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// OpenMS -- Open-Source Mass Spectrometry
// --------------------------------------------------------------------------
// Copyright The OpenMS Team -- Eberhard Karls University Tuebingen,
// ETH Zurich, and Freie Universitaet Berlin 2002-2013.
//
// This software is released under a three-clause BSD license:
// * Redistributions of source code must retain the above copyright
// notice, this list of conditions and the following disclaimer.
// * Redistributions in binary form must reproduce the above copyright
// notice, this list of conditions and the following disclaimer in the
// documentation and/or other materials provided with the distribution.
// * Neither the name of any author or any participating institution
// may be used to endorse or promote products derived from this software
// without specific prior written permission.
// For a full list of authors, refer to the file AUTHORS.
// --------------------------------------------------------------------------
// THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
// AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
// IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
// ARE DISCLAIMED. IN NO EVENT SHALL ANY OF THE AUTHORS OR THE CONTRIBUTING
// INSTITUTIONS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
// EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
// PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
// OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
// WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
// OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
// ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
//
// --------------------------------------------------------------------------
// $Maintainer: Stephan Aiche $
// $Authors: Stephan Aiche $
// --------------------------------------------------------------------------
#ifndef OPENMS_TRANSFORMATIONS_FEATUREFINDER_EGHTRACEFITTER_H
#define OPENMS_TRANSFORMATIONS_FEATUREFINDER_EGHTRACEFITTER_H
#include <sstream>
#include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/FeatureFinderAlgorithmPickedHelperStructs.h>
#include <OpenMS/TRANSFORMATIONS/FEATUREFINDER/TraceFitter.h>
namespace OpenMS
{
/**
* @brief A RT Profile fitter using an Exponential Gaussian Hybrid background model
*
* Lan K, Jorgenson JW.
* <b>A hybrid of exponential and gaussian functions as a simple model of asymmetric chromatographic peaks.</b>
* <em>Journal of Chromatography A.</em> 915 (1-2)p. 1-13.
* Available at: http://linkinghub.elsevier.com/retrieve/pii/S0021967301005945
*
* @htmlinclude OpenMS_EGHTraceFitter.parameters
*
* @experimental Needs further testing on real data. Note that the tests are currently also focused on testing the EGH as replacement for the gaussian.
*/
template <class PeakType>
class EGHTraceFitter :
public TraceFitter<PeakType>
{
public:
EGHTraceFitter()
{
//setName("EGHTraceFitter");
}
EGHTraceFitter(const EGHTraceFitter & other) :
TraceFitter<PeakType>(other)
{
this->height_ = other.height_;
this->apex_rt_ = other.apex_rt_;
this->sigma_square_ = other.sigma_square_;
this->tau_ = other.tau_;
this->sigma_5_bound_ = other.sigma_5_bound_;
this->fwhm_bound_ = other.fwhm_bound_;
