// There are fixed magic numbers in this function that should not be changed
// (even though actual tolerances or values in config file might differ)
// changing these numbers without retraining all models might give unanticipated results
void PeakList::calculateLogRandomProbabilities(vector<float>& logIntensities,
vector<float>& logRandomProbabilities) const
{
if (numPeaks_<2)
return;
const float oneOverSqrt2pi = 1.0 / sqrt(2.0*3.1415927);
const float logBias = log(1.2); // to fine tune the zero probabilites
const mass_t peakWindowSize = 0.6; // this is fixed and independent of the tolerance!!!
const mass_t margin = 25.0; // fixed and independent of values in config file
const mass_t windowSize = 100.0; // fixed and independent of values in config file
const mass_t minPeakMass = peaks_[0].mass;
const mass_t maxPeakMass = peaks_[numPeaks_-1].mass;
const mass_t visibleRange = (maxPeakMass - minPeakMass);
logRandomProbabilities.resize(numPeaks_);
int i;
for (i=0; i<numPeaks_; i++)
{
const mass_t peakMass = peaks_[i].mass;
const mass_t relativePosition = (peakMass-minPeakMass)/visibleRange;
const mass_t leftWindow = margin + relativePosition * windowSize;
const mass_t rightWindow = margin + windowSize - leftWindow;
const PeakRange pr = findPeaksInRange(peakMass - leftWindow, peakMass + rightWindow);
const float peakWindowProb = peakWindowSize /(leftWindow + rightWindow);
// some freak cases have 0 peak counts (only in unix)
const int numPeaksInRange = (pr.num_peaks>0 ? pr.num_peaks : 1);
const float zeroProbability = pow((1.0 - peakWindowProb),numPeaksInRange);
if (numPeaksInRange<5)
{
logRandomProbabilities[i] = log(1.0-zeroProbability) + logBias;
}
else // compute special probability based on peak densitiy model
{
vector<float> windowLogIntensities;
int j;
for (j=pr.low_idx; j<=pr.high_idx; j++)
windowLogIntensities.push_back(logIntensities[j]);
float mean=0,sd=1;
calc_mean_sd(windowLogIntensities,&mean,&sd);
const float e = (logIntensities[i] - mean)/sd;
if (e<0)
{
logRandomProbabilities[i] = log(1 - zeroProbability) + logBias;
}
else
{
const float normalizedValue = (oneOverSqrt2pi/ sd) * exp(-0.5*e*e);
const float normalizationConstant = (1.0 - zeroProbability) / (oneOverSqrt2pi / sd); //
logRandomProbabilities[i] = log(normalizedValue*normalizationConstant);
}
}
}
}
/***********************************************************************
calcs for each peak the probability of observing it at random (based on
the neighbor's distribution. Assumes the log_intens are distributed
according to a normal disribution.
************************************************************************/
void Spectrum::set_log_random_probs()
{
if (numPeaks_<2)
return;
const float log_add = log(1.2);
const float one_over_sqr_2pi = 1.0 / sqrt(2*3.1415927);
const mass_t peak_window_size = 0.6; // this is fixed and independent of the tolerance!
