void SeedListGenerator::generateSeedLists(const ConsensusMap& consensus,
Map<UInt64, SeedList>& seed_lists)
{
seed_lists.clear();
// iterate over all consensus features...
for (ConsensusMap::ConstIterator cons_it = consensus.begin();
cons_it != consensus.end(); ++cons_it)
{
DPosition<2> point(cons_it->getRT(), cons_it->getMZ());
// for each sub-map in the consensus map, add a seed at the position of
// this consensus feature:
for (ConsensusMap::FileDescriptions::const_iterator file_it =
consensus.getFileDescriptions().begin(); file_it !=
consensus.getFileDescriptions().end(); ++file_it)
seed_lists[file_it->first].push_back(point);
// for each feature contained in the consensus feature, remove the seed of
// the corresponding map:
for (ConsensusFeature::HandleSetType::const_iterator feat_it =
cons_it->getFeatures().begin(); feat_it !=
cons_it->getFeatures().end(); ++feat_it)
{
seed_lists[feat_it->getMapIndex()].pop_back();
}
// this leaves seeds for maps where no feature was found near the
// consensus position
}
}
ExitCodes main_(int, const char **)
{
String in = getStringOption_("in"), out = getStringOption_("out");
FileTypes::Type in_type = FileHandler::getType(in);
if (in_type == FileTypes::FEATUREXML)
{
FeatureMap<> features;
FeatureXMLFile().load(in, features);
for (FeatureMap<>::Iterator feat_it = features.begin();
feat_it != features.end(); ++feat_it)
{
resolveConflict_(feat_it->getPeptideIdentifications());
}
addDataProcessing_(features,
getProcessingInfo_(DataProcessing::FILTERING));
FeatureXMLFile().store(out, features);
}
else // consensusXML
{
ConsensusMap consensus;
ConsensusXMLFile().load(in, consensus);
for (ConsensusMap::Iterator cons_it = consensus.begin();
cons_it != consensus.end(); ++cons_it)
{
resolveConflict_(cons_it->getPeptideIdentifications());
}
addDataProcessing_(consensus,
getProcessingInfo_(DataProcessing::FILTERING));
ConsensusXMLFile().store(out, consensus);
}
return EXECUTION_OK;
}
void ConsensusMapNormalizerAlgorithmThreshold::normalizeMaps(ConsensusMap& map, const vector<double>& ratios)
{
ConsensusMap::Iterator cf_it;
ProgressLogger progresslogger;
progresslogger.setLogType(ProgressLogger::CMD);
progresslogger.startProgress(0, map.size(), "normalizing maps");
for (cf_it = map.begin(); cf_it != map.end(); ++cf_it)
{
progresslogger.setProgress(cf_it - map.begin());
ConsensusFeature::HandleSetType::const_iterator f_it;
for (f_it = cf_it->getFeatures().begin(); f_it != cf_it->getFeatures().end(); ++f_it)
{
f_it->asMutable().setIntensity(f_it->getIntensity() * ratios[f_it->getMapIndex()]);
}
}
progresslogger.endProgress();
}
void MapAlignmentAlgorithmPoseClustering::align(const ConsensusMap & map, TransformationDescription & trafo)
{
// TODO: move this to updateMembers_? (if consensusMap prevails)
// TODO: why does superimposer work on consensus map???
const ConsensusMap & map_model = reference_;
ConsensusMap map_scene = map;
// run superimposer to find the global transformation
TransformationDescription si_trafo;
superimposer_.run(map_model, map_scene, si_trafo);
// apply transformation to consensus features and contained feature
// handles
for (Size j = 0; j < map_scene.size(); ++j)
{
//Calculate new RT
double rt = map_scene[j].getRT();
rt = si_trafo.apply(rt);
//Set RT of consensus feature centroid
map_scene[j].setRT(rt);
//Set RT of consensus feature handles
map_scene[j].begin()->asMutable().setRT(rt);
}
//run pairfinder to find pairs
ConsensusMap result;
//TODO: add another 2map interface to pairfinder?
std::vector<ConsensusMap> input(2);
input[0] = map_model;
input[1] = map_scene;
pairfinder_.run(input, result);
// calculate the local transformation
si_trafo.invert(); // to undo the transformation applied above
TransformationDescription::DataPoints data;
for (ConsensusMap::Iterator it = result.begin(); it != result.end();
++it)
{
if (it->size() == 2) // two matching features
{
ConsensusFeature::iterator feat_it = it->begin();
double y = feat_it->getRT();
double x = si_trafo.apply((++feat_it)->getRT());
// one feature should be from the reference map:
if (feat_it->getMapIndex() != 0)
{
data.push_back(make_pair(x, y));
}
else
{
data.push_back(make_pair(y, x));
}
}
}
trafo = TransformationDescription(data);
trafo.fitModel("linear");
}
void FeatureGroupingAlgorithm::transferSubelements(const vector<ConsensusMap>& maps, ConsensusMap& out) const
{
// accumulate file descriptions from the input maps:
// cout << "Updating file descriptions..." << endl;
out.getFileDescriptions().clear();
// mapping: (map index, original id) -> new id
map<pair<Size, UInt64>, Size> mapid_table;
for (Size i = 0; i < maps.size(); ++i)
{
const ConsensusMap& consensus = maps[i];
for (ConsensusMap::FileDescriptions::const_iterator desc_it = consensus.getFileDescriptions().begin(); desc_it != consensus.getFileDescriptions().end(); ++desc_it)
{
Size counter = mapid_table.size();
mapid_table[make_pair(i, desc_it->first)] = counter;
out.getFileDescriptions()[counter] = desc_it->second;
}
}
// look-up table: input map -> unique ID -> consensus feature
// cout << "Creating look-up table..." << endl;
vector<map<UInt64, ConsensusMap::ConstIterator> > feat_lookup(maps.size());
for (Size i = 0; i < maps.size(); ++i)
{
const ConsensusMap& consensus = maps[i];
for (ConsensusMap::ConstIterator feat_it = consensus.begin();
feat_it != consensus.end(); ++feat_it)
{
// do NOT use "id_lookup[i][feat_it->getUniqueId()] = feat_it;" here as
// you will get "attempt to copy-construct an iterator from a singular
// iterator" in STL debug mode:
feat_lookup[i].insert(make_pair(feat_it->getUniqueId(), feat_it));
}
}
// adjust the consensus features:
// cout << "Adjusting consensus features..." << endl;
for (ConsensusMap::iterator cons_it = out.begin(); cons_it != out.end(); ++cons_it)
{
ConsensusFeature adjusted = ConsensusFeature(
static_cast<BaseFeature>(*cons_it)); // remove sub-features
for (ConsensusFeature::HandleSetType::const_iterator sub_it = cons_it->getFeatures().begin(); sub_it != cons_it->getFeatures().end(); ++sub_it)
{
UInt64 id = sub_it->getUniqueId();
Size map_index = sub_it->getMapIndex();
ConsensusMap::ConstIterator origin = feat_lookup[map_index][id];
for (ConsensusFeature::HandleSetType::const_iterator handle_it = origin->getFeatures().begin(); handle_it != origin->getFeatures().end(); ++handle_it)
{
FeatureHandle handle = *handle_it;
Size new_id = mapid_table[make_pair(map_index, handle.getMapIndex())];
handle.setMapIndex(new_id);
adjusted.insert(handle);
}
}
*cons_it = adjusted;
}
}
void MapAlignmentTransformer::transformSingleConsensusMap(ConsensusMap & cmap,
const TransformationDescription & trafo)
{
for (ConsensusMap::Iterator cmit = cmap.begin(); cmit != cmap.end();
++cmit)
{
applyToConsensusFeature_(*cmit, trafo);
}
// adapt RT values of unassigned peptides:
if (!cmap.getUnassignedPeptideIdentifications().empty())
{
transformSinglePeptideIdentification(
cmap.getUnassignedPeptideIdentifications(), trafo);
}
}
void ConsensusMapNormalizerAlgorithmQuantile::setNormalizedIntensityValues(const vector<vector<double> >& feature_ints, ConsensusMap& map)
{
//assumes the input map and feature_ints are in the same order as in the beginning,
//although feature_ints has normalized values now (but the same ranks as before)
Size number_of_maps = map.getColumnHeaders().size();
ConsensusMap::ConstIterator cf_it;
vector<Size> progress_indices(number_of_maps);
for (cf_it = map.begin(); cf_it != map.end(); ++cf_it)
{
ConsensusFeature::HandleSetType::const_iterator f_it;
for (f_it = cf_it->getFeatures().begin(); f_it != cf_it->getFeatures().end(); ++f_it)
{
Size map_idx = f_it->getMapIndex();
double intensity = feature_ints[map_idx][progress_indices[map_idx]++];
f_it->asMutable().setIntensity(intensity);
}
}
}
void QuantitativeExperimentalDesign::mergeConsensusMaps_(ConsensusMap & out, const String & experiment, StringList & file_paths)
{
ConsensusMap map;
LOG_INFO << "Merge consensus maps: " << endl;
UInt counter = 1;
for (StringList::Iterator file_it = file_paths.begin(); file_it != file_paths.end(); ++file_it, ++counter)
{
//load should clear the map
ConsensusXMLFile().load(*file_it, map);
for (ConsensusMap::iterator it = map.begin(); it != map.end(); ++it)
{
it->setMetaValue("experiment", DataValue(experiment));
}
out += map;
}
LOG_INFO << endl;
}
void ConsensusMapNormalizerAlgorithmQuantile::extractIntensityVectors(const ConsensusMap& map, vector<vector<double> >& out_intensities)
{
//reserve space for out_intensities (unequal vector lengths, 0-features omitted)
Size number_of_maps = map.getColumnHeaders().size();
out_intensities.clear();
out_intensities.resize(number_of_maps);
for (UInt i = 0; i < number_of_maps; i++)
{
ConsensusMap::ColumnHeaders::const_iterator it = map.getColumnHeaders().find(i);
if (it == map.getColumnHeaders().end()) throw Exception::ElementNotFound(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION, String(i));
out_intensities[i].reserve(it->second.size);
}
//fill out_intensities
ConsensusMap::ConstIterator cf_it;
for (cf_it = map.begin(); cf_it != map.end(); ++cf_it)
{
ConsensusFeature::HandleSetType::const_iterator f_it;
for (f_it = cf_it->getFeatures().begin(); f_it != cf_it->getFeatures().end(); ++f_it)
{
out_intensities[f_it->getMapIndex()].push_back(f_it->getIntensity());
}
}
}
vector<double> ConsensusMapNormalizerAlgorithmThreshold::computeCorrelation(const ConsensusMap& map, const double& ratio_threshold, const String& acc_filter, const String& desc_filter)
