void DetectabilitySimulation::svmFilter_(FeatureMapSim& features)
{
// transform featuremap to peptides vector
vector<String> peptides_vector(features.size());
for (Size i = 0; i < features.size(); ++i)
{
peptides_vector[i] = features[i].getPeptideIdentifications()[0].getHits()[0].getSequence().toUnmodifiedString();
}
vector<DoubleReal> labels;
vector<DoubleReal> detectabilities;
predictDetectabilities(peptides_vector, labels, detectabilities);
// copy all meta data stored in the feature map
FeatureMapSim temp_copy(features);
temp_copy.clear(false);
for (Size i = 0; i < peptides_vector.size(); ++i)
{
if (detectabilities[i] > min_detect_)
{
features[i].setMetaValue("detectability", detectabilities[i]);
temp_copy.push_back(features[i]);
}
#ifdef DEBUG_SIM
cout << detectabilities[i] << " " << min_detect_ << endl;
#endif
}
features.swap(temp_copy);
}
/**
@brief Gets a feature map containing the peptides and predicts for those the retention times
*/
void RTSimulation::predictRT(FeatureMapSim& features)
{
LOG_INFO << "RT Simulation ... started" << std::endl;
vector<DoubleReal> predicted_retention_times;
bool is_relative = (param_.getValue("auto_scale") == "true");
if (param_.getValue("rt_column") == "none")
{
noRTColumn_(features);
return;
}
// CE or HPLC:
else if (param_.getValue("rt_column") == "CE")
{
calculateMT_(features, predicted_retention_times);
}
else if (param_.getValue("rt_column") == "HPLC")
{
vector<AASequence> peptides_aa_vector(features.size());
for (Size i = 0; i < features.size(); ++i)
{
peptides_aa_vector[i] = features[i].getPeptideIdentifications()[0].getHits()[0].getSequence();
}
wrapSVM(peptides_aa_vector, predicted_retention_times);
}
// rt error dicing
SimCoordinateType rt_offset = param_.getValue("variation:affine_offset");
SimCoordinateType rt_scale = param_.getValue("variation:affine_scale");
SimCoordinateType rt_ft_stddev = param_.getValue("variation:feature_stddev");
FeatureMapSim fm_tmp(features);
fm_tmp.clear(false);
StringList deleted_features;
for (Size i = 0; i < predicted_retention_times.size(); ++i)
{
// relative -> absolute RT's (with border)
if (is_relative)
{
predicted_retention_times[i] *= total_gradient_time_;
}
//overwrite RT (if given by user)
if (features[i].metaValueExists("rt"))
{
predicted_retention_times[i] = features[i].getMetaValue("rt");
}
// add variation
SimCoordinateType rt_error = gsl_ran_gaussian(rnd_gen_->technical_rng, rt_ft_stddev) + rt_offset;
predicted_retention_times[i] = predicted_retention_times[i] * rt_scale + rt_error;
//overwrite RT [no randomization] (if given by user)
if (features[i].metaValueExists("RT"))
{
predicted_retention_times[i] = features[i].getMetaValue("RT");
}
// remove invalid peptides & (later) display removed ones
if (
(predicted_retention_times[i] < 0.0) || // check for invalid RT
(predicted_retention_times[i] > gradient_max_) || // check if RT is not in scan window
(predicted_retention_times[i] < gradient_min_) // check if RT is not in scan window
)
{
deleted_features.push_back(features[i].getPeptideIdentifications()[0].getHits()[0].getSequence().toUnmodifiedString() + " [" +
String::number(predicted_retention_times[i], 2)
+ "]");
continue;
}
features[i].setRT(predicted_retention_times[i]);
// determine shape parameters for EGH
DoubleReal variance = egh_variance_location_ + (egh_variance_scale_ == 0 ? 0 : gsl_ran_cauchy(rnd_gen_->technical_rng, egh_variance_scale_));
DoubleReal tau = egh_tau_location_ + (egh_tau_scale_ == 0 ? 0 : gsl_ran_cauchy(rnd_gen_->technical_rng, egh_tau_scale_));
// resample variance if it is below 0
// try this only 10 times to avoid endless loop in case of
// a bad parameter combination
Size retry_variance_sampling = 0;
while ((variance <= 0 || (fabs(variance - egh_variance_location_) > 10 * egh_variance_scale_)) && retry_variance_sampling < 9)
{
variance = egh_variance_location_ + gsl_ran_cauchy(rnd_gen_->technical_rng, egh_variance_scale_);
++retry_variance_sampling;
}
if (variance <= 0 || (fabs(variance - egh_variance_location_) > 10 * egh_variance_scale_))
{
LOG_ERROR << "Sigma^2 was negative, resulting in a feature with width=0. Tried to resample 10 times and then stopped. Setting it to the user defined width value of " << egh_variance_location_ << "!" << std::endl;
variance = egh_variance_location_;
}
// resample tau if the value is to big
// try this only 10 times to avoid endless loop in case of
// a bad parameter combination
Size retry_tau_sampling = 0;
while (fabs(tau - egh_tau_location_) > 10 * egh_tau_scale_ && retry_tau_sampling < 9)
{
tau = egh_tau_location_ + gsl_ran_cauchy(rnd_gen_->technical_rng, egh_tau_scale_);
++retry_tau_sampling;
}
if (fabs(tau - egh_tau_location_) > 10 * egh_tau_scale_)
{
LOG_ERROR << "Tau is to big for a reasonable feature. Tried to resample 10 times and then stopped. Setting it to the user defined skewness value of " << egh_tau_location_ << "!" << std::endl;
tau = egh_tau_location_;
}
features[i].setMetaValue("RT_egh_variance", variance);
features[i].setMetaValue("RT_egh_tau", tau);
fm_tmp.push_back(features[i]);
}
// print invalid features:
if (deleted_features.size() > 0)
{
LOG_WARN << "RT prediction gave 'invalid' results for " << deleted_features.size() << " peptide(s), making them unobservable.\n";
if (deleted_features.size() < 100)
LOG_WARN << " " << ListUtils::concatenate(deleted_features, "\n ") << std::endl;
else
LOG_WARN << " (List is too big to show)" << std::endl;
}
// only retain valid features:
features.swap(fm_tmp);
features.sortByPosition();
features.updateRanges();
}