updateMembers_();
}
EGHTraceFitter & operator=(const EGHTraceFitter & source)
{
TraceFitter<PeakType>::operator=(source);
this->height_ = source.height_;
this->apex_rt_ = source.apex_rt_;
this->sigma_square_ = source.sigma_square_;
this->tau_ = source.tau_;
this->sigma_5_bound_ = source.sigma_5_bound_;
this->fwhm_bound_ = source.fwhm_bound_;
updateMembers_();
return *this;
}
virtual ~EGHTraceFitter()
{
}
// override important methods
void fit(FeatureFinderAlgorithmPickedHelperStructs::MassTraces<PeakType> & traces)
{
setInitialParameters_(traces);
double x_init[NUM_PARAMS_] = {height_, apex_rt_, sigma_square_, tau_};
Size num_params = NUM_PARAMS_;
TraceFitter<PeakType>::optimize_(traces, num_params, x_init,
&(EGHTraceFitter<PeakType>::residual_),
&(EGHTraceFitter<PeakType>::jacobian_),
&(EGHTraceFitter<PeakType>::evaluate_));
}
DoubleReal getLowerRTBound() const
{
return sigma_5_bound_.first;
}
DoubleReal getTau() const
{
return tau_;
}
DoubleReal getUpperRTBound() const
{
return sigma_5_bound_.second;
}
DoubleReal getHeight() const
{
return height_;
}
DoubleReal getSigmaSquare() const
{
return sigma_square_;
}
DoubleReal getCenter() const
{
return apex_rt_;
}
bool checkMaximalRTSpan(const DoubleReal max_rt_span)
{
return (sigma_5_bound_.second - sigma_5_bound_.first) > max_rt_span * region_rt_span_;
}
virtual bool checkMinimalRTSpan(const std::pair<DoubleReal, DoubleReal> & rt_bounds, const DoubleReal min_rt_span)
{
return (rt_bounds.second - rt_bounds.first) < min_rt_span * (sigma_5_bound_.second - sigma_5_bound_.first);
}
DoubleReal computeTheoretical(const FeatureFinderAlgorithmPickedHelperStructs::MassTrace<PeakType> & trace, Size k)
{
double rt = trace.peaks[k].first;
double t_diff, t_diff2, denominator = 0.0;
double fegh = 0.0;
t_diff = rt - apex_rt_;
t_diff2 = t_diff * t_diff; // -> (t - t_R)^2
denominator = 2 * sigma_square_ + tau_ * t_diff; // -> 2\sigma_{g}^{2} + \tau \left(t - t_R\right)
if (denominator > 0.0)
{
fegh = trace.theoretical_int * height_ * exp(-t_diff2 / denominator);
}
return fegh;
}
virtual DoubleReal getFeatureIntensityContribution()
{
return height_ * (fwhm_bound_.second - fwhm_bound_.first);
}
DoubleReal getFWHM() const
{
std::pair<DoubleReal, DoubleReal> bounds = getAlphaBoundaries_(0.5);
return bounds.second - bounds.first;
}
virtual String getGnuplotFormula(FeatureFinderAlgorithmPickedHelperStructs::MassTrace<PeakType> const & trace, const char function_name, const DoubleReal baseline, const DoubleReal rt_shift)
{
std::stringstream s;
s << String(function_name) << "(x)= " << baseline << " + ";
s << "("; // the overall bracket
s << "((" << 2 * sigma_square_ << " + " << tau_ << " * (x - " << (rt_shift + apex_rt_) << " )) > 0) ? "; // condition
s << (trace.theoretical_int * height_) << " * exp(-1 * (x - " << (rt_shift + apex_rt_) << ")**2 " <<
"/" <<
" ( " << 2 * sigma_square_ << " + " << tau_ << " * (x - " << (rt_shift + apex_rt_) << " )))";
s << " : 0)";
return String(s.str());
}
protected:
DoubleReal apex_rt_;
DoubleReal height_;
DoubleReal sigma_square_;
DoubleReal tau_;
std::pair<DoubleReal, DoubleReal> sigma_5_bound_;
std::pair<DoubleReal, DoubleReal> fwhm_bound_;
DoubleReal region_rt_span_;
static const Size NUM_PARAMS_ = 4;
/**
