' estimation (if 'auto_mode' == 0): mean + 'auto_max_stdev_factor' * stdev
SignalToNoiseEstimationParameter:auto_max_percentile	int	95	min: 0 max: 100	parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile
SignalToNoiseEstimationParameter:auto_mode	int	0	min: -1 max: 1	method to use to determine maximal intensity: -1 --> use 'max_intensity'; 0 --> 'auto_max_stdev_factor' method (default); 1 --> 'auto_max_percentile' method
SignalToNoiseEstimationParameter:win_len	float	200	min: 1	window length in Thomson
SignalToNoiseEstimationParameter:bin_count	int	30	min: 3	number of bins for intensity values
SignalToNoiseEstimationParameter:min_required_elements	int	10	min: 1	minimum number of elements required in a window (otherwise it is considered sparse)
SignalToNoiseEstimationParameter:noise_for_empty_window	float	1e+20		noise value used for sparse windows