OpenMS  3.0.0
EmgScoring.h
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31 // $Maintainer: Hannes Roest $
32 // $Authors: Hannes Roest $
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34 
35 #pragma once
36 
40 
44 
46 
47 #include <vector>
48 #include <cmath> // for isnan
49 
50 namespace OpenMS
51 {
52 
60  class EmgScoring
61  {
62 
63  public :
64 
65  EmgScoring() = default;
66 
67  ~EmgScoring() = default;
68 
71  void setFitterParam(const Param& param)
72  {
73  fitter_emg1D_params_ = param;
74  }
75 
78  {
79  return EmgFitter1D().getDefaults();
80  }
81 
83  template<typename SpectrumType, class TransitionT>
84  double calcElutionFitScore(MRMFeature & mrmfeature, MRMTransitionGroup<SpectrumType, TransitionT> & transition_group) const
85  {
86  double avg_score = 0;
87  bool smooth_data = false;
88 
89  for (Size k = 0; k < transition_group.size(); k++)
90  {
91  // get the id, then find the corresponding transition and features within this peakgroup
92  String native_id = transition_group.getChromatograms()[k].getNativeID();
93  Feature f = mrmfeature.getFeature(native_id);
94  OPENMS_PRECONDITION(f.getConvexHulls().size() == 1, "Convex hulls need to have exactly one hull point structure");
95 
96  //TODO think about penalizing aborted fits even more. Currently -1 is just the "lowest" pearson correlation to
97  // a fit that you can have.
98  double fscore = elutionModelFit(f.getConvexHulls()[0].getHullPoints(), smooth_data);
99  avg_score += fscore;
100  }
101 
102  avg_score /= transition_group.size();
103  return avg_score;
104  }
105 
106  // Fxn from FeatureFinderAlgorithmMRM
107  // TODO: check whether we can leave out some of the steps here, e.g. gaussian smoothing
108  double elutionModelFit(const ConvexHull2D::PointArrayType& current_section, bool smooth_data) const
109  {
110  // We need at least 2 datapoints in order to create a fit
111  if (current_section.size() < 2)
112  {
113  return -1;
114  }
115 
116  // local PeakType is a small hack since here we *need* data of type
117  // Peak1D, otherwise our fitter will not accept it.
118  typedef Peak1D LocalPeakType;
119 
120  // -- cut line 301 of FeatureFinderAlgorithmMRM
121  std::vector<LocalPeakType> data_to_fit;
122  prepareFit_(current_section, data_to_fit, smooth_data);
123  std::unique_ptr<InterpolationModel> model_rt;
124  double quality = fitRT_(data_to_fit, model_rt);
125  // cut line 354 of FeatureFinderAlgorithmMRM
126 
127  return quality;
128  }
129 
130  protected:
131  template<class LocalPeakType>
132  double fitRT_(std::vector<LocalPeakType>& rt_input_data, std::unique_ptr<InterpolationModel>& model) const
133  {
134  EmgFitter1D fitter_emg1D;
135  fitter_emg1D.setParameters(fitter_emg1D_params_);
136  // Construct model for rt
137  // NaN is checked in fit1d: if (std::isnan(quality)) quality = -1.0;
138  return fitter_emg1D.fit1d(rt_input_data, model);
139  }
140 
141  // Fxn from FeatureFinderAlgorithmMRM
142  // TODO: check whether we can leave out some of the steps here, e.g. gaussian smoothing
143  template<class LocalPeakType>
144  void prepareFit_(const ConvexHull2D::PointArrayType & current_section, std::vector<LocalPeakType> & data_to_fit, bool smooth_data) const
145  {
146  // typedef Peak1D LocalPeakType;
147  PeakSpectrum filter_spec;
148  // first smooth the data to prevent outliers from destroying the fit
149  for (ConvexHull2D::PointArrayType::const_iterator it = current_section.begin(); it != current_section.end(); it++)
150  {
151  LocalPeakType p;
152  p.setMZ(it->getX());
153  p.setIntensity(it->getY());
154  filter_spec.push_back(p);
155  }
156 
157  // add two peaks at the beginning and at the end for better fit
158  // therefore calculate average distance first
159  std::vector<double> distances;
160  for (Size j = 1; j < filter_spec.size(); ++j)
161  {
162  distances.push_back(filter_spec[j].getMZ() - filter_spec[j - 1].getMZ());
163  }
164  double dist_average = std::accumulate(distances.begin(), distances.end(), 0.0) / (double) distances.size();
165 
166  // append peaks
167  Peak1D new_peak;
168  new_peak.setIntensity(0);
169  new_peak.setMZ(filter_spec.back().getMZ() + dist_average);
170  filter_spec.push_back(new_peak);
171  new_peak.setMZ(filter_spec.back().getMZ() + dist_average);
172  filter_spec.push_back(new_peak);
173  new_peak.setMZ(filter_spec.back().getMZ() + dist_average);
174  filter_spec.push_back(new_peak);
175 
176  // prepend peaks
177  new_peak.setMZ(filter_spec.front().getMZ() - dist_average);
178  filter_spec.insert(filter_spec.begin(), new_peak);
179  new_peak.setMZ(filter_spec.front().getMZ() - dist_average);
180  filter_spec.insert(filter_spec.begin(), new_peak);
181  new_peak.setMZ(filter_spec.front().getMZ() - dist_average);
182  filter_spec.insert(filter_spec.begin(), new_peak);
183 
184  // To get an estimate of the peak quality, we probably should not smooth
185  // and/or transform the data.