{
Size number_of_features = map.size();
Size number_of_maps = map.getFileDescriptions().size();
vector<vector<double> > feature_int(number_of_maps);
//get map with most features, resize feature_int
UInt map_with_most_features_idx = 0;
ConsensusMap::FileDescriptions::const_iterator map_with_most_features = map.getFileDescriptions().find(0);
for (UInt i = 0; i < number_of_maps; i++)
{
feature_int[i].resize(number_of_features);
ConsensusMap::FileDescriptions::const_iterator it = map.getFileDescriptions().find(i);
if (it->second.size > map_with_most_features->second.size)
{
map_with_most_features = it;
map_with_most_features_idx = i;
}
}
//fill feature_int with intensities
Size pass_counter = 0;
ConsensusMap::ConstIterator cf_it;
UInt idx = 0;
for (cf_it = map.begin(); cf_it != map.end(); ++cf_it, ++idx)
{
if (!ConsensusMapNormalizerAlgorithmMedian::passesFilters_(cf_it, map, acc_filter, desc_filter))
{
continue;
}
++pass_counter;
ConsensusFeature::HandleSetType::const_iterator f_it;
for (f_it = cf_it->getFeatures().begin(); f_it != cf_it->getFeatures().end(); ++f_it)
{
feature_int[f_it->getMapIndex()][idx] = f_it->getIntensity();
}
}
LOG_INFO << endl << "Using " << pass_counter << "/" << map.size() << " consensus features for computing normalization coefficients" << endl << endl;
//determine ratio
vector<double> ratio_vector(number_of_maps);
for (UInt j = 0; j < number_of_maps; j++)
{
vector<double> ratios;
for (UInt k = 0; k < number_of_features; ++k)
{
if (feature_int[map_with_most_features_idx][k] != 0.0 && feature_int[j][k] != 0.0)
{
double ratio = feature_int[map_with_most_features_idx][k] / feature_int[j][k];
if (ratio > ratio_threshold && ratio < 1 / ratio_threshold)
{
ratios.push_back(ratio);
}
}
}
if (ratios.empty())
{
LOG_WARN << endl << "Not enough features passing filters. Cannot compute normalization coefficients for all maps. Result will be unnormalized." << endl << endl;
return vector<double>(number_of_maps, 1.0);
}
ratio_vector[j] = Math::mean(ratios.begin(), ratios.end());
}
return ratio_vector;
}
IsobaricChannelExtractor ice(q_method);
// disable activation filtering
Param p = ice.getParameters();
p.setValue("select_activation", "");
p.setValue("min_precursor_intensity", 5300000.0);
ice.setParameters(p);
// extract channels
ConsensusMap cm_out;
ice.extractChannels(exp, cm_out);
// compare results
TEST_EQUAL(cm_out.size(), 4)
ABORT_IF(cm_out.size() != 4)
for(ConsensusMap::Iterator cf = cm_out.begin(); cf != cm_out.end(); ++cf)
{
DoubleReal prec_intensity = cf->getMetaValue("precursor_intensity");
TEST_EQUAL(prec_intensity > 5300000.0, true)
}
}
{
// load test data
MSExperiment<Peak1D> exp;
MzMLFile mzmlfile;
mzmlfile.load(OPENMS_GET_TEST_DATA_PATH("IsobaricChannelExtractor_6.mzML"), exp);
// add some more information to the quant method
Param pItraq = q_method->getParameters();
pItraq.setValue("channel_114_description", "ref");
pItraq.setValue("channel_115_description", "something");
ExitCodes outputTo(ostream& os)
{
//-------------------------------------------------------------
// Parameter handling
//-------------------------------------------------------------
// File names
String in = getStringOption_("in");
// File type
FileHandler fh;
FileTypes::Type in_type = FileTypes::nameToType(getStringOption_("in_type"));
if (in_type == FileTypes::UNKNOWN)
{
in_type = fh.getType(in);
writeDebug_(String("Input file type: ") + FileTypes::typeToName(in_type), 2);
}
if (in_type == FileTypes::UNKNOWN)
{
writeLog_("Error: Could not determine input file type!");
return PARSE_ERROR;
}
MSExperiment<Peak1D> exp;
FeatureMap feat;
ConsensusMap cons;
if (in_type == FileTypes::FEATUREXML) //features
{
FeatureXMLFile().load(in, feat);
feat.updateRanges();
}
else if (in_type == FileTypes::CONSENSUSXML) //consensus features
{
ConsensusXMLFile().load(in, cons);
cons.updateRanges();
}
//-------------------------------------------------------------
// meta information
//-------------------------------------------------------------
if (getFlag_("m"))
{
os << endl
<< "-- General information --" << endl
<< endl
<< "file name: " << in << endl
<< "file type: " << FileTypes::typeToName(in_type) << endl;
//basic info
os << endl
<< "-- Meta information --" << endl
<< endl;
if (in_type == FileTypes::FEATUREXML) //features
{
os << "Document id : " << feat.getIdentifier() << endl << endl;
}
else if (in_type == FileTypes::CONSENSUSXML) //consensus features
{
os << "Document id : " << cons.getIdentifier() << endl << endl;
}
}
//-------------------------------------------------------------
// data processing
//-------------------------------------------------------------
if (getFlag_("p"))
{
//basic info
os << endl
<< "-- Data processing information --" << endl
<< endl;
//get data processing info
vector<DataProcessing> dp;
if (in_type == FileTypes::FEATUREXML) //features
{
dp = feat.getDataProcessing();
}
else if (in_type == FileTypes::CONSENSUSXML) //consensus features
{
dp = cons.getDataProcessing();
}
int i = 0;
for (vector<DataProcessing>::iterator it = dp.begin(); it != dp.end(); ++it)
{
os << "Data processing " << i << endl;
os << "\tcompletion_time: " << (*it).getCompletionTime().getDate() << 'T' << (*it).getCompletionTime().getTime() << endl;
os << "\tsoftware name: " << (*it).getSoftware().getName() << " version " << (*it).getSoftware().getVersion() << endl;
for (set<DataProcessing::ProcessingAction>::const_iterator paIt = (*it).getProcessingActions().begin(); paIt != (*it).getProcessingActions().end(); ++paIt)
{
os << "\t\tprocessing action: " << DataProcessing::NamesOfProcessingAction[*paIt] << endl;
}
}
++i;
}
//-------------------------------------------------------------
// statistics
//-------------------------------------------------------------
if (getFlag_("s"))
{
//-------------------------------------------------------------
// Content statistics
//-------------------------------------------------------------
Map<String, int> meta_names;
if (in_type == FileTypes::FEATUREXML) //features
{
os << "Number of features: " << feat.size() << endl
<< endl
<< "Ranges:" << endl
<< " retention time: " << String::number(feat.getMin()[Peak2D::RT], 2) << " : " << String::number(feat.getMax()[Peak2D::RT], 2) << endl
<< " mass-to-charge: " << String::number(feat.getMin()[Peak2D::MZ], 2) << " : " << String::number(feat.getMax()[Peak2D::MZ], 2) << endl
<< " intensity: " << String::number(feat.getMinInt(), 2) << " : " << String::number(feat.getMaxInt(), 2) << endl
<< endl;
// Charge distribution
Map<UInt, UInt> charges;
for (Size i = 0; i < feat.size(); ++i)
{
charges[feat[i].getCharge()]++;
}
os << "Charge distribution" << endl;
for (Map<UInt, UInt>::const_iterator it = charges.begin();
it != charges.end(); ++it)
{
os << "charge " << it->first << ": " << it->second << endl;
}
}
else if (in_type == FileTypes::CONSENSUSXML) //consensus features
{
map<Size, UInt> num_consfeat_of_size;
for (ConsensusMap::const_iterator cmit = cons.begin();
cmit != cons.end(); ++cmit)
{
++num_consfeat_of_size[cmit->size()];
}
os << endl << "Number of consensus features:" << endl;
for (map<Size, UInt>::reverse_iterator i = num_consfeat_of_size.rbegin(); i != num_consfeat_of_size.rend(); ++i)
{
os << " of size " << setw(2) << i->first << ": " << setw(6) << i->second << endl;
}
os << " total: " << setw(6) << cons.size() << endl << endl;
os << "Ranges:" << endl
<< " retention time: " << String::number(cons.getMin()[Peak2D::RT], 2) << " : " << String::number(cons.getMax()[Peak2D::RT], 2) << endl
<< " mass-to-charge: " << String::number(cons.getMin()[Peak2D::MZ], 2) << " : " << String::number(cons.getMax()[Peak2D::MZ], 2) << endl
<< " intensity: " << String::number(cons.getMinInt(), 2) << " : " << String::number(cons.getMaxInt(), 2) << endl;
// file descriptions
const ConsensusMap::FileDescriptions& descs = cons.getFileDescriptions();
if (!descs.empty())
{
os << endl <<
"File descriptions:" << endl;
for (ConsensusMap::FileDescriptions::const_iterator it = descs.begin(); it != descs.end(); ++it)
{
os << " - " << it->second.filename << endl
<< " identifier: " << it->first << endl
<< " label : " << it->second.label << endl
<< " size : " << it->second.size << endl;
}
}
}
os << endl
<< "-- Summary Statistics --" << endl
<< endl;
}
if (in_type == FileTypes::FEATUREXML) //features
{
feat.sortByRT();
vector<double> slice_stats;
Size n = getIntOption_("n");
Size begin = 0;
Size end = 0;
os << "#slice\tRT_begin\tRT_end\tnumber_of_features\ttic\t"
<< "int_mean\tint_stddev\tint_min\tint_max\tint_median\tint_lowerq\tint_upperq\t"
<< "mz_mean\tmz_stddev\tmz_min\tmz_max\tmz_median\tmz_lowerq\tmz_upperq\t"
<< "width_mean\twidth_stddev\twidth_min\twidth_max\twidth_median\twidth_lowerq\twidth_upperq\t"
<< "qual_mean\tqual_stddev\tqual_min\tqual_max\tqual_median\tqual_lowerq\tqual_upperq\t"
<< "rt_qual_mean\trt_qual_stddev\trt_qual_min\trt_qual_max\trt_qual_median\trt_qual_lowerq\trt_qual_upperq\t"
<< "mz_qual_mean\tmz_qual_stddev\tmz_qual_min\tmz_qual_max\tmz_qual_median\tmz_qual_lowerq\tmz_qual_upperq"
<< endl;
double rt_begin = 0.0;
for (Size slice = 0; slice < n; ++slice)
{
// Determine slice boundaries.
double rt_end = feat.back().getRT() / (double)n * (slice + 1);
for (end = begin; end < feat.size() && feat[end].getRT() < rt_end; ++end) {}
// Compute statistics on all features in this slice.
