FeatureFinderAlgorithm implementation using the Simple* modules. More...
#include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/FeatureFinderAlgorithmSimple.h>
Public Member Functions | |
| FeatureFinderAlgorithmSimple () | |
| default constructor | |
| virtual Param | getDefaultParameters () const |
| virtual void | run () |
Static Public Member Functions | |
| static FeatureFinderAlgorithm < PeakType, FeatureType > * | create () |
| static const String | getProductName () |
Private Member Functions | |
| FeatureFinderAlgorithmSimple & | operator= (const FeatureFinderAlgorithmSimple &) |
| Not implemented. | |
| FeatureFinderAlgorithmSimple (const FeatureFinderAlgorithmSimple &) | |
| Not implemented. | |
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:| Name | Type | Default | Restrictions | Description |
|---|---|---|---|---|
| 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. |
Note:
- If a section name is documented, the documentation is displayed as tooltip.
- Advanced parameter names are italic.
Constructor & Destructor Documentation
| FeatureFinderAlgorithmSimple | ( | ) | [inline] |
default constructor
References DefaultParamHandler::check_defaults_, DefaultParamHandler::defaults_, and FeatureFinderAlgorithmSimple< PeakType, FeatureType >::getDefaultParameters().
Referenced by FeatureFinderAlgorithmSimple< PeakType, FeatureType >::create().
| FeatureFinderAlgorithmSimple | ( | const FeatureFinderAlgorithmSimple< PeakType, FeatureType > & | ) | [private] |
Not implemented.
Member Function Documentation
| static FeatureFinderAlgorithm<PeakType, FeatureType>* create | ( | ) | [inline, static] |
| virtual Param getDefaultParameters | ( | ) | const [inline, virtual] |
References FeatureFinderAlgorithm< PeakType, FeatureType >::features_, FeatureFinderAlgorithm< PeakType, FeatureType >::ff_, DefaultParamHandler::getParameters(), Param::insert(), FeatureFinderAlgorithm< PeakType, FeatureType >::map_, and Param::setSectionDescription().
Referenced by FeatureFinderAlgorithmSimple< PeakType, FeatureType >::FeatureFinderAlgorithmSimple().
| static const String getProductName | ( | ) | [inline, static] |
| FeatureFinderAlgorithmSimple& operator= | ( | const FeatureFinderAlgorithmSimple< PeakType, FeatureType > & | ) | [private] |
Not implemented.
| virtual void run | ( | ) | [inline, virtual] |
Summary of fitting results
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(), and Param::setValue().
| OpenMS / TOPP release 1.10.0 | Documentation generated on Thu Mar 7 2013 09:42:57 using doxygen 1.7.1 |