* @brief Return an ordered pair of the positions where the EGH reaches a height of alpha * height of the EGH
*
* @param alpha The alpha at which the boundaries should be computed
*/
std::pair<DoubleReal, DoubleReal> getAlphaBoundaries_(const DoubleReal alpha) const
{
std::pair<DoubleReal, DoubleReal> bounds;
DoubleReal L = log(alpha);
DoubleReal s = sqrt(
((L * tau_) * (L * tau_) / 4) - 2 * L * sigma_square_
);
DoubleReal s1, s2;
s1 = (-1 * (L * tau_) / 2) + s;
s2 = (-1 * (L * tau_) / 2) - s;
// the smaller one (should be < 0) = lower bound
bounds.first = apex_rt_ + std::min(s1, s2);
// bigger one (should be > 0) = upper bound
bounds.second = apex_rt_ + std::max(s1, s2);
return bounds;
}
void getOptimizedParameters_(gsl_multifit_fdfsolver * fdfsolver)
{
height_ = gsl_vector_get(fdfsolver->x, 0);
apex_rt_ = gsl_vector_get(fdfsolver->x, 1);
sigma_square_ = gsl_vector_get(fdfsolver->x, 2);
tau_ = gsl_vector_get(fdfsolver->x, 3);
// we set alpha to 0.04 which is conceptually equal to
// 2.5 sigma for lower and upper bound
sigma_5_bound_ = getAlphaBoundaries_(0.043937);
// this is needed for the intensity contribution -> this is the 1.25 sigma region
fwhm_bound_ = getAlphaBoundaries_(0.45783);
}
static Int residual_(const gsl_vector * param, void * data, gsl_vector * f)
{
FeatureFinderAlgorithmPickedHelperStructs::MassTraces<PeakType> * traces = static_cast<FeatureFinderAlgorithmPickedHelperStructs::MassTraces<PeakType> *>(data);
double H = gsl_vector_get(param, 0);
double tR = gsl_vector_get(param, 1);
double sigma_square = gsl_vector_get(param, 2);
double tau = gsl_vector_get(param, 3);
double t_diff, t_diff2, denominator = 0.0;
double fegh = 0.0;
UInt count = 0;
for (Size t = 0; t < traces->size(); ++t)
{
FeatureFinderAlgorithmPickedHelperStructs::MassTrace<PeakType> & trace = traces->at(t);
for (Size i = 0; i < trace.peaks.size(); ++i)
{
DoubleReal rt = trace.peaks[i].first;
t_diff = rt - tR;
t_diff2 = t_diff * t_diff; // -> (t - t_R)^2
denominator = 2 * sigma_square + tau * t_diff; // -> 2\sigma_{g}^{2} + \tau \left(t - t_R\right)
if (denominator > 0.0)
{
fegh = traces->baseline + trace.theoretical_int * H * exp(-t_diff2 / denominator);
}
else
{
fegh = 0.0;
}
gsl_vector_set(f, count, (fegh - trace.peaks[i].second->getIntensity()));
++count;
}
}
return GSL_SUCCESS;
}
static Int jacobian_(const gsl_vector * param, void * data, gsl_matrix * J)
{
FeatureFinderAlgorithmPickedHelperStructs::MassTraces<PeakType> * traces = static_cast<FeatureFinderAlgorithmPickedHelperStructs::MassTraces<PeakType> *>(data);
double H = gsl_vector_get(param, 0);
double tR = gsl_vector_get(param, 1);
double sigma_square = gsl_vector_get(param, 2);
double tau = gsl_vector_get(param, 3);
double derivative_H, derivative_tR, derivative_sigma_square, derivative_tau = 0.0;
double t_diff, t_diff2, exp1, denominator = 0.0;
UInt count = 0;
for (Size t = 0; t < traces->size(); ++t)
{
FeatureFinderAlgorithmPickedHelperStructs::MassTrace<PeakType> & trace = traces->at(t);
for (Size i = 0; i < trace.peaks.size(); ++i)
{
DoubleReal rt = trace.peaks[i].first;
t_diff = rt - tR;
t_diff2 = t_diff * t_diff; // -> (t - t_R)^2
denominator = 2 * sigma_square + tau * t_diff; // -> 2\sigma_{g}^{2} + \tau \left(t - t_R\right)
if (denominator > 0)
{
exp1 = exp(-t_diff2 / denominator);
