void ConsensusMap::updateRanges()
{
clearRanges();
updateRanges_(begin(), end());
// enlarge the range by the internal points of each feature
for (Size i = 0; i < size(); ++i)
{
for (ConsensusFeature::HandleSetType::const_iterator it = operator[](i).begin(); it != operator[](i).end(); ++it)
{
DoubleReal rt = it->getRT();
DoubleReal mz = it->getMZ();
DoubleReal intensity = it->getIntensity();
// update RT
if (rt < pos_range_.minPosition()[Peak2D::RT])
{
pos_range_.setMinX(rt);
}
if (rt > pos_range_.maxPosition()[Peak2D::RT])
{
pos_range_.setMaxX(rt);
}
// update m/z
if (mz < pos_range_.minPosition()[Peak2D::MZ])
{
pos_range_.setMinY(mz);
}
if (mz > pos_range_.maxPosition()[Peak2D::MZ])
{
pos_range_.setMaxY(mz);
}
// update intensity
if (intensity < int_range_.minX())
{
int_range_.setMinX(intensity);
}
if (intensity > int_range_.maxX())
{
int_range_.setMaxX(intensity);
}
}
}
}
void EDTAFile::store(const String& filename, const ConsensusMap& map) const
{
TextFile tf;
// search for maximum number of sub-features (since this determines the number of columns)
Size max_sub(0);
for (Size i = 0; i < map.size(); ++i)
{
max_sub = std::max(max_sub, map[i].getFeatures().size());
}
// write header
String header("RT\tm/z\tintensity\tcharge");
for (Size i = 1; i <= max_sub; ++i)
{
header += "\tRT" + String(i) + "\tm/z" + String(i) + "\tintensity" + String(i) + "\tcharge" + String(i);
}
tf.addLine(header);
for (Size i = 0; i < map.size(); ++i)
{
ConsensusFeature f = map[i];
// consensus
String entry = String(f.getRT()) + "\t" + f.getMZ() + "\t" + f.getIntensity() + "\t" + f.getCharge();
// sub-features
ConsensusFeature::HandleSetType handle = f.getFeatures();
for (ConsensusFeature::HandleSetType::const_iterator it = handle.begin(); it != handle.end(); ++it)
{
entry += String("\t") + it->getRT() + "\t" + it->getMZ() + "\t" + it->getIntensity() + "\t" + it->getCharge();
}
// missing sub-features
for (Size j = handle.size(); j < max_sub; ++j)
{
entry += "\tNA\tNA\tNA\tNA";
}
tf.addLine(entry);
}
tf.store(filename);
}
void ProteinInference::infer_(ConsensusMap & consensus_map,
const size_t protein_idenfication_index,
const UInt reference_map)
{
ProteinIdentification & protein_ident = consensus_map.getProteinIdentifications()[protein_idenfication_index];
for (size_t i = 0; i < protein_ident.getHits().size(); ++i)
{
// Protein Accession
String accession = protein_ident.getHits()[i].getAccession();
// consensus feature -> peptide hit
Map<size_t, PeptideHit> consensus_to_peptide;
// search for it in consensus elements:
for (size_t i_cm = 0; i_cm < consensus_map.size(); ++i_cm)
{
std::vector<PeptideHit> peptide_hits;
for (std::vector<PeptideIdentification>::iterator it_pepid = consensus_map[i_cm].getPeptideIdentifications().begin();
it_pepid != consensus_map[i_cm].getPeptideIdentifications().end();
++it_pepid)
{
// are Protein- and PeptideIdentification from the same search engine run?
if (it_pepid->getIdentifier() != protein_ident.getIdentifier())
continue;
std::vector<PeptideHit> peptide_hits_local;
it_pepid->getReferencingHits(accession, peptide_hits_local);
if (peptide_hits_local.empty())
continue;
if (sortByUnique_(peptide_hits_local, it_pepid->isHigherScoreBetter())) // we found a unique peptide
{
peptide_hits.push_back(peptide_hits_local[0]);
}
}
// if several PeptideIdentifications (==Spectra) were assigned to current ConsensusElement
// --> take the best (as above), e.g. in SILAC this could happen
// TODO: better idea?
