MasstraceCorrelator.h

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// --------------------------------------------------------------------------
//                   OpenMS -- Open-Source Mass Spectrometry               
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// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
// ETH Zurich, and Freie Universitaet Berlin 2002-2020.
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// $Maintainer: Hannes Roest $
// $Authors: Hannes Roest $
// --------------------------------------------------------------------------
 
#pragma once
 
#include <OpenMS/DATASTRUCTURES/DefaultParamHandler.h>
#include <OpenMS/CONCEPT/ProgressLogger.h>
#include <OpenMS/KERNEL/ConsensusMap.h>
#include <OpenMS/KERNEL/MSExperiment.h>
 
namespace OpenMS
{
 
  class OPENMS_DLLAPI MasstraceCorrelator : 
    public DefaultParamHandler,
    public ProgressLogger
  {
 
  public:
 
    MasstraceCorrelator();
 
    ~MasstraceCorrelator() override;
 
    // a mass trace is a vector of pairs in (RT, Intensity)
    typedef std::vector<std::pair<double, double> > MasstracePointsType;
 
    void createPseudoSpectra(const ConsensusMap& map, MSExperiment& pseudo_spectra,
        Size min_peak_nr, double min_correlation, int max_lag,
        double max_rt_apex_difference);
 
    /* Score two mass traces against each other
     *
     * This function scores two mass traces (vector of <RT,Intensity>) against each other:
     *
     *  - The algorithm first creates 2 arrays that contain matched intensities
     *    in RT-space (accounting for missing data points and unequal length)
     *  - Next, these arrays are scored using cross-correlation scores and
     *    pearson coefficients.
     *
     * @note The pairs need to be sorted by the first entry (RT)
     *
     * @param hull_points1 The first input masstrace
     * @param hull_points2 The second input masstrace
     * @param lag The computed lag (output coelution score) 
     * @param lag_intensity The computed intensity at the lag (output shape score) 
     * @param pearson_score The computed pearson score (output)
     * @param min_corr Minimal correlation needed to proceed computing the cross-correlations
     * @param max_lag Currently unused
     * @param mindiff Minimal differences for matching up the two mass traces
     *
    */
    void scoreHullpoints(const MasstracePointsType& hull_points1,
                         const MasstracePointsType& hull_points2,
                         int& lag,
                         double& lag_intensity,
                         double& pearson_score,
                         const double min_corr,
                         const int max_lag,
                         const double mindiff = 0.1);
 
    /* Create a cache of the features in a consensus map
     *
     * This creates a cache of the input consensus map by creating the
     * following data structures:
     *  - a vector of mass traces (each mass trace is simply a vector of <RT,Intensity>
     *  - a vector of maximal intensities (max_rt, max_int)
     *  - a vector of retention times of the feature
     *
     * @param map The input consensus map
     * @param feature_points The list of all mass traces
     * @param max_intensities The list of maximal intensities 
     * @param rt_cache The list of retention times of all features
    */
    void createConsensusMapCache(const ConsensusMap& map,
                                 std::vector<MasstracePointsType>& feature_points,
                                 std::vector<std::pair<double, double> >& max_intensities,
                                 std::vector<double>& rt_cache);
 
  protected:
 
    void matchMassTraces_(const MasstracePointsType& hull_points1,
                          const MasstracePointsType& hull_points2,
                          std::vector<double>& vec1,
                          std::vector<double>& vec2,
                          double mindiff,
                          double padEnds = true);
  };
}