// \partial H f_{egh}(t) = \exp\left( \frac{-\left(t-t_R \right)}{2\sigma_{g}^{2} + \tau \left(t - t_R\right)} \right)
derivative_H = trace.theoretical_int * exp1;
// \partial t_R f_{egh}(t) &=& H \exp \left( \frac{-\left(t-t_R \right)}{2\sigma_{g}^{2} + \tau \left(t - t_R\right)} \right) \left( \frac{\left( 4 \sigma_{g}^{2} + \tau \left(t-t_R \right) \right) \left(t-t_R \right)}{\left( 2\sigma_{g}^{2} + \tau \left(t - t_R\right) \right)^2} \right)
derivative_tR = trace.theoretical_int * H * exp1 * (((4 * sigma_square + tau * t_diff) * t_diff) / (denominator * denominator));
// \partial \sigma_{g}^{2} f_{egh}(t) &=& H \exp \left( \frac{-\left(t-t_R \right)^2}{2\sigma_{g}^{2} + \tau \left(t - t_R\right)} \right) \left( \frac{ 2 \left(t - t_R\right)^2}{\left( 2\sigma_{g}^{2} + \tau \left(t - t_R\right) \right)^2} \right)
derivative_sigma_square = trace.theoretical_int * H * exp1 * ((2 * t_diff2) / (denominator * denominator));
// \partial \tau f_{egh}(t) &=& H \exp \left( \frac{-\left(t-t_R \right)^2}{2\sigma_{g}^{2} + \tau \left(t - t_R\right)} \right) \left( \frac{ \left(t - t_R\right)^3}{\left( 2\sigma_{g}^{2} + \tau \left(t - t_R\right) \right)^2} \right)
derivative_tau = trace.theoretical_int * H * exp1 * ((t_diff * t_diff2) / (denominator * denominator));
}
else
{
derivative_H = 0.0;
derivative_tR = 0.0;
derivative_sigma_square = 0.0;
derivative_tau = 0.0;
}
// set the jacobian matrix
gsl_matrix_set(J, count, 0, derivative_H);
gsl_matrix_set(J, count, 1, derivative_tR);
gsl_matrix_set(J, count, 2, derivative_sigma_square);
gsl_matrix_set(J, count, 3, derivative_tau);
++count;
}
}
return GSL_SUCCESS;
}
static Int evaluate_(const gsl_vector * param, void * data, gsl_vector * f, gsl_matrix * J)
{
residual_(param, data, f);
jacobian_(param, data, J);
return GSL_SUCCESS;
}
void setInitialParameters_(FeatureFinderAlgorithmPickedHelperStructs::MassTraces<PeakType> & traces)
{
LOG_DEBUG << "EGHTraceFitter->setInitialParameters(..)" << std::endl;
LOG_DEBUG << "Traces length: " << traces.size() << std::endl;
LOG_DEBUG << "Max trace: " << traces.max_trace << std::endl;
// initial values for externals
height_ = traces[traces.max_trace].max_peak->getIntensity() - traces.baseline;
LOG_DEBUG << "height: " << height_ << std::endl;
apex_rt_ = traces[traces.max_trace].max_rt;
LOG_DEBUG << "apex_rt: " << apex_rt_ << std::endl;
region_rt_span_ = traces[traces.max_trace].peaks.back().first - traces[traces.max_trace].peaks[0].first;
LOG_DEBUG << "region_rt_span_: " << region_rt_span_ << std::endl;
const PeakType * max_peak = traces[traces.max_trace].peaks.begin()->second;
Size max_pos = 0;
for (Size i = 1; i < traces[traces.max_trace].peaks.size(); ++i)
{
if (traces[traces.max_trace].peaks[i].second->getIntensity() > max_peak->getIntensity())
{
max_peak = traces[traces.max_trace].peaks[i].second;
max_pos = i;
}
}
Size i = max_pos;
LOG_DEBUG << "max_pos: " << max_pos << std::endl;
if (traces[traces.max_trace].peaks.size() < 3)
{
// TODO: abort the whole thing here??
// because below we REQUIRE at least three peaks!!!
}
Size filter_max_pos = traces[traces.max_trace].peaks.size() - 2;
// compute a smoothed value for the maxima
// if the maximum is close to the borders, we need to think of something...