186  if (smooth_data)
187  {
188  GaussFilter filter;
189  Param filter_param(filter.getParameters());
190  filter.setParameters(filter_param);
191  filter_param.setValue("gaussian_width", 4 * dist_average);
192  filter.setParameters(filter_param);
193  filter.filter(filter_spec);
194  }
195 
196  // transform the data for fitting and fit RT profile
197  for (Size j = 0; j != filter_spec.size(); ++j)
198  {
199  LocalPeakType p;
200  p.setPosition(filter_spec[j].getMZ());
201  p.setIntensity(filter_spec[j].getIntensity());
202  data_to_fit.push_back(p);
203  }
204  }
205 
207  };
208 
209 }
210 
double
OpenMS::EmgFitter1D::fit1d
QualityType fit1d(const RawDataArrayType &range, std::unique_ptr< InterpolationModel > &model) override
return interpolation model
OpenMS::Feature::getConvexHulls
const std::vector< ConvexHull2D > & getConvexHulls() const
Non-mutable access to the convex hulls.
OpenMS::Peak1D::setMZ
void setMZ(CoordinateType mz)
Mutable access to m/z.
Definition: Peak1D.h:119
OpenMS::Constants::k
const double k
Definition: Constants.h:153
OpenMS::String
A more convenient string class.
Definition: String.h:58
OpenMS::EmgScoring::calcElutionFitScore
double calcElutionFitScore(MRMFeature &mrmfeature, MRMTransitionGroup< SpectrumType, TransitionT > &transition_group) const
calculate the elution profile fit score
Definition: EmgScoring.h:84
OpenMS::GaussFilter::filter
void filter(MSSpectrum &spectrum)
Smoothes an MSSpectrum containing profile data.
OpenMS::Size
size_t Size
Size type e.g. used as variable which can hold result of size()
Definition: Types.h:127
OpenMS::Peak1D::setIntensity
void setIntensity(IntensityType intensity)
Mutable access to the data point intensity (height)
Definition: Peak1D.h:110
OpenMS::EmgScoring::EmgScoring
EmgScoring()=default
OPENMS_PRECONDITION
#define OPENMS_PRECONDITION(condition, message)
Precondition macro.
Definition: openms/include/OpenMS/CONCEPT/Macros.h:120
GaussFilter.h
OpenMS::MRMFeature
A multi-chromatogram MRM feature.
Definition: MRMFeature.h:50
MRMTransitionGroup.h
OpenMS::ConvexHull2D::PointArrayType
std::vector< PointType > PointArrayType
Definition: ConvexHull2D.h:76
OpenMS
Main OpenMS namespace.
Definition: FeatureDeconvolution.h:47
OpenMS::MRMFeature::getFeature
Feature & getFeature(const String &key)
get a specified feature
MRMFeature.h
OpenMS::EmgScoring::elutionModelFit
double elutionModelFit(const ConvexHull2D::PointArrayType &current_section, bool smooth_data) const
Definition: EmgScoring.h:108
OpenMS::EmgFitter1D
Exponentially modified gaussian distribution fitter (1-dim.) using Levenberg-Marquardt algorithm (Eig...
Definition: EmgFitter1D.h:47
EmgFitter1D.h
OpenMS::DefaultParamHandler::setParameters
void setParameters(const Param &param)
Sets the parameters.
OpenMS::DefaultParamHandler::getDefaults
const Param & getDefaults() const
Non-mutable access to the default parameters.
OpenMS::DefaultParamHandler::getParameters
const Param & getParameters() const
Non-mutable access to the parameters.
OpenMS::Peak1D
A 1-dimensional raw data point or peak.
Definition: Peak1D.h:53
OpenMS::EmgScoring::fitter_emg1D_params_
Param fitter_emg1D_params_
Definition: EmgScoring.h:206
OpenMS::EmgScoring
Scoring of an elution peak using an exponentially modified gaussian distribution model.
Definition: EmgScoring.h:60
OpenMS::Feature
An LC-MS feature.
Definition: Feature.h:70
OpenMS::EmgScoring::~EmgScoring
~EmgScoring()=default
OpenMS::MRMTransitionGroup
The representation of a group of transitions in a targeted proteomics experiment.
Definition: MRMTransitionGroup.h:67
OpenMS::Param
Management and storage of parameters / INI files.
Definition: Param.h:69
OpenMS::EmgScoring::getDefaults
Param getDefaults()
Get default params for the Emg1D fitting.
Definition: EmgScoring.h:77
EmgModel.h
OpenMS::EmgScoring::setFitterParam
void setFitterParam(const Param &param)
Definition: EmgScoring.h:71
OpenMS::MRMTransitionGroup::getChromatograms
std::vector< ChromatogramType > & getChromatograms()
Definition: MRMTransitionGroup.h:186
OpenMS::MSSpectrum
The representation of a 1D spectrum.
Definition: MSSpectrum.h:66
OpenMS::MRMTransitionGroup::size
Size size() const
Definition: MRMTransitionGroup.h:125
StandardTypes.h
OpenMS::GaussFilter
This class represents a Gaussian lowpass-filter which works on uniform as well as on non-uniform prof...
Definition: GaussFilter.h:70
MSSpectrum.h
OpenMS::EmgScoring::fitRT_
double fitRT_(std::vector< LocalPeakType > &rt_input_data, std::unique_ptr< InterpolationModel > &model) const
Definition: EmgScoring.h:132
OpenMS::EmgScoring::prepareFit_
void prepareFit_(const ConvexHull2D::PointArrayType &current_section, std::vector< LocalPeakType > &data_to_fit, bool smooth_data) const
Definition: EmgScoring.h:144