libopenms-dev 1.11.1-3 / usr / include / OpenMS / ANALYSIS / ID / PILISNeutralLossModel.h

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//                   OpenMS -- Open-Source Mass Spectrometry
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// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
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// $Maintainer: Andreas Bertsch $
// $Authors: Andreas Bertsch $
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#ifndef OPENMS_ANALYSIS_ID_PILISNEUTRALLOSSMODEL_H
#define OPENMS_ANALYSIS_ID_PILISNEUTRALLOSSMODEL_H

#include <vector>
#include <OpenMS/KERNEL/StandardTypes.h>
#include <OpenMS/DATASTRUCTURES/Map.h>
#include <OpenMS/DATASTRUCTURES/String.h>
#include <OpenMS/CONCEPT/Types.h>
#include <OpenMS/ANALYSIS/ID/HiddenMarkovModel.h>
#include <OpenMS/DATASTRUCTURES/DefaultParamHandler.h>

namespace OpenMS
{
  class AASequence;

  /**
    @brief This class implements the simulation of the spectra from PILIS

      PILIS uses a HMM based structure to model the population of fragment ions
      from a peptide. The spectrum generator can be accessed via the getSpectrum
      method.

      @htmlinclude OpenMS_PILISNeutralLossModel.parameters

      @ingroup Analysis_ID
  */
  class OPENMS_DLLAPI PILISNeutralLossModel :
    public DefaultParamHandler
  {
    friend class PILISNeutralLossModelGenerator;

public:

    /** @name Constructors and destructors
    */
    //@{
    /// default constructor
    PILISNeutralLossModel();

    /// copy constructor
    PILISNeutralLossModel(const PILISNeutralLossModel & model);

    /// destructor
    virtual ~PILISNeutralLossModel();
    //@}

    /// assignment operator
    PILISNeutralLossModel & operator=(const PILISNeutralLossModel & mode);

    /** @name Accessors
    */
    //@{
    /// performs a training step; needs as parameters a spectrum with annotated sequence and charge; returns the intensity sum of the matched peaks
    DoubleReal train(const RichPeakSpectrum & spec, const AASequence & peptide, DoubleReal ion_weight, UInt charge, DoubleReal peptide_weight);

    /// given a peptide (a ion) the model returns the peaks with intensities relative to initial_prob
    void getIons(std::vector<RichPeak1D> & peaks, const AASequence & peptide, DoubleReal initial_prob);

    /// sets the hidden markov model
    void setHMM(const HiddenMarkovModel & model);

    /// writes the HMM to the given file in the GraphML format. A detailed description of the GraphML format can be found under http://graphml.graphdrawing.org/
    const HiddenMarkovModel & getHMM() const;

    /// generates the models
    void generateModel();

    /// this method evaluates the model after training; it should be called after all training steps with train
    void evaluate();
    //@}

protected:

    /// extracts the precursor and related intensities of a training spectrum
    DoubleReal getIntensitiesFromSpectrum_(const RichPeakSpectrum & train_spec, Map<String, DoubleReal> & pre_ints, DoubleReal ion_weight, const AASequence & peptide, UInt charge);

    /// trains precursor and related peaks
    void trainIons_(DoubleReal initial_probability, const Map<String, DoubleReal> & intensities, const AASequence & peptide);

    /// estimates the precursor intensities
    void getIons_(Map<String, DoubleReal> & intensities, DoubleReal initial_probability, const AASequence & precursor);

    /// enables the states needed for precursor training/simulation
    void enableIonStates_(const AASequence & peptide);

    /// precursor model used
    HiddenMarkovModel hmm_precursor_;

    ///
    UInt num_explicit_;

    void updateMembers_();
  };
}
#endif