if (!peptide_hits.empty())
{
if (sortByUnique_(peptide_hits, consensus_map[i_cm].getPeptideIdentifications()[0].isHigherScoreBetter())) //found a unique peptide for current ConsensusElement
{
consensus_to_peptide[i_cm] = peptide_hits[0];
#ifdef DEBUG_INFERENCE
std::cout << "assign peptide " << peptide_hits[0].getSequence() << " to Protein " << accession << std::endl;
#endif
}
}
} // ! ConsensusMap loop
// no peptides found that match current Protein
if (consensus_to_peptide.empty())
continue;
// Use all matching ConsensusElements to derive a quantitation for current protein
// build up ratios for every map vs reference
double coverage = 0;
Map<Size, std::vector<IntensityType> > ratios;
// number of unique peptides pointing to current protein
UInt coverage_count = (UInt)consensus_to_peptide.size();
for (Map<size_t, PeptideHit>::iterator it_pephits = consensus_to_peptide.begin();
it_pephits != consensus_to_peptide.end();
++it_pephits)
{
coverage += it_pephits->second.getSequence().size();
const ConsensusFeature::HandleSetType & handles = consensus_map[it_pephits->first].getFeatures();
//search if reference is present
ConsensusFeature::HandleSetType::const_iterator it_ref = handles.end();
for (ConsensusFeature::HandleSetType::const_iterator it = handles.begin();
it != handles.end();
++it)
{
if (it->getMapIndex() == reference_map)
{
it_ref = it;
break;
}
}
// did not find a reference
// TODO assume intensity==0 instead??
if (it_ref == handles.end())
continue;
for (ConsensusFeature::HandleSetType::const_iterator it = handles.begin();
it != handles.end();
++it)
{
ratios[it->getMapIndex()].push_back(it->getIntensity() / it_ref->getIntensity());
}
}
// sort ratios map-wise and take median
for (ConsensusMap::FileDescriptions::const_iterator it_file = consensus_map.getFileDescriptions().begin();
it_file != consensus_map.getFileDescriptions().end();
++it_file)
{
if (ratios.has(it_file->first))
{
//sort intensity ratios for map #it_file->first
std::sort(ratios[it_file->first].begin(), ratios[it_file->first].end());
//take median
IntensityType protein_ratio = ratios[it_file->first][ratios[it_file->first].size() / 2];
//TODO if ratios have high variance emit a warning!
protein_ident.getHits()[i].setMetaValue(String("ratio_") + String(it_file->first), protein_ratio);
}
} // ! map loop
// % coverage of protein by peptides
coverage /= DoubleReal(protein_ident.getHits()[i].getSequence().size()) / 100;
protein_ident.getHits()[i].setMetaValue("coverage", coverage);
protein_ident.getHits()[i].setMetaValue("hits", coverage_count);
} // ! Protein loop
// protein_to_peptides now contains the Protein -> Peptides mapping
// lets estimate the
}
void IBSpectraFile::store(const String& filename, const ConsensusMap& cm)
{
// typdefs for shorter code
typedef std::vector<ProteinHit>::iterator ProtHitIt;
// general settings .. do we need to expose these?
// ----------------------------------------------------------------------
/// Allow also non-unique peptides to be exported
bool allow_non_unique = true;
/// Intensities below this value will be set to 0.0 to avoid numerical problems when quantifying
double intensity_threshold = 0.00001;
// ----------------------------------------------------------------------
// guess experiment type
boost::shared_ptr<IsobaricQuantitationMethod> quantMethod = guessExperimentType_(cm);
// we need the protein identifications to reference the protein names
ProteinIdentification protIdent;
bool has_proteinIdentifications = false;
if (cm.getProteinIdentifications().size() > 0)
{
protIdent = cm.getProteinIdentifications()[0];
has_proteinIdentifications = true;
}
// start the file by adding the tsv header
TextFile textFile;
textFile.addLine(ListUtils::concatenate(constructHeader_(*quantMethod), "\t"));
for (ConsensusMap::ConstIterator cm_iter = cm.begin();
cm_iter != cm.end();
++cm_iter)
{
const ConsensusFeature& cFeature = *cm_iter;
std::vector<IdCSV> entries;
/// 1st we extract the identification information from the consensus feature
if (cFeature.getPeptideIdentifications().size() == 0 || !has_proteinIdentifications)
{
// we store unidentified hits anyway, because the iTRAQ quant is still helpful for normalization
entries.push_back(IdCSV());
}
else
{
// protein name:
const PeptideHit& peptide_hit = cFeature.getPeptideIdentifications()[0].getHits()[0];