slice_stats = sliceStatistics(feat, begin, end);
// Write the beginning and end of the slices to the output as well as the slice index.
os << slice << "\t" << rt_begin << "\t" << rt_end << "\t" << end - begin << "\t";
// Write the statistics as a line of an csv file
copy(slice_stats.begin(), slice_stats.end(), ostream_iterator<double>(os, "\t"));
os << endl;
begin = end;
rt_begin = rt_end;
}
}
else if (in_type == FileTypes::CONSENSUSXML) //consensus features
{
Size size = cons.size();
vector<double> intensities;
intensities.reserve(size);
vector<double> qualities(size);
qualities.reserve(size);
vector<double> widths(size);
widths.reserve(size);
vector<double> rt_delta_by_elems;
vector<double> rt_aad_by_elems;
vector<double> rt_aad_by_cfs;
rt_aad_by_cfs.reserve(size);
vector<double> mz_delta_by_elems;
vector<double> mz_aad_by_elems;
vector<double> mz_aad_by_cfs;
mz_aad_by_cfs.reserve(size);
vector<double> it_delta_by_elems;
vector<double> it_aad_by_elems;
vector<double> it_aad_by_cfs;
it_aad_by_cfs.reserve(size);
for (ConsensusMap::const_iterator cm_iter = cons.begin();
cm_iter != cons.end(); ++cm_iter)
{
double rt_aad = 0;
double mz_aad = 0;
double it_aad = 0;
intensities.push_back(cm_iter->getIntensity());
qualities.push_back(cm_iter->getQuality());
widths.push_back(cm_iter->getWidth());
for (ConsensusFeature::HandleSetType::const_iterator hs_iter = cm_iter->begin();
hs_iter != cm_iter->end(); ++hs_iter)
{
double rt_diff = hs_iter->getRT() - cm_iter->getRT();
rt_delta_by_elems.push_back(rt_diff);
if (rt_diff < 0)
{
rt_diff = -rt_diff;
}
rt_aad_by_elems.push_back(rt_diff);
rt_aad += rt_diff;
double mz_diff = hs_iter->getMZ() - cm_iter->getMZ();
mz_delta_by_elems.push_back(mz_diff);
if (mz_diff < 0)
{
mz_diff = -mz_diff;
}
mz_aad_by_elems.push_back(mz_diff);
mz_aad += mz_diff;
double it_ratio = hs_iter->getIntensity() / (cm_iter->getIntensity() ? cm_iter->getIntensity() : 1.);
it_delta_by_elems.push_back(it_ratio);
if (it_ratio < 1.)
{
it_ratio = 1. / it_ratio;
}
it_aad_by_elems.push_back(it_ratio);
it_aad += it_ratio;
}
if (!cm_iter->empty())
{
rt_aad /= cm_iter->size();
mz_aad /= cm_iter->size();
it_aad /= cm_iter->size();
} // otherwise rt_aad etc. are 0 anyway
rt_aad_by_cfs.push_back(rt_aad);
mz_aad_by_cfs.push_back(mz_aad);
it_aad_by_cfs.push_back(it_aad);
}
OpenMS::SomeStatistics some_statistics;
os.precision(writtenDigits(ConsensusFeature::IntensityType()));
os << "Intensities of consensus features:" << endl << some_statistics(intensities) << endl;
os.precision(writtenDigits(ConsensusFeature::QualityType()));
os << "Qualities of consensus features:" << endl << some_statistics(qualities) << endl;
os.precision(writtenDigits(ConsensusFeature::CoordinateType()));
os << "Retention time differences ( element-center, weight 1 per element):" << endl << some_statistics(rt_delta_by_elems) << endl;
os << "Absolute retention time differences ( |element-center|, weight 1 per element):" << endl << some_statistics(rt_aad_by_elems) << endl;
os << "Average absolute differences of retention time within consensus features ( |element-center|, weight 1 per consensus features):" << endl << some_statistics(rt_aad_by_cfs) << endl;
os.precision(writtenDigits(ConsensusFeature::CoordinateType()));
os << "Mass-to-charge differences ( element-center, weight 1 per element):" << endl << some_statistics(mz_delta_by_elems) << endl;
os << "Absolute differences of mass-to-charge ( |element-center|, weight 1 per element):" << endl << some_statistics(mz_aad_by_elems) << endl;
os << "Average absolute differences of mass-to-charge within consensus features ( |element-center|, weight 1 per consensus features):" << endl << some_statistics(mz_aad_by_cfs) << endl;
os.precision(writtenDigits(ConsensusFeature::IntensityType()));
os << "Intensity ratios ( element/center, weight 1 per element):" << endl << some_statistics(it_delta_by_elems) << endl;
os << "Relative intensity error ( max{(element/center),(center/element)}, weight 1 per element):" << endl << some_statistics(it_aad_by_elems) << endl;
os << "Average relative intensity error within consensus features ( max{(element/center),(center/element)}, weight 1 per consensus features):" << endl << some_statistics(it_aad_by_cfs) << endl;
}
return EXECUTION_OK;
}
ExitCodes main_(int, const char**)
{
//-------------------------------------------------------------
// parameter handling
//-------------------------------------------------------------
// file list
StringList file_list = getStringList_("in");
// file type
FileHandler file_handler;
FileTypes::Type force_type;
if (getStringOption_("in_type").size() > 0)
{
force_type = FileTypes::nameToType(getStringOption_("in_type"));
}
else
{
force_type = file_handler.getType(file_list[0]);
}
// output file names and types
String out_file = getStringOption_("out");
bool annotate_file_origin = getFlag_("annotate_file_origin");
rt_gap_ = getDoubleOption_("rt_concat:gap");
vector<String> trafo_out = getStringList_("rt_concat:trafo_out");
if (trafo_out.empty())
{
// resize now so we don't have to worry about indexing out of bounds:
trafo_out.resize(file_list.size());
}
else if (trafo_out.size() != file_list.size())
{
writeLog_("Error: Number of transformation output files must equal the number of input files (parameters 'rt_concat:trafo_out'/'in')!");
return ILLEGAL_PARAMETERS;
}
//-------------------------------------------------------------
// calculations
//-------------------------------------------------------------
if (force_type == FileTypes::FEATUREXML)
{
FeatureMap out;
FeatureXMLFile fh;
for (Size i = 0; i < file_list.size(); ++i)
{
FeatureMap map;
fh.load(file_list[i], map);
if (annotate_file_origin)
{
for (FeatureMap::iterator it = map.begin(); it != map.end(); ++it)
{
it->setMetaValue("file_origin", DataValue(file_list[i]));
}
}
if (rt_gap_ > 0.0) // concatenate in RT
{
adjustRetentionTimes_(map, trafo_out[i], i == 0);
}
out += map;
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
// annotate output with data processing info
addDataProcessing_(out, getProcessingInfo_(DataProcessing::FORMAT_CONVERSION));
fh.store(out_file, out);
}
else if (force_type == FileTypes::CONSENSUSXML)
{
ConsensusMap out;
ConsensusXMLFile fh;
fh.load(file_list[0], out);
// skip first file
for (Size i = 1; i < file_list.size(); ++i)
{
ConsensusMap map;
fh.load(file_list[i], map);
if (annotate_file_origin)
{
for (ConsensusMap::iterator it = map.begin(); it != map.end(); ++it)
{
it->setMetaValue("file_origin", DataValue(file_list[i]));
}
}
if (rt_gap_ > 0.0) // concatenate in RT
{
adjustRetentionTimes_(map, trafo_out[i], i == 0);
}
out += map;
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
// annotate output with data processing info
addDataProcessing_(out, getProcessingInfo_(DataProcessing::FORMAT_CONVERSION));
fh.store(out_file, out);
}
else if (force_type == FileTypes::TRAML)
{
TargetedExperiment out;
TraMLFile fh;
for (Size i = 0; i < file_list.size(); ++i)
{
TargetedExperiment map;
fh.load(file_list[i], map);
out += map;
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
// annotate output with data processing info
Software software;
software.setName("FileMerger");
software.setVersion(VersionInfo::getVersion());
out.addSoftware(software);
fh.store(out_file, out);
}
else // raw data input (e.g. mzML)
{
// RT
bool rt_auto_number = getFlag_("raw:rt_auto");
bool rt_filename = getFlag_("raw:rt_filename");
bool rt_custom = false;
DoubleList custom_rts = getDoubleList_("raw:rt_custom");
if (!custom_rts.empty())
{
rt_custom = true;
if (custom_rts.size() != file_list.size())
{
writeLog_("Custom retention time list (parameter 'raw:rt_custom') must have as many elements as there are input files (parameter 'in')!");
return ILLEGAL_PARAMETERS;
}
}
// MS level
Int ms_level = getIntOption_("raw:ms_level");
MSExperiment<> out;
UInt rt_auto = 0;
UInt native_id = 0;
for (Size i = 0; i < file_list.size(); ++i)
{
String filename = file_list[i];
// load file
force_type = file_handler.getType(file_list[i]);
MSExperiment<> in;
file_handler.loadExperiment(filename, in, force_type, log_type_);
if (in.empty() && in.getChromatograms().empty())
{
writeLog_(String("Warning: Empty file '") + filename + "'!");
continue;
}
out.reserve(out.size() + in.size());
// warn if custom RT and more than one scan in input file
if (rt_custom && in.size() > 1)
{
writeLog_(String("Warning: More than one scan in file '") + filename + "'! All scans will have the same retention time!");
}
// handle special raw data options:
for (MSExperiment<>::iterator spec_it = in.begin();
spec_it != in.end(); ++spec_it)
{
float rt_final = spec_it->getRT();
if (rt_auto_number)
{
rt_final = ++rt_auto;
}
else if (rt_custom)
{
rt_final = custom_rts[i];
}
else if (rt_filename)
{
static const boost::regex re("rt(\\d+(\\.\\d+)?)");
boost::smatch match;
bool found = boost::regex_search(filename, match, re);
if (found)
{
rt_final = String(match[1]).toFloat();
}
else
{
writeLog_("Warning: could not extract retention time from filename '" + filename + "'");
}
}
// none of the rt methods were successful
if (rt_final < 0)
{
writeLog_(String("Warning: No valid retention time for output scan '") + rt_auto + "' from file '" + filename + "'");
}
spec_it->setRT(rt_final);
spec_it->setNativeID("spectrum=" + String(native_id));
if (ms_level > 0)
{
spec_it->setMSLevel(ms_level);
}
++native_id;
}
// if we have only one spectrum, we can annotate it directly, for more spectra, we just name the source file leaving the spectra unannotated (to avoid a long and redundant list of sourceFiles)
if (in.size() == 1)
{
in[0].setSourceFile(in.getSourceFiles()[0]);
in.getSourceFiles().clear(); // delete source file annotated from source file (it's in the spectrum anyways)
}
if (rt_gap_ > 0.0) // concatenate in RT
{
adjustRetentionTimes_(in, trafo_out[i], i == 0);
}
// add spectra to output
for (MSExperiment<>::const_iterator spec_it = in.begin();
spec_it != in.end(); ++spec_it)
{
out.addSpectrum(*spec_it);
}
// also add the chromatograms
for (vector<MSChromatogram<ChromatogramPeak> >::const_iterator
chrom_it = in.getChromatograms().begin(); chrom_it !=
in.getChromatograms().end(); ++chrom_it)
{
out.addChromatogram(*chrom_it);
}
// copy experimental settings from first file
if (i == 0)
{
out.ExperimentalSettings::operator=(in);
}
else // otherwise append
{
out.getSourceFiles().insert(out.getSourceFiles().end(), in.getSourceFiles().begin(), in.getSourceFiles().end()); // could be emtpty if spectrum was annotated above, but that's ok then
}
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
// annotate output with data processing info
addDataProcessing_(out, getProcessingInfo_(DataProcessing::FORMAT_CONVERSION));
MzMLFile f;
f.setLogType(log_type_);
f.store(out_file, out);
}
return EXECUTION_OK;
}
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);
}
ExitCodes main_(int, const char **)
{
String in = getStringOption_("in"), out = getStringOption_("out"),
id_out = getStringOption_("id_out");
if (out.empty() && id_out.empty())
{
throw Exception::RequiredParameterNotGiven(__FILE__, __LINE__,
__PRETTY_FUNCTION__,
"out/id_out");
}
vector<ProteinIdentification> proteins;
vector<PeptideIdentification> peptides;
FileTypes::Type in_type = FileHandler::getType(in);
if (in_type == FileTypes::MZML)
{
MSExperiment<> experiment;
MzMLFile().load(in, experiment);
// what about unassigned peptide IDs?
