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FeatureFinderAlgorithmSimple< PeakType, FeatureType > Class Template Reference

FeatureFinderAlgorithm implementation using the Simple* modules. More...

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

Inheritance diagram for FeatureFinderAlgorithmSimple< PeakType, FeatureType >:
FeatureFinderAlgorithm< PeakType, FeatureType > FeatureFinderDefs DefaultParamHandler

Public Member Functions

 FeatureFinderAlgorithmSimple ()
 default constructor More...
 
virtual Param getDefaultParameters () const
 Returns the default parameters. Reimplment. More...
 
virtual void run ()
 Main method that implements the actual algorithm. More...
 
- Public Member Functions inherited from FeatureFinderAlgorithm< PeakType, FeatureType >
 FeatureFinderAlgorithm ()
 default constructor More...
 
virtual ~FeatureFinderAlgorithm ()
 destructor More...
 
void setData (const MapType &map, FeatureMapType &features, FeatureFinder &ff)
 Sets a reference to the calling FeatureFinder. More...
 
virtual void setSeeds (const FeatureMapType &seeds)
 Sets a reference to the calling FeatureFinder. 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 DefaultParamHandleroperator= (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 ParamgetParameters () const
 Non-mutable access to the parameters. More...
 
const ParamgetDefaults () const
 Non-mutable access to the default parameters. More...
 
const StringgetName () 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...
 

Static Public Member Functions

static FeatureFinderAlgorithm
< PeakType, FeatureType > * 
create ()
 
static const String getProductName ()
 
- Static Public Member Functions inherited from FeatureFinderAlgorithm< PeakType, FeatureType >
static void registerChildren ()
 register all derived classes here (see FeatureFinderAlgorithm_impl.h) More...
 

Private Member Functions

FeatureFinderAlgorithmSimpleoperator= (const FeatureFinderAlgorithmSimple &)
 Not implemented. More...
 
 FeatureFinderAlgorithmSimple (const FeatureFinderAlgorithmSimple &)
 Not implemented. More...
 

Additional Inherited Members

- Public Types inherited from FeatureFinderAlgorithm< PeakType, FeatureType >
typedef MSExperiment< PeakTypeMapType
 Input map type. More...
 
typedef MapType::CoordinateType CoordinateType
 Coordinate/Position type of peaks. More...
 
typedef MapType::IntensityType IntensityType
 Intensity type of peaks. More...
 
typedef FeatureMap< FeatureType > FeatureMapType
 Output feature type. More...
 
- Public Types inherited from FeatureFinderDefs
enum  Flag { UNUSED, USED }
 Flags that indicate if a peak is already used in a feature. More...
 
typedef IsotopeCluster::IndexPair IndexPair
 Index to peak consisting of two UInts (scan index / peak index) More...
 
typedef
IsotopeCluster::ChargedIndexSet 
ChargedIndexSet
 Index to peak consisting of two UInts (scan index / peak index) with charge information. More...
 
typedef IsotopeCluster::IndexSet IndexSet
 A set of peak indices. 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...
 
- Protected Attributes inherited from FeatureFinderAlgorithm< PeakType, FeatureType >
const MapTypemap_
 Input data pointer. More...
 
FeatureMapTypefeatures_
 Output data pointer. More...
 
FeatureFinderff_
 Pointer to the calling FeatureFinder that is used to access the feature flags. 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< Stringsubsections_
 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...
 

Detailed Description

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

FeatureFinderAlgorithm implementation using the Simple* modules.

Deprecated:
Deprecated in OpenMS 1.7.

SimpleSeeder, SimpleExtender, ModelFitter (using EmgModel (exponentially modified gaussian with parameter optimization [Levenberg-Marquardt algorithm]) in RT dimension and IsotopeModel (charge does not equal zero) or LmaGaussModel (parameter optimization using Levenberg-Marquardt algorithm) in dimension of mz).

Parameters of this class are:

