FeatureFinderAlgorithm implementation using the Simple* modules. More...
#include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/FeatureFinderAlgorithmSimplest.h>
Public Member Functions | |
FeatureFinderAlgorithmSimplest () | |
default constructor More... | |
virtual Param | getDefaultParameters () const |
Returns the default parameters. Reimplment. More... | |
virtual void | run () |
Main method that implements the actual algorithm. More... | |
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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... | |
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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 ¶m) |
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... | |
Static Public Member Functions | |
static FeatureFinderAlgorithm < PeakType, FeatureType > * | create () |
static const String | getProductName () |
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static void | registerChildren () |
register all derived classes here (see FeatureFinderAlgorithm_impl.h) More... | |
Private Member Functions | |
FeatureFinderAlgorithmSimplest & | operator= (const FeatureFinderAlgorithmSimplest &) |
Not implemented. More... | |
FeatureFinderAlgorithmSimplest (const FeatureFinderAlgorithmSimplest &) | |
Not implemented. More... | |
Additional Inherited Members | |
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typedef MSExperiment< PeakType > | MapType |
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... | |
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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... | |
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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... | |
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const MapType * | map_ |
Input data pointer. More... | |
FeatureMapType * | features_ |
Output data pointer. More... | |
FeatureFinder * | ff_ |
Pointer to the calling FeatureFinder that is used to access the feature flags. More... | |
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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... | |
FeatureFinderAlgorithm implementation using the Simple* modules.
SimpleSeeder, SimpleExtender, ModelFitter (using BiGaussModel in RT dimension and IsotopeModel (charge does not equal zero) or GaussModel in dimension of mz).
Parameters of this class are:Name | Type | Default | Restrictions | Description |
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seeder:min_intensity | float | 0 | min: 0 | Absolute value for the minimum intensity required for a seed. |
seeder:signal_to_noise | float | 10 | min: 0 | Minimal required SignalToNoise (S/N) ratio for a seed. |
seeder: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. |
seeder: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 |
seeder:SignalToNoiseEstimationParameter:auto_max_percentile | int | 95 | min: 0 max: 100 | parameter for 'max_intensity' estimation (if 'auto_mode' == 1): auto_max_percentile th percentile |
seeder: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 |
seeder:SignalToNoiseEstimationParameter:win_len | float | 200 | min: 1 | window length in Thomson |
seeder:SignalToNoiseEstimationParameter:bin_count | int | 30 | min: 3 | number of bins for intensity values |
seeder:SignalToNoiseEstimationParameter:min_required_elements | int | 10 | min: 1 | minimum number of elements required in a window (otherwise it is considered sparse) |
seeder:SignalToNoiseEstimationParameter:noise_for_empty_window | float | 1e+20 | noise value used for sparse windows | |
extender:dist_mz_up | float | 6 | min: 0 | Maximum high m/z distance of peak in the region/boundary from the seed. |
extender:dist_mz_down | float | 2 | min: 0 | Maximum low m/z distance of peak in the region/boundary from the seed. |
extender:dist_rt_up | float | 5 | min: 0 | Maximum high RT distance of peak in the region/boundary from the seed. |
extender:dist_rt_down | float | 5 | min: 0 | Maximum 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 | float | 0.03 | min: 0 max: 1 | Influences 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 | string | simple | simple, simplest, wavelet | Fitting algorithm type (internal parameter). |
fitter:max_iteration | int | 500 | min: 1 | Maximum number of iterations for fitting with Levenberg-Marquardt algorithm. |
fitter:deltaAbsError | float | 0.0001 | min: 0 | Absolute error used by the Levenberg-Marquardt algorithm. |
fitter:deltaRelError | float | 0.0001 | min: 0 | Relative error used by the Levenberg-Marquardt algorithm. |
fitter:tolerance_stdev_bounding_box | float | 3 | min: 0 | Bounding 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 | float | 0.0500000007450581 | min: 0 max: 1 | Cutoff peaks with a predicted intensity below intensity_cutoff_factor times the maximal intensity of the model |
fitter:feature_intensity_sum | int | 1 | min: 0 max: 1 | Determines what is reported as feature intensity. 1: the sum of peak intensities; 0: the maximum intensity of all peaks |