for (MSExperiment<>::Iterator exp_it = experiment.begin();
exp_it != experiment.end(); ++exp_it)
{
peptides.insert(peptides.end(),
exp_it->getPeptideIdentifications().begin(),
exp_it->getPeptideIdentifications().end());
exp_it->getPeptideIdentifications().clear();
}
experiment.getProteinIdentifications().swap(proteins);
if (!out.empty())
{
addDataProcessing_(experiment,
getProcessingInfo_(DataProcessing::FILTERING));
MzMLFile().store(out, experiment);
}
}
else if (in_type == FileTypes::FEATUREXML)
{
FeatureMap features;
FeatureXMLFile().load(in, features);
features.getUnassignedPeptideIdentifications().swap(peptides);
for (FeatureMap::Iterator feat_it = features.begin();
feat_it != features.end(); ++feat_it)
{
peptides.insert(peptides.end(),
feat_it->getPeptideIdentifications().begin(),
feat_it->getPeptideIdentifications().end());
feat_it->getPeptideIdentifications().clear();
}
features.getProteinIdentifications().swap(proteins);
if (!out.empty())
{
addDataProcessing_(features,
getProcessingInfo_(DataProcessing::FILTERING));
FeatureXMLFile().store(out, features);
}
}
else // consensusXML
{
ConsensusMap consensus;
ConsensusXMLFile().load(in, consensus);
consensus.getUnassignedPeptideIdentifications().swap(peptides);
for (ConsensusMap::Iterator cons_it = consensus.begin();
cons_it != consensus.end(); ++cons_it)
{
peptides.insert(peptides.end(),
cons_it->getPeptideIdentifications().begin(),
cons_it->getPeptideIdentifications().end());
cons_it->getPeptideIdentifications().clear();
}
consensus.getProteinIdentifications().swap(proteins);
if (!out.empty())
{
addDataProcessing_(consensus,
getProcessingInfo_(DataProcessing::FILTERING));
ConsensusXMLFile().store(out, consensus);
}
}
if (!id_out.empty())
{
// IDMapper can match a peptide ID to several overlapping features,
// resulting in duplicates; this shouldn't be the case for peak data
if (in_type != FileTypes::MZML) removeDuplicates_(peptides);
IdXMLFile().store(id_out, proteins, peptides);
}
return EXECUTION_OK;
}
ExitCodes main_(int, const char**)
{
vector<ProteinIdentification> prot_ids;
vector<PeptideIdentification> pep_ids;
ProteinHit temp_protein_hit;
//-------------------------------------------------------------
// parsing parameters
//-------------------------------------------------------------
String inputfile_id = getStringOption_("id");
String inputfile_feature = getStringOption_("feature");
String inputfile_consensus = getStringOption_("consensus");
String inputfile_raw = getStringOption_("in");
String outputfile_name = getStringOption_("out");
//~ bool Ms1(getFlag_("MS1"));
//~ bool Ms2(getFlag_("MS2"));
bool remove_duplicate_features(getFlag_("remove_duplicate_features"));
//-------------------------------------------------------------
// fetch vocabularies
//------------------------------------------------------------
ControlledVocabulary cv;
cv.loadFromOBO("PSI-MS", File::find("/CV/psi-ms.obo"));
cv.loadFromOBO("QC", File::find("/CV/qc-cv.obo"));
QcMLFile qcmlfile;
//-------------------------------------------------------------
// MS aqiusition
//------------------------------------------------------------
String base_name = QFileInfo(QString::fromStdString(inputfile_raw)).baseName();
cout << "Reading mzML file..." << endl;
MzMLFile mz_data_file;
MSExperiment<Peak1D> exp;
MzMLFile().load(inputfile_raw, exp);
//---prep input
exp.sortSpectra();
UInt min_mz = std::numeric_limits<UInt>::max();
UInt max_mz = 0;
std::map<Size, UInt> mslevelcounts;
qcmlfile.registerRun(base_name,base_name); //TODO use UIDs
//---base MS aquisition qp
String msaq_ref = base_name + "_msaq";
QcMLFile::QualityParameter qp;
qp.id = msaq_ref; ///< Identifier
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000004";
try
{
//~ const ControlledVocabulary::CVTerm& test = cv.getTermByName("MS aquisition result details");
//~ cout << test.name << test.id << endl;
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
//~ const ControlledVocabulary::CVTerm& term = cv.getTerm("0000004");
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "mzML file"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
//---file origin qp
qp = QcMLFile::QualityParameter();
qp.name = "mzML file"; ///< Name
qp.id = base_name + "_run_name"; ///< Identifier
qp.cvRef = "MS"; ///< cv reference
qp.cvAcc = "MS:1000577";
qp.value = base_name;
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.name = "instrument model"; ///< Name
qp.id = base_name + "_instrument_name"; ///< Identifier
qp.cvRef = "MS"; ///< cv reference
qp.cvAcc = "MS:1000031";
qp.value = exp.getInstrument().getName();
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.name = "completion time"; ///< Name
qp.id = base_name + "_date"; ///< Identifier
qp.cvRef = "MS"; ///< cv reference
qp.cvAcc = "MS:1000747";
qp.value = exp.getDateTime().getDate();
qcmlfile.addRunQualityParameter(base_name, qp);
//---precursors at
QcMLFile::Attachment at;
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000044";
at.qualityRef = msaq_ref;
at.id = base_name + "_precursors"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "precursors"; ///< Name
}
at.colTypes.push_back("MS:1000894_[sec]"); //RT
at.colTypes.push_back("MS:1000040"); //MZ
for (Size i = 0; i < exp.size(); ++i)
{
mslevelcounts[exp[i].getMSLevel()]++;
if (exp[i].getMSLevel() == 2)
{
if (exp[i].getPrecursors().front().getMZ() < min_mz)
{
min_mz = exp[i].getPrecursors().front().getMZ();
}
if (exp[i].getPrecursors().front().getMZ() > max_mz)
{
max_mz = exp[i].getPrecursors().front().getMZ();
}
std::vector<String> row;
row.push_back(exp[i].getRT());
row.push_back(exp[i].getPrecursors().front().getMZ());
at.tableRows.push_back(row);
}
}
qcmlfile.addRunAttachment(base_name, at);
//---aquisition results qp
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000006"; ///< cv accession for "aquisition results"
qp.id = base_name + "_ms1aquisition"; ///< Identifier
qp.value = String(mslevelcounts[1]);
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "number of ms1 spectra"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000007"; ///< cv accession for "aquisition results"
qp.id = base_name + "_ms2aquisition"; ///< Identifier
qp.value = String(mslevelcounts[2]);
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "number of ms2 spectra"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000008"; ///< cv accession for "aquisition results"
qp.id = base_name + "_Chromaquisition"; ///< Identifier
qp.value = String(exp.getChromatograms().size());
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "number of chromatograms"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000009";
at.qualityRef = msaq_ref;
at.id = base_name + "_mzrange"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "MS MZ aquisition ranges"; ///< Name
}
at.colTypes.push_back("QC:0000010"); //MZ
at.colTypes.push_back("QC:0000011"); //MZ
std::vector<String> rowmz;
rowmz.push_back(String(min_mz));
rowmz.push_back(String(max_mz));
at.tableRows.push_back(rowmz);
qcmlfile.addRunAttachment(base_name, at);
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000012";
at.qualityRef = msaq_ref;
at.id = base_name + "_rtrange"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "MS RT aquisition ranges"; ///< Name
}
at.colTypes.push_back("QC:0000013"); //MZ
at.colTypes.push_back("QC:0000014"); //MZ
std::vector<String> rowrt;
rowrt.push_back(String(exp.begin()->getRT()));
rowrt.push_back(String(exp.getSpectra().back().getRT()));
at.tableRows.push_back(rowrt);
qcmlfile.addRunAttachment(base_name, at);
//---ion current stability ( & tic ) qp
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000022";
at.qualityRef = msaq_ref;
at.id = base_name + "_tics"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "MS TICs"; ///< Name
}
at.colTypes.push_back("MS:1000894_[sec]");
at.colTypes.push_back("MS:1000285");
UInt max = 0;
Size below_10k = 0;
for (Size i = 0; i < exp.size(); ++i)
{
if (exp[i].getMSLevel() == 1)
{
UInt sum = 0;
for (Size j = 0; j < exp[i].size(); ++j)
{
sum += exp[i][j].getIntensity();
}
if (sum > max)
{
max = sum;
}
if (sum < 10000)
{
++below_10k;
}
std::vector<String> row;
row.push_back(exp[i].getRT());
row.push_back(sum);
at.tableRows.push_back(row);
}
}
qcmlfile.addRunAttachment(base_name, at);
qp = QcMLFile::QualityParameter();
qp.id = base_name + "_ticslump"; ///< Identifier
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000023";
qp.value = String((100 / exp.size()) * below_10k);
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "percentage of tic slumps"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
//-------------------------------------------------------------
// MS id
//------------------------------------------------------------
if (inputfile_id != "")
{
IdXMLFile().load(inputfile_id, prot_ids, pep_ids);
cerr << "idXML read ended. Found " << pep_ids.size() << " peptide identifications." << endl;
ProteinIdentification::SearchParameters params = prot_ids[0].getSearchParameters();
vector<String> var_mods = params.variable_modifications;