std::set<String> protein_accessions = peptide_hit.extractProteinAccessions();
if (protein_accessions.size() != 1)
{
if (!allow_non_unique) continue; // we only want unique peptides
}
for (std::set<String>::const_iterator prot_ac = protein_accessions.begin(); prot_ac != protein_accessions.end(); ++prot_ac)
{
IdCSV entry;
entry.charge = cFeature.getPeptideIdentifications()[0].getHits()[0].getCharge();
entry.peptide = cFeature.getPeptideIdentifications()[0].getHits()[0].getSequence().toUnmodifiedString();
entry.theo_mass = cFeature.getPeptideIdentifications()[0].getHits()[0].getSequence().getMonoWeight(Residue::Full, cFeature.getPeptideIdentifications()[0].getHits()[0].getCharge());
// write modif
entry.modif = getModifString_(cFeature.getPeptideIdentifications()[0].getHits()[0].getSequence());
ProtHitIt proteinHit = protIdent.findHit(*prot_ac);
if (proteinHit == protIdent.getHits().end())
{
std::cerr << "Protein referenced in peptide not found...\n";
continue; // protein not found
}
entry.accession = proteinHit->getAccession();
entries.push_back(entry);
}
}
// 2nd we add the quantitative information of the channels
// .. skip features with 0 intensity
if (cFeature.getIntensity() == 0)
{
continue;
}
for (std::vector<IdCSV>::iterator entry = entries.begin();
entry != entries.end();
++entry)
{
// set parent intensity
entry->parent_intens = cFeature.getIntensity();
entry->retention_time = cFeature.getRT();
entry->spectrum = cFeature.getUniqueId();
entry->exp_mass = cFeature.getMZ();
// create output line
StringList currentLine;
// add entry to currentLine
entry->toStringList(currentLine);
// extract channel intensities and positions
std::map<Int, double> intensityMap;
ConsensusFeature::HandleSetType features = cFeature.getFeatures();
for (ConsensusFeature::HandleSetType::const_iterator fIt = features.begin();
fIt != features.end();
++fIt)
{
intensityMap[Int(fIt->getMZ())] = (fIt->getIntensity() > intensity_threshold ? fIt->getIntensity() : 0.0);
}
for (IsobaricQuantitationMethod::IsobaricChannelList::const_iterator it = quantMethod->getChannelInformation().begin();
it != quantMethod->getChannelInformation().end();
++it)
{
currentLine.push_back(String(it->center));
}
for (IsobaricQuantitationMethod::IsobaricChannelList::const_iterator it = quantMethod->getChannelInformation().begin();
it != quantMethod->getChannelInformation().end();
++it)
{
currentLine.push_back(String(intensityMap[int(it->center)]));
}
textFile.addLine(ListUtils::concatenate(currentLine, "\t"));
}
}
// write to file
textFile.store(filename);
}
//features
TEST_EQUAL(map.size(), 6)
ConsensusFeature cons_feature = map[0];
TEST_REAL_SIMILAR(cons_feature.getRT(), 1273.27)
TEST_REAL_SIMILAR(cons_feature.getMZ(), 904.47)
TEST_REAL_SIMILAR(cons_feature.getIntensity(), 3.12539e+07)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().minPosition()[0], 1273.27)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().maxPosition()[0], 1273.27)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().minPosition()[1], 904.47)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().maxPosition()[1], 904.47)
TEST_REAL_SIMILAR(cons_feature.getIntensityRange().minPosition()[0], 3.12539e+07)
TEST_REAL_SIMILAR(cons_feature.getIntensityRange().maxPosition()[0], 3.12539e+07)
TEST_REAL_SIMILAR(cons_feature.getQuality(), 1.1)
TEST_EQUAL(cons_feature.getMetaValue("peptide_id") == DataValue("RefSeq:NC_1234"), true)
ConsensusFeature::HandleSetType::const_iterator it = cons_feature.begin();
TEST_REAL_SIMILAR(it->getIntensity(), 3.12539e+07)
cons_feature = map[5];
TEST_REAL_SIMILAR(cons_feature.getRT(), 1194.82)
TEST_REAL_SIMILAR(cons_feature.getMZ(), 777.101)
TEST_REAL_SIMILAR(cons_feature.getIntensity(), 1.78215e+07)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().minPosition()[0], 1194.82)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().maxPosition()[0], 1194.82)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().minPosition()[1], 777.101)
TEST_REAL_SIMILAR(cons_feature.getPositionRange().maxPosition()[1], 777.101)
TEST_REAL_SIMILAR(cons_feature.getIntensityRange().minPosition()[0], 1.78215e+07)
TEST_REAL_SIMILAR(cons_feature.getIntensityRange().maxPosition()[0], 1.78215e+07)
TEST_REAL_SIMILAR(cons_feature.getQuality(), 0.0)
it = cons_feature.begin();
TEST_REAL_SIMILAR(it->getIntensity(), 1.78215e+07)
++ it;