NameTypeDefaultRestrictionsDescription
seeder:min_intensity float0 min: 0Absolute value for the minimum intensity required for a seed.
seeder:signal_to_noise float10 min: 0Minimal required SignalToNoise (S/N) ratio for a seed.
seeder:SignalToNoiseEstimationParameter:max_intensity int-1 min: -1maximal 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.
seeder:SignalToNoiseEstimationParameter:auto_max_stdev_factor float3 min: 0 max: 999parameter for 'max_intensity' estimation (if 'auto_mode' == 0): mean + 'auto_max_stdev_factor' * stdev
seeder:SignalToNoiseEstimationParameter:auto_max_percentile int95 min: 0 max: 100parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile
seeder:SignalToNoiseEstimationParameter:auto_mode int0 min: -1 max: 1method to use to determine maximal intensity: -1 --> use 'max_intensity'; 0 --> 'auto_max_stdev_factor' method (default); 1 --> 'auto_max_percentile' method
seeder:SignalToNoiseEstimationParameter:win_len float200 min: 1window length in Thomson
seeder:SignalToNoiseEstimationParameter:bin_count int30 min: 3number of bins for intensity values
seeder:SignalToNoiseEstimationParameter:min_required_elements int10 min: 1minimum number of elements required in a window (otherwise it is considered sparse)
seeder:SignalToNoiseEstimationParameter:noise_for_empty_window float1e+20  noise value used for sparse windows
extender:dist_mz_up float6 min: 0Maximum high m/z distance of peak in the region/boundary from the seed.
extender:dist_mz_down float2 min: 0Maximum low m/z distance of peak in the region/boundary from the seed.
extender:dist_rt_up float5 min: 0Maximum high RT distance of peak in the region/boundary from the seed.
extender:dist_rt_down float5 min: 0Maximum low RT distance of peak in the region/boundary from the seed.
extender:priority_thr float-0.1  Minimum priority for data points to be included into the boundary of the feature (default 0.0). The priority of a data point is a function of its intensity and its distance to the last point included into the feature region. Setting this threshold to zero or a very small value is usually a good idea.
extender:intensity_factor float0.03 min: 0 max: 1Influences for intensity (ion count) threshold in the feature extension. We include only raw data points into this region if their intensity is larger than [intensity_factor * (intensity of the seed)].
fitter:fit_algorithm stringsimple simple, simplest, waveletFitting algorithm type (internal parameter).
fitter:max_iteration int500 min: 1Maximum number of iterations for fitting with Levenberg-Marquardt algorithm.
fitter:deltaAbsError float0.0001 min: 0Absolute error used by the Levenberg-Marquardt algorithm.
fitter:deltaRelError float0.0001 min: 0Relative error used by the Levenberg-Marquardt algorithm.
fitter:tolerance_stdev_bounding_box float3 min: 0Bounding box has range [minimim of data, maximum of data] enlarged by tolerance_stdev_bounding_box times the standard deviation of the data
fitter:intensity_cutoff_factor float0.0500000007450581 min: 0 max: 1Cutoff peaks with a predicted intensity below intensity_cutoff_factor times the maximal intensity of the model
fitter:feature_intensity_sum int1 min: 0 max: 1Determines what is reported as feature intensity.
1: the sum of peak intensities;
0: the maximum intensity of all peaks
fitter:min_num_peaks:final int5 min: 1Minimum number of peaks left after cutoff. If smaller, feature will be discarded.
fitter:min_num_peaks:extended int10 min: 1Minimum number of peaks after extension. If smaller, feature will be discarded.
fitter:rt:interpolation_step float0.200000002980232 min: 0Step size in seconds used to interpolate model for RT.
fitter:mz:interpolation_step float0.0299999993294477 min: 0.001Interpolation step size for m/z.
fitter:mz:model_type:first int1 min: 0Numeric id of first m/z model fitted (usually indicating the charge state), 0 = no isotope pattern (fit a single gaussian).
fitter:mz:model_type:last int4 min: 0Numeric id of last m/z model fitted (usually indicating the charge state), 0 = no isotope pattern (fit a single gaussian).
fitter:quality:type stringCorrelation Correlation, RankCorrelationType of the quality measure used to assess the fit of model vs data.
fitter:quality:minimum float0.649999976158142 min: 0 max: 1Minimum quality of fit, features below this threshold are discarded.
fitter:isotope_model:stdev:first float0.0399999991059303 min: 0First standard deviation to be considered for isotope model.
fitter:isotope_model:stdev:last float0.119999997317791 min: 0Last standard deviation to be considered for isotope model.
fitter:isotope_model:stdev:step float0.0399999991059303 min: 0Step size for standard deviations considered for isotope model.
fitter:isotope_model:averagines:C float0.0444398894906044 min: 0Number of C atoms per Dalton of the mass.
fitter:isotope_model:averagines:H float0.0698157176375389 min: 0Number of H atoms per Dalton of the mass.
fitter:isotope_model:averagines:N float0.0122177302837372 min: 0Number of N atoms per Dalton of the mass.
fitter:isotope_model:averagines:O float0.0132939899340272 min: 0Number of O atoms per Dalton of the mass.
fitter:isotope_model:averagines:S float0.000375250005163252 min: 0Number of S atoms per Dalton of the mass.
fitter:isotope_model:isotope:trim_right_cutoff float0.00100000004749745 min: 0Cutoff for averagine distribution, trailing isotopes below this relative intensity are not considered.
fitter:isotope_model:isotope:maximum int100 min: 1Maximum number of isotopes being used for the IsotopeModel.
fitter:isotope_model:isotope:distance float1.00049495697021 min: 0Distance between consecutive isotopic peaks.

Note:

Constructor & Destructor Documentation

Not implemented.

Member Function Documentation

static FeatureFinderAlgorithm<PeakType, FeatureType>* create ( )
inlinestatic
virtual Param getDefaultParameters ( ) const
inlinevirtual
static const String getProductName ( )
inlinestatic
FeatureFinderAlgorithmSimple& operator= ( const FeatureFinderAlgorithmSimple< PeakType, FeatureType > &  )
private

Not implemented.

virtual void run ( )
inlinevirtual

OpenMS / TOPP release 1.11.1 Documentation generated on Thu Nov 14 2013 11:19:34 using doxygen 1.8.5