//~ boost::regex re("(?<=[KR])(?=[^P])");
String msid_ref = base_name + "_msid";
QcMLFile::QualityParameter qp;
qp.id = msid_ref; ///< Identifier
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000025";
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "MS identification result details"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000026";
at.qualityRef = msid_ref;
at.id = base_name + "_idsetting"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "MS id settings"; ///< Name
}
at.colTypes.push_back("MS:1001013"); //MS:1001013 db name MS:1001016 version MS:1001020 taxonomy
at.colTypes.push_back("MS:1001016");
at.colTypes.push_back("MS:1001020");
std::vector<String> row;
row.push_back(String(prot_ids.front().getSearchParameters().db));
row.push_back(String(prot_ids.front().getSearchParameters().db_version));
row.push_back(String(prot_ids.front().getSearchParameters().taxonomy));
at.tableRows.push_back(row);
qcmlfile.addRunAttachment(base_name, at);
UInt spectrum_count = 0;
Size peptide_hit_count = 0;
UInt runs_count = 0;
Size protein_hit_count = 0;
set<String> peptides;
set<String> proteins;
Size missedcleavages = 0;
for (Size i = 0; i < pep_ids.size(); ++i)
{
if (!pep_ids[i].empty())
{
++spectrum_count;
peptide_hit_count += pep_ids[i].getHits().size();
const vector<PeptideHit>& temp_hits = pep_ids[i].getHits();
for (Size j = 0; j < temp_hits.size(); ++j)
{
peptides.insert(temp_hits[j].getSequence().toString());
}
}
}
for (set<String>::iterator it = peptides.begin(); it != peptides.end(); ++it)
{
for (String::const_iterator st = it->begin(); st != it->end() - 1; ++st)
{
if (*st == 'K' || *st == 'R')
{
++missedcleavages;
}
}
}
for (Size i = 0; i < prot_ids.size(); ++i)
{
++runs_count;
protein_hit_count += prot_ids[i].getHits().size();
const vector<ProteinHit>& temp_hits = prot_ids[i].getHits();
for (Size j = 0; j < temp_hits.size(); ++j)
{
proteins.insert(temp_hits[j].getAccession());
}
}
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000037"; ///< cv accession
qp.id = base_name + "_misscleave"; ///< Identifier
qp.value = missedcleavages;
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "total number of missed cleavages"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000032"; ///< cv accession
qp.id = base_name + "_totprot"; ///< Identifier
qp.value = protein_hit_count;
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "total number of identified proteins"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000033"; ///< cv accession
qp.id = base_name + "_totuniqprot"; ///< Identifier
qp.value = String(proteins.size());
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "total number of uniquely identified proteins"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000029"; ///< cv accession
qp.id = base_name + "_psms"; ///< Identifier
qp.value = String(spectrum_count);
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "total number of PSM"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000030"; ///< cv accession
qp.id = base_name + "_totpeps"; ///< Identifier
qp.value = String(peptide_hit_count);
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "total number of identified peptides"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000031"; ///< cv accession
qp.id = base_name + "_totuniqpeps"; ///< Identifier
qp.value = String(peptides.size());
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "total number of uniquely identified peptides"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000038";
at.qualityRef = msid_ref;
at.id = base_name + "_massacc"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "delta ppm tables";
}
//~ delta ppm QC:0000039 RT MZ uniqueness ProteinID MS:1000885 target/decoy Score PeptideSequence MS:1000889 Annots string Similarity Charge UO:0000219 TheoreticalWeight UO:0000221 Oxidation_(M)
at.colTypes.push_back("RT");
at.colTypes.push_back("MZ");
at.colTypes.push_back("Score");
at.colTypes.push_back("PeptideSequence");
at.colTypes.push_back("Charge");
at.colTypes.push_back("TheoreticalWeight");
at.colTypes.push_back("delta_ppm");
for (UInt w = 0; w < var_mods.size(); ++w)
{
at.colTypes.push_back(String(var_mods[w]).substitute(' ', '_'));
}
std::vector<double> deltas;
//~ prot_ids[0].getSearchParameters();
for (vector<PeptideIdentification>::iterator it = pep_ids.begin(); it != pep_ids.end(); ++it)
{
if (it->getHits().size() > 0)
{
std::vector<String> row;
row.push_back(it->getRT());
row.push_back(it->getMZ());
PeptideHit tmp = it->getHits().front(); //TODO depends on score & sort
vector<UInt> pep_mods;
for (UInt w = 0; w < var_mods.size(); ++w)
{
pep_mods.push_back(0);
}
for (AASequence::ConstIterator z = tmp.getSequence().begin(); z != tmp.getSequence().end(); ++z)
{
Residue res = *z;
String temp;
if (res.getModification().size() > 0 && res.getModification() != "Carbamidomethyl")
{
temp = res.getModification() + " (" + res.getOneLetterCode() + ")";
//cout<<res.getModification()<<endl;
for (UInt w = 0; w < var_mods.size(); ++w)
{
if (temp == var_mods[w])
{
//cout<<temp;
pep_mods[w] += 1;
}
}
}
}
row.push_back(tmp.getScore());
row.push_back(tmp.getSequence().toString().removeWhitespaces());
row.push_back(tmp.getCharge());
row.push_back(String((tmp.getSequence().getMonoWeight() + tmp.getCharge() * Constants::PROTON_MASS_U) / tmp.getCharge()));
double dppm = /* std::abs */ (getMassDifference(((tmp.getSequence().getMonoWeight() + tmp.getCharge() * Constants::PROTON_MASS_U) / tmp.getCharge()), it->getMZ(), true));
row.push_back(String(dppm));
deltas.push_back(dppm);
for (UInt w = 0; w < var_mods.size(); ++w)
{
row.push_back(pep_mods[w]);
}
at.tableRows.push_back(row);
}
}
qcmlfile.addRunAttachment(base_name, at);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000040"; ///< cv accession
qp.id = base_name + "_mean_delta"; ///< Identifier
qp.value = String(OpenMS::Math::mean(deltas.begin(), deltas.end()));
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "mean delta ppm"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000041"; ///< cv accession
qp.id = base_name + "_median_delta"; ///< Identifier
qp.value = String(OpenMS::Math::median(deltas.begin(), deltas.end(), false));
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "median delta ppm"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000035"; ///< cv accession
qp.id = base_name + "_ratio_id"; ///< Identifier
qp.value = String(double(pep_ids.size()) / double(mslevelcounts[2]));
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "id ratio"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
}
//-------------------------------------------------------------
// MS quantitation
//------------------------------------------------------------
FeatureMap map;
String msqu_ref = base_name + "_msqu";
if (inputfile_feature != "")
{
FeatureXMLFile f;
f.load(inputfile_feature, map);
cout << "Read featureXML file..." << endl;
//~ UInt fiter = 0;
map.sortByRT();
map.updateRanges();
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000045"; ///< cv accession
qp.id = msqu_ref; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "MS quantification result details"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
qp = QcMLFile::QualityParameter();
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:0000046"; ///< cv accession
qp.id = base_name + "_feature_count"; ///< Identifier
qp.value = String(map.size());
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(qp.cvAcc);
qp.name = term.name; ///< Name
}
catch (...)
{
qp.name = "number of features"; ///< Name
}
qcmlfile.addRunQualityParameter(base_name, qp);
}
if (inputfile_feature != "" && !remove_duplicate_features)
{
QcMLFile::Attachment at;
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000047";
at.qualityRef = msqu_ref;
at.id = base_name + "_features"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "features"; ///< Name
}
at.colTypes.push_back("MZ");
at.colTypes.push_back("RT");
at.colTypes.push_back("Intensity");
at.colTypes.push_back("Charge");
at.colTypes.push_back("Quality");
at.colTypes.push_back("FWHM");
at.colTypes.push_back("IDs");
UInt fiter = 0;
map.sortByRT();
//ofstream out(outputfile_name.c_str());
while (fiter < map.size())
{
std::vector<String> row;
row.push_back(map[fiter].getMZ());
row.push_back(map[fiter].getRT());
row.push_back(map[fiter].getIntensity());
row.push_back(map[fiter].getCharge());
row.push_back(map[fiter].getOverallQuality());
row.push_back(map[fiter].getWidth());
row.push_back(map[fiter].getPeptideIdentifications().size());
fiter++;
at.tableRows.push_back(row);
}
qcmlfile.addRunAttachment(base_name, at);
}
else if (inputfile_feature != "" && remove_duplicate_features)
{
QcMLFile::Attachment at;
at = QcMLFile::Attachment();
at.cvRef = "QC"; ///< cv reference
at.cvAcc = "QC:0000047";
at.qualityRef = msqu_ref;
at.id = base_name + "_features"; ///< Identifier
try
{
const ControlledVocabulary::CVTerm& term = cv.getTerm(at.cvAcc);
at.name = term.name; ///< Name
}
catch (...)