const mass_t margin = 25.0;
const mass_t window_size = 100.0;
const mass_t min_mass = peaks_[0].mass;
const mass_t max_mass = peaks_[numPeaks_-1].mass;
const mass_t viz_range = (max_mass - min_mass);
logRandomProbabilities_.resize(numPeaks_);
int i;
for (i=0; i<numPeaks_; i++)
{
const mass_t peak_mass = peaks_[i].mass;
const mass_t rel_position = (peak_mass-min_mass)/viz_range;
const mass_t left_window = margin + rel_position * window_size;
const mass_t right_window = margin + window_size - left_window;
const PeakRange pr = findPeaksInRange(peak_mass-left_window,peak_mass+right_window);
const float peak_window_prob = peak_window_size /(left_window + right_window);
// some freak cases have 0 peak counts (only in unix)
const int num_peaks_in_range = (pr.num_peaks>0 ? pr.num_peaks : 1);
const float zero_prob = pow((1.0 - peak_window_prob),num_peaks_in_range);
if (pr.num_peaks<5)
{
logRandomProbabilities_[i] = log(1.0-zero_prob) + log_add;
}
else
{
vector<float> log_intens;
int j;
for (j=pr.low_idx; j<=pr.high_idx; j++)
log_intens.push_back(logIntensities_[j]);
float mean=0,sd=1;
calc_mean_sd(log_intens,&mean,&sd);
const float e = (logIntensities_[i] - mean)/sd;
if (e<0)
{
logRandomProbabilities_[i] = log(1 - zero_prob) + log_add;
}
else
{
const float norm = (one_over_sqr_2pi/ sd) * exp(-0.5*e*e);
const float norm_const = (1 - zero_prob) / (one_over_sqr_2pi/ sd); //
logRandomProbabilities_[i] = log(norm*norm_const);
}
}
}
}
void PMCSQS_Scorer::output_pmc_rank_results(const FileManager& fm,
int charge,
const vector<SingleSpectrumFile *>& test_ssfs)
{
BasicSpecReader bsr;
static QCPeak peaks[5000];
vector<int> org_offset_counts, new_offset_counts;
org_offset_counts.resize(201,0);
new_offset_counts.resize(201,0);
vector<mass_t> org_offsets;
vector<mass_t> corr_offsets;
org_offsets.clear();
corr_offsets.clear();
int i;
for (i=0; i<test_ssfs.size(); i++)
{
SingleSpectrumFile* ssf = test_ssfs[i];
BasicSpectrum bs;
bs.num_peaks = bsr.read_basic_spec(config,fm,ssf,peaks);
bs.peaks = peaks;
bs.ssf = ssf;
init_for_current_spec(config,bs);
calculate_curr_spec_pmc_values(bs, bin_increment);
PmcSqsChargeRes res;
find_best_mz_values_from_rank_model(bs, charge, config->get_pm_tolerance(),res);
ssf->peptide.calc_mass(config);
mass_t true_mz = (ssf->peptide.get_mass() + 18.01 + charge)/charge;
org_offsets.push_back(true_mz - ssf->m_over_z);
corr_offsets.push_back(true_mz - res.mz1);
}
mass_t m_org,sd_org,m_corr,sd_corr;
calc_mean_sd(org_offsets,&m_org, &sd_org);
calc_mean_sd(corr_offsets,&m_corr,&sd_corr);
cout << "CHARGE: " << charge << endl;
cout << "ORG: mean " << m_org << " " << sd_org << endl;
cout << "CORR: mean " << m_corr << " " << sd_corr << endl;
for (i=0; i<org_offsets.size(); i++)
{
int org_idx = 100 + int(org_offsets[i] * 20);
if (org_idx<0)
org_idx = 0;
if (org_idx>200)
org_idx=200;
org_offset_counts[org_idx]++;
int new_idx = 100 + int(corr_offsets[i] * 20);
if (new_idx<0)
new_idx = 0;
if (new_idx>200)
new_idx=200;
new_offset_counts[new_idx]++;
}
int cum_org=0;
int cum_new=0;
for (i=0; i<=200; i++)
{
if (org_offset_counts[i]==0 && new_offset_counts[i]==0)
continue;
cum_org+=org_offset_counts[i];
cum_new+=new_offset_counts[i];
cout << fixed << setprecision(3) << i*0.05 - 5.0 << "\t" <<
org_offset_counts[i]/(float)org_offsets.size() << "\t" <<
new_offset_counts[i]/(float)corr_offsets.size() << "\t" <<
cum_org/(float)org_offsets.size() << "\t"<<
cum_new/(float)corr_offsets.size() << endl;
}
}
