void ConsensusIDAlgorithm::apply(vector<PeptideIdentification>& ids,
Size number_of_runs)
{
// abort if no IDs present
if (ids.empty())
{
return;
}
number_of_runs_ = (number_of_runs != 0) ? number_of_runs : ids.size();
// prepare data here, so that it doesn't have to happen in each algorithm:
for (vector<PeptideIdentification>::iterator pep_it = ids.begin();
pep_it != ids.end(); ++pep_it)
{
pep_it->sort();
if ((considered_hits_ > 0) &&
(pep_it->getHits().size() > considered_hits_))
{
pep_it->getHits().resize(considered_hits_);
}
}
// make sure there are no duplicated hits (by sequence):
IDFilter::removeDuplicatePeptideHits(ids, true);
SequenceGrouping results;
apply_(ids, results); // actual (subclass-specific) processing
String score_type = ids[0].getScoreType();
bool higher_better = ids[0].isHigherScoreBetter();
ids.clear();
ids.resize(1);
ids[0].setScoreType(score_type);
ids[0].setHigherScoreBetter(higher_better);
for (SequenceGrouping::iterator res_it = results.begin();
res_it != results.end(); ++res_it)
{
OPENMS_PRECONDITION(!res_it->second.second.empty(),
"Consensus score for peptide required");
PeptideHit hit;
if (res_it->second.second.size() == 2)
{
// filter by "support" value:
double support = res_it->second.second[1];
if (support < min_support_) continue;
hit.setMetaValue("consensus_support", support);
}
hit.setSequence(res_it->first);
hit.setCharge(res_it->second.first);
hit.setScore(res_it->second.second[0]);
ids[0].insertHit(hit);
#ifdef DEBUG_ID_CONSENSUS
LOG_DEBUG << " - Output hit: " << hit.getSequence() << " "
<< hit.getScore() << endl;
#endif
}
ids[0].assignRanks();
}
void IDDecoyProbability::apply(vector<PeptideIdentification> & ids)
{
double lower_score_better_default_value_if_zero(static_cast<double>(param_.getValue("lower_score_better_default_value_if_zero")));
double lower_score_better_default_value_if_zero_exp = pow(10.0, -lower_score_better_default_value_if_zero);
vector<double> rev_scores, fwd_scores, all_scores;
// get the forward scores
for (vector<PeptideIdentification>::iterator it = ids.begin(); it != ids.end(); ++it)
{
String score_type = it->getScoreType();
if (it->getHits().size() > 0)
{
vector<PeptideHit> hits = it->getHits();
for (vector<PeptideHit>::iterator pit = hits.begin(); pit != hits.end(); ++pit)
{
double score = pit->getScore();
pit->setMetaValue(score_type + "_Score", score);
if (!it->isHigherScoreBetter())
{
if (score < lower_score_better_default_value_if_zero_exp)
{
score = lower_score_better_default_value_if_zero;
}
else
{
score = -log10(score);
}
}
String target_decoy = (String)pit->getMetaValue("target_decoy");
if (target_decoy == "target")
{
fwd_scores.push_back(score);
}
else if (target_decoy == "decoy")
{
rev_scores.push_back(score);
}
all_scores.push_back(score);
}
it->setHits(hits);
}
}
#ifdef IDDECOYPROBABILITY_DEBUG
cerr << ids.size() << " " << rev_scores.size() << " " << fwd_scores.size() << " " << all_scores.size() << endl;
#endif
apply_(ids, rev_scores, fwd_scores, all_scores);
return;
}
auto apply(Xs && ... xs) const
{
constexpr size_t N = function_arity<F>::value - sizeof...(Ts);
static_assert(N >= sizeof...(Xs), "Too many argument!");
return apply_(std::integral_constant<size_t, N - sizeof...(Xs)>(), std::make_index_sequence<sizeof...(Xs)>(), std::forward<Xs>(xs)...);
}
void IDDecoyProbability::apply(vector<PeptideIdentification> & prob_ids, const vector<PeptideIdentification> & orig_fwd_ids, const vector<PeptideIdentification> & rev_ids)
{
double lower_score_better_default_value_if_zero((double)param_.getValue("lower_score_better_default_value_if_zero"));
double lower_score_better_default_value_if_zero_exp = pow((double)10.0, -lower_score_better_default_value_if_zero);
vector<PeptideIdentification> fwd_ids = orig_fwd_ids;
vector<double> rev_scores, fwd_scores, all_scores;
// get the forward scores
for (vector<PeptideIdentification>::iterator it = fwd_ids.begin(); it != fwd_ids.end(); ++it)
{
String score_type = it->getScoreType();
if (it->getHits().size() > 0)
{
vector<PeptideHit> hits = it->getHits();
for (vector<PeptideHit>::iterator pit = hits.begin(); pit != hits.end(); ++pit)
{
double score = pit->getScore();
pit->setMetaValue(score_type + "_Score", score);
if (!it->isHigherScoreBetter())
{
if (score < lower_score_better_default_value_if_zero_exp)
{
score = lower_score_better_default_value_if_zero;
}
else
{
score = -log10(score);
}
}
fwd_scores.push_back(score);
all_scores.push_back(score);
}
it->setHits(hits);
}
}
// get the reverse scores
for (vector<PeptideIdentification>::const_iterator it = rev_ids.begin(); it != rev_ids.end(); ++it)
{
if (it->getHits().size() > 0)
{
for (vector<PeptideHit>::const_iterator pit = it->getHits().begin(); pit != it->getHits().end(); ++pit)
{
double score = pit->getScore();
if (!it->isHigherScoreBetter())
{
if (score < lower_score_better_default_value_if_zero_exp)
{
score = lower_score_better_default_value_if_zero;
}
else
{
score = -log10(score);
}
}
rev_scores.push_back(score);
all_scores.push_back(score);
}
}
}
prob_ids = fwd_ids;
apply_(prob_ids, rev_scores, fwd_scores, all_scores);
return;
}
virtual y_type eval(x_arg_type x, const std::array<double, sizeof...(ARGS)>& p) {
return apply_(index_bind<1>(_Fcn, x), p);
}