DoubleReal smoothed_height;
if ((max_pos < 2) || (max_pos + 2 >= traces[traces.max_trace].peaks.size()))
{
// ... too close to border... no smoothing
smoothed_height = traces[traces.max_trace].peaks[max_pos].second->getIntensity();
// TODO: does this trace even make sense?! why wasn't it extended it further? or should we have skipped it beforehand?
}
else
{
smoothed_height = (traces[traces.max_trace].peaks[max_pos - 2].second->getIntensity()
+ traces[traces.max_trace].peaks[max_pos - 1].second->getIntensity()
+ traces[traces.max_trace].peaks[max_pos].second->getIntensity()
+ traces[traces.max_trace].peaks[max_pos + 1].second->getIntensity()
+ traces[traces.max_trace].peaks[max_pos + 2].second->getIntensity()) / 5.0;
}
// use moving average filter to avoid bad initial values
// moving average of size 5
// TODO: optimize windows size
while (i > 2 && i < filter_max_pos)
{
// compute smoothed
DoubleReal smoothed = (traces[traces.max_trace].peaks[i - 2].second->getIntensity()
+ traces[traces.max_trace].peaks[i - 1].second->getIntensity()
+ traces[traces.max_trace].peaks[i].second->getIntensity()
+ traces[traces.max_trace].peaks[i + 1].second->getIntensity()
+ traces[traces.max_trace].peaks[i + 2].second->getIntensity()) / 5.0;
if (smoothed / smoothed_height < 0.5) break;
else --i;
}
LOG_DEBUG << "Left alpha at " << i << " with " << traces[traces.max_trace].peaks[i].first << std::endl;
double A = apex_rt_ - traces[traces.max_trace].peaks[i].first;
i = max_pos;
while (i < filter_max_pos && i > 2)
{
DoubleReal smoothed = (traces[traces.max_trace].peaks[i - 2].second->getIntensity()
+ traces[traces.max_trace].peaks[i - 1].second->getIntensity()
+ traces[traces.max_trace].peaks[i].second->getIntensity()
+ traces[traces.max_trace].peaks[i + 1].second->getIntensity()
+ traces[traces.max_trace].peaks[i + 2].second->getIntensity()) / 5.0;
if (smoothed / smoothed_height < 0.5) break;
else ++i;
}
LOG_DEBUG << "Right alpha at " << i << " with " << traces[traces.max_trace].peaks[i].first << std::endl;
double B = traces[traces.max_trace].peaks[i].first - apex_rt_;
//LOG_DEBUG << "A: " << A << std::endl;
//LOG_DEBUG << "B: " << B << std::endl;
// compute estimates for tau / sigma_square based on A/B
double log_alpha = log(0.5);
tau_ = (-1 / log_alpha) * (B - A);
LOG_DEBUG << "tau: " << tau_ << std::endl;
sigma_square_ = (-1 / (2 * log_alpha)) * (B * A);
LOG_DEBUG << "sigma_square: " << sigma_square_ << std::endl;
}
virtual void updateMembers_()
{
TraceFitter<PeakType>::updateMembers_();
}
void printState_(SignedSize iter, gsl_multifit_fdfsolver * s)
{
LOG_DEBUG << "iter: " << iter << " "
<< "height: " << gsl_vector_get(s->x, 0) << " "
<< "apex_rt: " << gsl_vector_get(s->x, 1) << " "
<< "sigma_square: " << gsl_vector_get(s->x, 2) << " "
<< "tau: " << gsl_vector_get(s->x, 3) << " "
<< "|f(x)| = " << gsl_blas_dnrm2(s->f) << std::endl;
}
};
} // namespace OpenMS
#endif // #ifndef OPENMS_TRANSFORMATIONS_FEATUREFINDER_FEATUREFINDERALGORITHMPICKEDTRACEFITTERGAUSS_H
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