{
at.name = "features"; ///< Name
}
at.colTypes.push_back("MZ");
at.colTypes.push_back("RT");
at.colTypes.push_back("Intensity");
at.colTypes.push_back("Charge");
FeatureMap map, map_out;
FeatureXMLFile f;
f.load(inputfile_feature, map);
UInt fiter = 0;
map.sortByRT();
while (fiter < map.size())
{
FeatureMap map_tmp;
for (UInt k = fiter; k <= map.size(); ++k)
{
if (abs(map[fiter].getRT() - map[k].getRT()) < 0.1)
{
//~ cout << fiter << endl;
map_tmp.push_back(map[k]);
}
else
{
fiter = k;
break;
}
}
map_tmp.sortByMZ();
UInt retif = 1;
map_out.push_back(map_tmp[0]);
while (retif < map_tmp.size())
{
if (abs(map_tmp[retif].getMZ() - map_tmp[retif - 1].getMZ()) > 0.01)
{
cout << "equal RT, but mass different" << endl;
map_out.push_back(map_tmp[retif]);
}
retif++;
}
}
qcmlfile.addRunAttachment(base_name, at);
}
if (inputfile_consensus != "")
{
cout << "Reading consensusXML file..." << endl;
ConsensusXMLFile f;
ConsensusMap map;
f.load(inputfile_consensus, map);
//~ String CONSENSUS_NAME = "_consensus.tsv";
//~ String combined_out = outputfile_name + CONSENSUS_NAME;
//~ ofstream out(combined_out.c_str());
at = QcMLFile::Attachment();
qp.name = "consensuspoints"; ///< Name
//~ qp.id = base_name + "_consensuses"; ///< Identifier
qp.cvRef = "QC"; ///< cv reference
qp.cvAcc = "QC:xxxxxxxx"; ///< cv accession "featuremapper results"
at.colTypes.push_back("Native_spectrum_ID");
at.colTypes.push_back("DECON_RT_(sec)");
at.colTypes.push_back("DECON_MZ_(Th)");
at.colTypes.push_back("DECON_Intensity");
at.colTypes.push_back("Feature_RT_(sec)");
at.colTypes.push_back("Feature_MZ_(Th)");
at.colTypes.push_back("Feature_Intensity");
at.colTypes.push_back("Feature_Charge");
for (ConsensusMap::const_iterator cmit = map.begin(); cmit != map.end(); ++cmit)
{
const ConsensusFeature& CF = *cmit;
for (ConsensusFeature::const_iterator cfit = CF.begin(); cfit != CF.end(); ++cfit)
{
std::vector<String> row;
FeatureHandle FH = *cfit;
row.push_back(CF.getMetaValue("spectrum_native_id"));
row.push_back(CF.getRT()); row.push_back(CF.getMZ());
row.push_back(CF.getIntensity());
row.push_back(FH.getRT());
row.push_back(FH.getMZ());
row.push_back(FH.getCharge());
at.tableRows.push_back(row);
}
}
qcmlfile.addRunAttachment(base_name, at);
}
//-------------------------------------------------------------
// finalize
//------------------------------------------------------------
qcmlfile.store(outputfile_name);
return EXECUTION_OK;
}
void LabeledPairFinder::run(const vector<ConsensusMap>& input_maps, ConsensusMap& result_map)
{
if (input_maps.size() != 1)
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "exactly one input map required");
if (result_map.getFileDescriptions().size() != 2)
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "two file descriptions required");
if (result_map.getFileDescriptions().begin()->second.filename != result_map.getFileDescriptions().rbegin()->second.filename)
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "the two file descriptions have to contain the same file name");
checkIds_(input_maps);
//look up the light and heavy index
Size light_index = numeric_limits<Size>::max();
Size heavy_index = numeric_limits<Size>::max();
for (ConsensusMap::FileDescriptions::const_iterator it = result_map.getFileDescriptions().begin();
it != result_map.getFileDescriptions().end();
++it)
{
if (it->second.label == "heavy")
{
heavy_index = it->first;
}
else if (it->second.label == "light")
{
light_index = it->first;
}
}
if (light_index == numeric_limits<Size>::max() || heavy_index == numeric_limits<Size>::max())
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "the input maps have to be labeled 'light' and 'heavy'");
}
result_map.clear(false);
// sort consensus features by RT (and MZ) to speed up searching afterwards
typedef ConstRefVector<ConsensusMap> RefMap;
RefMap model_ref(input_maps[0].begin(), input_maps[0].end());
model_ref.sortByPosition();
//calculate matches
ConsensusMap matches;
//settings
double rt_pair_dist = param_.getValue("rt_pair_dist");
double rt_dev_low = param_.getValue("rt_dev_low");
double rt_dev_high = param_.getValue("rt_dev_high");
double mz_dev = param_.getValue("mz_dev");
DoubleList mz_pair_dists = param_.getValue("mz_pair_dists");
bool mrm = param_.getValue("mrm").toBool();
//estimate RT parameters
if (param_.getValue("rt_estimate") == "true")
{
//find all possible RT distances of features with the same charge and a good m/z distance
vector<double> dists;
dists.reserve(model_ref.size());
for (RefMap::const_iterator it = model_ref.begin(); it != model_ref.end(); ++it)
{
for (RefMap::const_iterator it2 = model_ref.begin(); it2 != model_ref.end(); ++it2)
{
for (DoubleList::const_iterator dist_it = mz_pair_dists.begin(); dist_it != mz_pair_dists.end(); ++dist_it)
{
double mz_pair_dist = *dist_it;
if (it2->getCharge() == it->getCharge()
&& it2->getMZ() >= it->getMZ() + mz_pair_dist / it->getCharge() - mz_dev
&& it2->getMZ() <= it->getMZ() + mz_pair_dist / it->getCharge() + mz_dev)
{
dists.push_back(it2->getRT() - it->getRT());
}
}
}
}
if (dists.empty())
{
cout << "Warning: Could not find pairs for RT distance estimation. The manual settings are used!" << endl;
}
else
{
if (dists.size() < 50)
{
cout << "Warning: Found only " << dists.size() << " pairs. The estimated shift and std deviation are probably not reliable!" << endl;
}
//--------------------------- estimate initial parameters of fit ---------------------------
GaussFitter::GaussFitResult result(-1, -1, -1);
//first estimate of the optimal shift: median of the distances
sort(dists.begin(), dists.end());
Size median_index = dists.size() / 2;
result.x0 = dists[median_index];
//create histogram of distances
//consider only the maximum of pairs, centered around the optimal shift
Size max_pairs = model_ref.size() / 2;
Size start_index = (Size) max((SignedSize)0, (SignedSize)(median_index - max_pairs / 2));
Size end_index = (Size) min((SignedSize)(dists.size() - 1), (SignedSize)(median_index + max_pairs / 2));
double start_value = dists[start_index];
double end_value = dists[end_index];
double bin_step = fabs(end_value - start_value) / 99.999; //ensure that we have 100 bins
Math::Histogram<> hist(start_value, end_value, bin_step);
//std::cout << "HIST from " << start_value << " to " << end_value << " (bin size " << bin_step << ")" << endl;
for (Size i = start_index; i <= end_index; ++i)
{
hist.inc(dists[i]);
}
//cout << hist << endl;
dists.clear();
//determine median of bins (uniform background distribution)
vector<Size> bins(hist.begin(), hist.end());
sort(bins.begin(), bins.end());
Size bin_median = bins[bins.size() / 2];
bins.clear();
//estimate scale A: maximum of the histogram
Size max_value = hist.maxValue();
result.A = max_value - bin_median;
//overwrite estimate of x0 with the position of the highest bin
for (Size i = 0; i < hist.size(); ++i)
{
if (hist[i] == max_value)
{
result.x0 = hist.centerOfBin(i);
break;
}
}
//estimate sigma: first time the count is less or equal the median count in the histogram
double pos = result.x0;
while (pos > start_value && hist.binValue(pos) > bin_median)
{
pos -= bin_step;
}
double sigma_low = result.x0 - pos;
pos = result.x0;
while (pos<end_value&& hist.binValue(pos)> bin_median)
{
pos += bin_step;
}
double sigma_high = pos - result.x0;
result.sigma = (sigma_high + sigma_low) / 6.0;
//cout << "estimated optimal RT distance (before fit): " << result.x0 << endl;
//cout << "estimated allowed deviation (before fit): " << result.sigma*3.0 << endl;
//--------------------------- do gauss fit ---------------------------
vector<DPosition<2> > points(hist.size());
for (Size i = 0; i < hist.size(); ++i)
{
points[i][0] = hist.centerOfBin(i);
points[i][1] = max(0u, hist[i]);
}
GaussFitter fitter;
fitter.setInitialParameters(result);
result = fitter.fit(points);
cout << "estimated optimal RT distance: " << result.x0 << endl;
cout << "estimated allowed deviation: " << fabs(result.sigma) * 3.0 << endl;
rt_pair_dist = result.x0;
rt_dev_low = fabs(result.sigma) * 3.0;
rt_dev_high = fabs(result.sigma) * 3.0;
}
}
// check each feature
for (RefMap::const_iterator it = model_ref.begin(); it != model_ref.end(); ++it)
{
for (DoubleList::const_iterator dist_it = mz_pair_dists.begin(); dist_it != mz_pair_dists.end(); ++dist_it)
{
double mz_pair_dist = *dist_it;
RefMap::const_iterator it2 = lower_bound(model_ref.begin(), model_ref.end(), it->getRT() + rt_pair_dist - rt_dev_low, ConsensusFeature::RTLess());
while (it2 != model_ref.end() && it2->getRT() <= it->getRT() + rt_pair_dist + rt_dev_high)
{
// if in mrm mode, we need to compare precursor mass difference and fragment mass difference, charge remains the same
double prec_mz_diff(0);
if (mrm)
{
prec_mz_diff = fabs((double)it2->getMetaValue("MZ") - (double)it->getMetaValue("MZ"));
if (it->getCharge() != 0)
{
prec_mz_diff = fabs(prec_mz_diff - mz_pair_dist / it->getCharge());
}
else
{
prec_mz_diff = fabs(prec_mz_diff - mz_pair_dist);
}
}
bool mrm_correct_dist(false);
double frag_mz_diff = fabs(it->getMZ() - it2->getMZ());
//cerr << it->getRT() << " charge1=" << it->getCharge() << ", charge2=" << it2->getCharge() << ", prec_diff=" << prec_mz_diff << ", frag_diff=" << frag_mz_diff << endl;
if (mrm &&
it2->getCharge() == it->getCharge() &&
prec_mz_diff < mz_dev &&
(frag_mz_diff < mz_dev || fabs(frag_mz_diff - mz_pair_dist) < mz_dev))
{
mrm_correct_dist = true;
//cerr << "mrm_correct_dist" << endl;
}
if ((mrm && mrm_correct_dist) || (!mrm &&
it2->getCharge() == it->getCharge() &&
it2->getMZ() >= it->getMZ() + mz_pair_dist / it->getCharge() - mz_dev &&
it2->getMZ() <= it->getMZ() + mz_pair_dist / it->getCharge() + mz_dev
))
{
//cerr << "dist correct" << endl;
double score = sqrt(
PValue_(it2->getMZ() - it->getMZ(), mz_pair_dist / it->getCharge(), mz_dev, mz_dev) *
PValue_(it2->getRT() - it->getRT(), rt_pair_dist, rt_dev_low, rt_dev_high)
);
// Note: we used to copy the id from the light feature here, but that strategy does not generalize to more than two labels.
// We might want to report consensus features where the light one is missing but more than one heavier variant was found.
