void FidoInterface::run()
{
dogridSearch = !(alpha != -1 && beta != -1 && gamma != -1);
double peptidePrior_local = peptidePrior;
if(computePriors)
{
peptidePrior_local = estimatePriors();
if(VERB > 1)
{
std::cerr << "The estimated peptide level prior probability is : " << peptidePrior_local << std::endl;
}
}
double local_protein_threshold = proteinThreshold;
if(truncate) local_protein_threshold = 0.0;
proteinGraph = new GroupPowerBigraph (alpha,beta,gamma,nogroupProteins,noseparate,noprune,trivialGrouping);
proteinGraph->setMaxAllowedConfigurations(max_allow_configurations);
proteinGraph->setPeptidePrior(peptidePrior_local);
if(reduceTree && dogridSearch)
{
//NOTE lets create a smaller tree to estimate the parameters faster
if(VERB > 1)
{
std::cerr << "Reducing the tree of proteins to increase the speed of the grid search.." << std::endl;
}
proteinGraph->setProteinThreshold(reduced_proteinThreshold);
proteinGraph->setPsmThreshold(reduced_psmThreshold);
proteinGraph->setPeptideThreshold(reduced_peptideThreshold);
proteinGraph->setGroupProteins(false);
proteinGraph->setSeparateProteins(false);
proteinGraph->setPruneProteins(false);
proteinGraph->setTrivialGrouping(true);
proteinGraph->setMultipleLabeledPeptides(false);
}
else
{
proteinGraph->setProteinThreshold(local_protein_threshold);
proteinGraph->setPsmThreshold(psmThreshold);
proteinGraph->setPeptideThreshold(peptideThreshold);
proteinGraph->setMultipleLabeledPeptides(allow_multiple_labeled_peptides);
}
}
bool FidoInterface::initialize(Scores& peptideScores, const Enzyme* enzyme) {
doGridSearch_ = !(alpha_ != -1 && beta_ != -1 && gamma_ != -1);
localPeptidePrior_ = kPeptidePrior;
if (kComputePriors) {
if (absenceRatio_ != 1.0) {
localPeptidePrior_ = 1 - absenceRatio_;
} else {
localPeptidePrior_ = estimatePriors(peptideScores);
}
if (VERB > 1) {
std::cerr << "The estimated peptide level prior probability is : " << localPeptidePrior_ << std::endl;
}
}
peptideScorePtr_ = &peptideScores;
return ProteinProbEstimator::initialize(peptideScores, enzyme);
}
void FidoInterface::run() {
doGridSearch_ = !(alpha_ != -1 && beta_ != -1 && gamma_ != -1);
double localPeptidePrior = kPeptidePrior;
if (kComputePriors) {
if (absenceRatio_ != 1.0) {
localPeptidePrior = 1 - absenceRatio_;
} else {
localPeptidePrior = estimatePriors();
}
if (VERB > 1) {
std::cerr << "The estimated peptide level prior probability is : " << localPeptidePrior << std::endl;
}
}
proteinGraph_ = new GroupPowerBigraph(alpha_, beta_, gamma_, noClustering_, noPartitioning_, noPruning_, trivialGrouping_);
proteinGraph_->setMaxAllowedConfigurations(LOG_MAX_ALLOWED_CONFIGURATIONS);
proteinGraph_->setPeptidePrior(localPeptidePrior);
if (gridSearchThreshold_ > 0.0 && doGridSearch_) {
//NOTE lets create a smaller tree to estimate the parameters faster
if (VERB > 1) {
std::cerr << "Reducing the tree of proteins to increase the speed of the grid search.." << std::endl;
}
proteinGraph_->setProteinThreshold(gridSearchThreshold_);
proteinGraph_->setPsmThreshold(gridSearchThreshold_);
proteinGraph_->setPeptideThreshold(gridSearchThreshold_);
proteinGraph_->setNoClustering(false); // i.e. do clustering
proteinGraph_->setNoPartitioning(false); // i.e. do partitioning
proteinGraph_->setNoPruning(false); // i.e. do pruning
proteinGraph_->setTrivialGrouping(true);
proteinGraph_->setMultipleLabeledPeptides(false);
} else {
proteinGraph_->setProteinThreshold(proteinThreshold_);
proteinGraph_->setPsmThreshold(kPsmThreshold);
proteinGraph_->setPeptideThreshold(kPeptideThreshold);
proteinGraph_->setMultipleLabeledPeptides(kAddPeptideDecoyLabel);
}
}