fitter:min_num_peaks:final | int | 5 | min: 1 | Minimum number of peaks left after cutoff. If smaller, feature will be discarded. |
fitter:min_num_peaks:extended | int | 10 | min: 1 | Minimum number of peaks after extension. If smaller, feature will be discarded. |
fitter:rt:interpolation_step | float | 0.200000002980232 | min: 0 | Step size in seconds used to interpolate model for RT. |
fitter:mz:interpolation_step | float | 0.0299999993294477 | min: 0.001 | Interpolation step size for m/z. |
fitter:mz:model_type:first | int | 1 | min: 0 | Numeric 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 | int | 4 | min: 0 | Numeric id of last m/z model fitted (usually indicating the charge state), 0 = no isotope pattern (fit a single gaussian). |
fitter:quality:type | string | Correlation | Correlation, RankCorrelation | Type of the quality measure used to assess the fit of model vs data. |
fitter:quality:minimum | float | 0.649999976158142 | min: 0 max: 1 | Minimum quality of fit, features below this threshold are discarded. |
fitter:isotope_model:stdev:first | float | 0.0399999991059303 | min: 0 | First standard deviation to be considered for isotope model. |
fitter:isotope_model:stdev:last | float | 0.119999997317791 | min: 0 | Last standard deviation to be considered for isotope model. |
fitter:isotope_model:stdev:step | float | 0.0399999991059303 | min: 0 | Step size for standard deviations considered for isotope model. |
fitter:isotope_model:averagines:C | float | 0.0444398894906044 | min: 0 | Number of C atoms per Dalton of the mass. |
fitter:isotope_model:averagines:H | float | 0.0698157176375389 | min: 0 | Number of H atoms per Dalton of the mass. |
fitter:isotope_model:averagines:N | float | 0.0122177302837372 | min: 0 | Number of N atoms per Dalton of the mass. |
fitter:isotope_model:averagines:O | float | 0.0132939899340272 | min: 0 | Number of O atoms per Dalton of the mass. |
fitter:isotope_model:averagines:S | float | 0.000375250005163252 | min: 0 | Number of S atoms per Dalton of the mass. |
fitter:isotope_model:isotope:trim_right_cutoff | float | 0.00100000004749745 | min: 0 | Cutoff for averagine distribution, trailing isotopes below this relative intensity are not considered. |
fitter:isotope_model:isotope:maximum | int | 100 | min: 1 | Maximum number of isotopes being used for the IsotopeModel. |
fitter:isotope_model:isotope:distance | float | 1.00049495697021 | min: 0 | Distance between consecutive isotopic peaks. |
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inline |
default constructor
References DefaultParamHandler::check_defaults_, DefaultParamHandler::defaults_, and FeatureFinderAlgorithmSimplest< PeakType, FeatureType >::getDefaultParameters().
Referenced by FeatureFinderAlgorithmSimplest< PeakType, FeatureType >::create().
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private |
Not implemented.
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inlinestatic |
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inlinevirtual |
Returns the default parameters. Reimplment.
Reimplment if you derive a class and have to incoopreate sub-algorithm default parameters.
Reimplemented from FeatureFinderAlgorithm< PeakType, FeatureType >.
References FeatureFinderAlgorithm< PeakType, FeatureType >::features_, FeatureFinderAlgorithm< PeakType, FeatureType >::ff_, DefaultParamHandler::getParameters(), Param::insert(), FeatureFinderAlgorithm< PeakType, FeatureType >::map_, and Param::setSectionDescription().
Referenced by FeatureFinderAlgorithmSimplest< PeakType, FeatureType >::FeatureFinderAlgorithmSimplest().
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inlinestatic |
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private |
Not implemented.
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inlinevirtual |
Main method that implements the actual algorithm.
Summary of fitting results
Implements FeatureFinderAlgorithm< PeakType, FeatureType >.
References Summary::charge, Summary::corr_max, Summary::corr_mean, Summary::corr_min, DataValue::EMPTY, ProgressLogger::endProgress(), Summary::exception, Param::exists(), SimpleExtender< PeakType, FeatureType >::extend(), FeatureFinderAlgorithm< PeakType, FeatureType >::features_, FeatureFinderAlgorithm< PeakType, FeatureType >::ff_, ModelFitter< PeakType, FeatureType >::fit(), BaseFeature::getCharge(), Feature::getModelDescription(), BaseException::getName(), Feature::getOverallQuality(), ModelDescription< D >::getParam(), DefaultParamHandler::getParameters(), FeatureFinder::getPeakFlag(), Param::getValue(), FeatureFinderAlgorithm< PeakType, FeatureType >::map_, Summary::mz_model, Summary::mz_stdev, SimpleSeeder< PeakType, FeatureType >::nextSeed(), Summary::no_exceptions, Param::setDefaults(), DefaultParamHandler::setParameters(), Param::setValue(), FeatureFinderDefs::UNUSED, and BaseException::what().
OpenMS / TOPP release 1.11.1 | Documentation generated on Thu Nov 14 2013 11:19:34 using doxygen 1.8.5 |