// Also, the old strategy is inconsistent with what was done in the unlabeled case. Thus now we assign a new unique id here.
matches.push_back(ConsensusFeature());
matches.back().setUniqueId();
matches.back().insert(light_index, *it);
matches.back().clearMetaInfo();
matches.back().insert(heavy_index, *it2);
matches.back().setQuality(score);
matches.back().setCharge(it->getCharge());
matches.back().computeMonoisotopicConsensus();
}
++it2;
}
}
}
//compute best pairs
// - sort matches by quality
// - take highest-quality matches first (greedy) and mark them as used
set<Size> used_features;
matches.sortByQuality(true);
for (ConsensusMap::const_iterator match = matches.begin(); match != matches.end(); ++match)
{
//check if features are not used yet
if (used_features.find(match->begin()->getUniqueId()) == used_features.end() &&
used_features.find(match->rbegin()->getUniqueId()) == used_features.end()
)
{
//if unused, add it to the final set of elements
result_map.push_back(*match);
used_features.insert(match->begin()->getUniqueId());
used_features.insert(match->rbegin()->getUniqueId());
}
}
//Add protein identifications to result map
for (Size i = 0; i < input_maps.size(); ++i)
{
result_map.getProteinIdentifications().insert(result_map.getProteinIdentifications().end(), input_maps[i].getProteinIdentifications().begin(), input_maps[i].getProteinIdentifications().end());
}
//Add unassigned peptide identifications to result map
for (Size i = 0; i < input_maps.size(); ++i)
{
result_map.getUnassignedPeptideIdentifications().insert(result_map.getUnassignedPeptideIdentifications().end(), input_maps[i].getUnassignedPeptideIdentifications().begin(), input_maps[i].getUnassignedPeptideIdentifications().end());
}
// Very useful for checking the results, and the ids have no real meaning anyway
result_map.sortByMZ();
}
ExitCodes main_(int, const char**)
{
//-------------------------------------------------------------
// parameter handling
//-------------------------------------------------------------
//file list
StringList file_list = getStringList_("in");
//file type
FileHandler fh;
FileTypes::Type force_type;
if (getStringOption_("in_type").size() > 0)
{
force_type = FileTypes::nameToType(getStringOption_("in_type"));
}
else
{
force_type = fh.getType(file_list[0]);
}
//output file names and types
String out_file = getStringOption_("out");
//-------------------------------------------------------------
// calculations
//-------------------------------------------------------------
bool annotate_file_origin = getFlag_("annotate_file_origin");
if (force_type == FileTypes::FEATUREXML)
{
FeatureMap<> out;
for (Size i = 0; i < file_list.size(); ++i)
{
FeatureMap<> map;
FeatureXMLFile fh;
fh.load(file_list[i], map);
if (annotate_file_origin)
{
for (FeatureMap<>::iterator it = map.begin(); it != map.end(); ++it)
{
it->setMetaValue("file_origin", DataValue(file_list[i]));
}
}
out += map;
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
//annotate output with data processing info
addDataProcessing_(out, getProcessingInfo_(DataProcessing::FORMAT_CONVERSION));
FeatureXMLFile f;
f.store(out_file, out);
}
else if (force_type == FileTypes::CONSENSUSXML)
{
ConsensusMap out;
ConsensusXMLFile fh;
fh.load(file_list[0], out);
//skip first file
for (Size i = 1; i < file_list.size(); ++i)
{
ConsensusMap map;
ConsensusXMLFile fh;
fh.load(file_list[i], map);
if (annotate_file_origin)
{
for (ConsensusMap::iterator it = map.begin(); it != map.end(); ++it)
{
it->setMetaValue("file_origin", DataValue(file_list[i]));
}
}
out += map;
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
//annotate output with data processing info
addDataProcessing_(out, getProcessingInfo_(DataProcessing::FORMAT_CONVERSION));
ConsensusXMLFile f;
f.store(out_file, out);
}
else if (force_type == FileTypes::TRAML)
{
TargetedExperiment out;
for (Size i = 0; i < file_list.size(); ++i)
{
TargetedExperiment map;
TraMLFile fh;
fh.load(file_list[i], map);
out += map;
}
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
//annotate output with data processing info
Software software;
software.setName("FileMerger");
software.setVersion(VersionInfo::getVersion());
out.addSoftware(software);
TraMLFile f;
f.store(out_file, out);
}
else
{
// we might want to combine different types, thus we only
// query in_type (which applies to all files)
// and not the suffix or content of a single file
force_type = FileTypes::nameToType(getStringOption_("in_type"));
//rt
bool rt_auto_number = getFlag_("raw:rt_auto");
bool rt_filename = getFlag_("raw:rt_filename");
bool rt_custom = false;
DoubleList custom_rts = getDoubleList_("raw:rt_custom");
if (custom_rts.size() != 0)
{
rt_custom = true;
if (custom_rts.size() != file_list.size())
{
writeLog_("Custom retention time list must have as many elements as there are input files!");
printUsage_();
return ILLEGAL_PARAMETERS;
}
}
//ms level
bool user_ms_level = getFlag_("raw:user_ms_level");
MSExperiment<> out;
out.reserve(file_list.size());
UInt rt_auto = 0;
UInt native_id = 0;
std::vector<MSChromatogram<ChromatogramPeak> > all_chromatograms;
for (Size i = 0; i < file_list.size(); ++i)
{
String filename = file_list[i];
//load file
MSExperiment<> in;
fh.loadExperiment(filename, in, force_type, log_type_);
if (in.empty() && in.getChromatograms().empty())
{
writeLog_(String("Warning: Empty file '") + filename + "'!");
continue;
}
out.reserve(out.size() + in.size());
//warn if custom RT and more than one scan in input file
if (rt_custom && in.size() > 1)
{
writeLog_(String("Warning: More than one scan in file '") + filename + "'! All scans will have the same retention time!");
}
for (MSExperiment<>::const_iterator it2 = in.begin(); it2 != in.end(); ++it2)
{
//handle rt
Real rt_final = it2->getRT();
if (rt_auto_number)
{
rt_final = ++rt_auto;
}
else if (rt_custom)
{
rt_final = custom_rts[i];
}
else if (rt_filename)
{
if (!filename.hasSubstring("rt"))
{
writeLog_(String("Warning: cannot guess retention time from filename as it does not contain 'rt'"));
}
for (Size i = 0; i < filename.size(); ++i)
{
if (filename[i] == 'r' && ++i != filename.size() && filename[i] == 't' && ++i != filename.size() && isdigit(filename[i]))
{
String rt;
while (i != filename.size() && (filename[i] == '.' || isdigit(filename[i])))
{
rt += filename[i++];
}
if (rt.size() > 0)
{
// remove dot from rt3892.98.dta
// ^
if (rt[rt.size() - 1] == '.')
{
// remove last character
rt.erase(rt.end() - 1);
}
}
try
{
float tmp = rt.toFloat();
rt_final = tmp;
}
catch (Exception::ConversionError)
{
writeLog_(String("Warning: cannot convert the found retention time in a value '" + rt + "'."));
}
}
}
}
// none of the rt methods were successful
if (rt_final == -1)
{
writeLog_(String("Warning: No valid retention time for output scan '") + rt_auto + "' from file '" + filename + "'");
}
out.addSpectrum(*it2);
out.getSpectra().back().setRT(rt_final);
out.getSpectra().back().setNativeID(native_id);
if (user_ms_level)
{
out.getSpectra().back().setMSLevel((int)getIntOption_("raw:ms_level"));
}
++native_id;
}
// if we had only one spectrum, we can annotate it directly, for more spectra, we just name the source file leaving the spectra unannotated (to avoid a long and redundant list of sourceFiles)
if (in.size() == 1)
{
out.getSpectra().back().setSourceFile(in.getSourceFiles()[0]);
in.getSourceFiles().clear(); // delete source file annotated from source file (its in the spectrum anyways)
}
// copy experimental settings from first file
if (i == 0)
{
out.ExperimentalSettings::operator=(in);
}
else // otherwise append
{
out.getSourceFiles().insert(out.getSourceFiles().end(), in.getSourceFiles().begin(), in.getSourceFiles().end()); // could be emtpty if spectrum was annotated above, but that's ok then
}
// also add the chromatograms
for (std::vector<MSChromatogram<ChromatogramPeak> >::const_iterator it2 = in.getChromatograms().begin(); it2 != in.getChromatograms().end(); ++it2)
{
all_chromatograms.push_back(*it2);
}
}
// set the chromatograms
out.setChromatograms(all_chromatograms);
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
//annotate output with data processing info
addDataProcessing_(out, getProcessingInfo_(DataProcessing::FORMAT_CONVERSION));
MzMLFile f;
f.setLogType(log_type_);
f.store(out_file, out);
}
return EXECUTION_OK;
}
void PoseClusteringShiftSuperimposer::run(const ConsensusMap & map_model, const ConsensusMap & map_scene, TransformationDescription & transformation)
{
typedef ConstRefVector<ConsensusMap> PeakPointerArray_;
typedef Math::LinearInterpolation<double, double> LinearInterpolationType_;
LinearInterpolationType_ shift_hash_;
// OLD STUFF
// LinearInterpolationType_ scaling_hash_1;
// LinearInterpolationType_ scaling_hash_2;
// LinearInterpolationType_ shift_hash_;
// LinearInterpolationType_ rt_high_hash_;
/// Maximum deviation in mz of two partner points
const double mz_pair_max_distance = param_.getValue("mz_pair_max_distance");
/// Size of each shift bucket
const double shift_bucket_size = param_.getValue("shift_bucket_size");
const UInt struc_elem_length_datapoints = 21; // MAGIC ALERT: number of data points in structuring element for tophat filter, which removes baseline from histogram
const double scaling_histogram_crossing_slope = 3.0; // MAGIC ALERT: used when distinguishing noise level and enriched histogram bins
const double scaling_cutoff_stdev_multiplier = 1.5; // MAGIC ALERT: multiplier for stdev in cutoff for outliers
const UInt loops_mean_stdev_cutoff = 3; // MAGIC ALERT: number of loops in stdev cutoff for outliers
startProgress(0, 100, "shift pose clustering");
UInt actual_progress = 0;
setProgress(++actual_progress);
// Optionally, we will write dumps of the hash table buckets.
bool do_dump_buckets = false;
String dump_buckets_basename;
if (param_.getValue("dump_buckets") != "")
{
do_dump_buckets = true;
dump_buckets_basename = param_.getValue("dump_buckets");
}
setProgress(++actual_progress);
// Even more optionally, we will write dumps of the hashed pairs.
bool do_dump_pairs = false;
String dump_pairs_basename;
if (param_.getValue("dump_pairs") != "")
{
do_dump_pairs = true;
dump_pairs_basename = param_.getValue("dump_pairs");
}
setProgress(++actual_progress);
//**************************************************************************
// Select the most abundant data points only. After that, disallow modifications
// (we tend to have annoying issues with const_iterator versus iterator).
PeakPointerArray_ model_map_ini(map_model.begin(), map_model.end());
const PeakPointerArray_ & model_map(model_map_ini);
PeakPointerArray_ scene_map_ini(map_scene.begin(), map_scene.end());
const PeakPointerArray_ & scene_map(scene_map_ini);
{
// truncate the data as necessary
// casting to SignedSize is done on PURPOSE here! (num_used_points will be maximal if -1 is used)
const Size num_used_points = (SignedSize) param_.getValue("num_used_points");
if (model_map_ini.size() > num_used_points)
{
model_map_ini.sortByIntensity(true);
model_map_ini.resize(num_used_points);
}
model_map_ini.sortByComparator(Peak2D::MZLess());
setProgress(++actual_progress);
if (scene_map_ini.size() > num_used_points)
{
scene_map_ini.sortByIntensity(true);
scene_map_ini.resize(num_used_points);
}
scene_map_ini.sortByComparator(Peak2D::MZLess());
setProgress(++actual_progress);
// Note: model_map_ini and scene_map_ini will not be used further below
}
setProgress((actual_progress = 10));
//**************************************************************************
// Preprocessing
// get RT ranges (NOTE: we trust that min and max have been updated in the
// ConsensusMap::convert() method !)
const double model_low = map_model.getMin()[ConsensusFeature::RT];
const double scene_low = map_scene.getMin()[ConsensusFeature::RT];
const double model_high = map_model.getMax()[ConsensusFeature::RT];
const double scene_high = map_scene.getMax()[ConsensusFeature::RT];
// OLD STUFF
// const double rt_low = (maps[0].getMin()[ConsensusFeature::RT] + maps[1].getMin()[ConsensusFeature::RT]) / 2.;
// const double rt_high = (maps[0].getMax()[ConsensusFeature::RT] + maps[1].getMax()[ConsensusFeature::RT]) / 2.;
// Initialize the hash tables: shift_hash_
// OLD STUFF: was: rt_scaling_hash_, rt_low_hash_, and rt_high_hash_
{
// (over)estimate the required number of buckets for shifting
double max_shift = param_.getValue("max_shift");
// actually the largest possible shift can be much smaller, depending on the data
do
{
if (max_shift < 0)
max_shift = -max_shift;
// ...ml@@@mh........ , ........ml@@@mh...
// ........sl@@@sh... , ...sl@@@sh........
double diff;
diff = model_high - scene_low;
if (diff < 0)
diff = -diff;
if (max_shift > diff)
max_shift = diff;
diff = model_low - scene_high;
if (diff < 0)
diff = -diff;
if (max_shift > diff)
max_shift = diff;
}
while (0);
const Int shift_buckets_num_half = 4 + (Int) ceil((max_shift) / shift_bucket_size);
const Int shift_buckets_num = 1 + 2 * shift_buckets_num_half;
shift_hash_.getData().clear();
shift_hash_.getData().resize(shift_buckets_num);
shift_hash_.setMapping(shift_bucket_size, shift_buckets_num_half, 0);
}
setProgress(++actual_progress);
//**************************************************************************
// compute the ratio of the total intensities of both maps, for normalization
double total_intensity_ratio;
do
{
double total_int_model_map = 0;
for (Size i = 0; i < model_map.size(); ++i)
{
total_int_model_map += model_map[i].getIntensity();
}
setProgress(++actual_progress);
double total_int_scene_map = 0;
for (Size i = 0; i < scene_map.size(); ++i)
{
total_int_scene_map += scene_map[i].getIntensity();
}
setProgress(++actual_progress);
// ... and finally ...
total_intensity_ratio = total_int_model_map / total_int_scene_map;
}
while (0); // (the extra syntax helps with code folding in eclipse!)
setProgress((actual_progress = 20));
/// The serial number is incremented for each invocation of this, to avoid overwriting of hash table dumps.
static Int dump_buckets_serial = 0;
++dump_buckets_serial;
//**************************************************************************
// Hashing
// Compute the transformations between each point pair in the model map
// and each point pair in the scene map and hash the shift
// transformation.
// To speed up the calculation of the final transformation, we confine the number of
// considered point pairs. We match a point p in the model map only onto those points p'
// in the scene map that lie in a certain mz interval.
Size const model_map_size = model_map.size(); // i /* OLD STUFF: also: j */
Size const scene_map_size = scene_map.size(); // k /* OLD STUFF: also: l */
const double winlength_factor_baseline = 0.1; // MAGIC ALERT: Each window is given unit weight. If there are too many pairs for a window, the individual contributions will be very small, but running time will be high, so we provide a cutoff for this. Typically this will exclude compounds which elute over the whole retention time range from consideration.
///////////////////////////////////////////////////////////////////
// Hashing: Estimate the shift
do // begin of hashing (the extra syntax helps with code folding in eclipse!)
{
String dump_pairs_filename;
std::ofstream dump_pairs_file;
if (do_dump_pairs)
{
dump_pairs_filename = dump_pairs_basename + String(dump_buckets_serial);
dump_pairs_file.open(dump_pairs_filename.c_str());
dump_pairs_file << "#" << ' ' << "i" << ' ' << "k" << std::endl;
}
setProgress(++actual_progress);
// first point in model map
for (Size i = 0, i_low = 0, i_high = 0, k_low = 0, k_high = 0; i < model_map_size - 1; ++i)
{
setProgress(actual_progress + float(i) / model_map_size * 10.f);
// Adjust window around i in model map
while (i_low < model_map_size && model_map[i_low].getMZ() < model_map[i].getMZ() - mz_pair_max_distance)
++i_low;
while (i_high < model_map_size && model_map[i_high].getMZ() <= model_map[i].getMZ() + mz_pair_max_distance)
++i_high;
double i_winlength_factor = 1. / (i_high - i_low);
i_winlength_factor -= winlength_factor_baseline;
if (i_winlength_factor <= 0)
continue;
// Adjust window around k in scene map
while (k_low < scene_map_size && scene_map[k_low].getMZ() < model_map[i].getMZ() - mz_pair_max_distance)
++k_low;
while (k_high < scene_map_size && scene_map[k_high].getMZ() <= model_map[i].getMZ() + mz_pair_max_distance)
++k_high;
// first point in scene map
for (Size k = k_low; k < k_high; ++k)
{
double k_winlength_factor = 1. / (k_high - k_low);
k_winlength_factor -= winlength_factor_baseline;
if (k_winlength_factor <= 0)
continue;
// compute similarity of intensities i k
double similarity_ik;
{
const double int_i = model_map[i].getIntensity();
const double int_k = scene_map[k].getIntensity() * total_intensity_ratio;
similarity_ik = (int_i < int_k) ? int_i / int_k : int_k / int_i;
// weight is inverse proportional to number of elements with similar mz
similarity_ik *= i_winlength_factor;
similarity_ik *= k_winlength_factor;
// VV_(int_i<<' '<<int_k<<' '<<int_similarity_ik);
}
// compute the transformation (i) -> (k)
double shift = model_map[i].getRT() - scene_map[k].getRT();
// hash the images of scaling, rt_low and rt_high into their respective hash tables
shift_hash_.addValue(shift, similarity_ik);
if (do_dump_pairs)
{
dump_pairs_file << i << ' ' << model_map[i].getRT() << ' ' << model_map[i].getMZ() << ' ' << k << ' ' << scene_map[k].getRT() << ' '
<< scene_map[k].getMZ() << ' ' << similarity_ik << ' ' << std::endl;
}
} // k
} // i
}
while (0); // end of hashing (the extra syntax helps with code folding in eclipse!)
setProgress((actual_progress = 30));
///////////////////////////////////////////////////////////////////
// work on shift_hash_
// double shift_low;
// double shift_centroid;
// double shift_high;
// OLD STUFF
// double shift_low;
double shift_centroid;
// double shift_high;
do
{
UInt filtering_stage = 0;
// optionally, dump before filtering
String dump_buckets_filename;
std::ofstream dump_buckets_file;
if (do_dump_buckets)
{
dump_buckets_filename = dump_buckets_basename + "_" + String(dump_buckets_serial);
dump_buckets_file.open(dump_buckets_filename.c_str());
VV_(dump_buckets_filename);
dump_buckets_file << "# shift hash table buckets dump ( scale, height ) : " << dump_buckets_filename << std::endl;
dump_buckets_file << "# unfiltered hash data\n";
for (Size index = 0; index < shift_hash_.getData().size(); ++index)
{
const double image = shift_hash_.index2key(index);
const double height = shift_hash_.getData()[index];
dump_buckets_file << filtering_stage << '\t' << index << '\t' << image << '\t' << height << '\n';
}
dump_buckets_file << '\n';
}
++filtering_stage;
setProgress(++actual_progress);
// apply tophat filter to histogram
MorphologicalFilter morph_filter;
Param morph_filter_param;
morph_filter_param.setValue("struc_elem_unit", "DataPoints");
morph_filter_param.setValue("struc_elem_length", double(struc_elem_length_datapoints));
morph_filter_param.setValue("method", "tophat");
morph_filter.setParameters(morph_filter_param);
LinearInterpolationType_::container_type buffer(shift_hash_.getData().size());
morph_filter.filterRange(shift_hash_.getData().begin(), shift_hash_.getData().end(), buffer.begin());
shift_hash_.getData().swap(buffer);
// optionally, dump after filtering
if (do_dump_buckets)
{
dump_buckets_file << "# tophat filtered hash data\n";
for (Size index = 0; index < shift_hash_.getData().size(); ++index)
{
const double image = shift_hash_.index2key(index);
const double height = shift_hash_.getData()[index];
dump_buckets_file << filtering_stage << '\t' << index << '\t' << image << '\t' << height << '\n';
}
dump_buckets_file << '\n';
}
setProgress(++actual_progress);
++filtering_stage;
// compute freq_cutoff using a fancy criterion to distinguish between the noise level of the histogram and enriched histogram bins
double freq_cutoff_low;
do
{
{
std::copy(shift_hash_.getData().begin(), shift_hash_.getData().end(), buffer.begin());
std::sort(buffer.begin(), buffer.end(), std::greater<double>());
double freq_intercept = shift_hash_.getData().front();
double freq_slope = (shift_hash_.getData().back() - shift_hash_.getData().front()) / double(buffer.size())
/ scaling_histogram_crossing_slope;
if (!freq_slope || !buffer.size())
{
// in fact these conditions are actually impossible, but let's be really sure ;-)
freq_cutoff_low = 0;
}
else
{
Size index = 1; // not 0 (!)
while (buffer[index] >= freq_intercept + freq_slope * double(index))
{
++index;
}
freq_cutoff_low = buffer[--index]; // note that we have index >= 1
}
}
}
while (0);
setProgress(++actual_progress);
// apply freq_cutoff, setting smaller values to zero
for (Size index = 0; index < shift_hash_.getData().size(); ++index)
{
if (shift_hash_.getData()[index] < freq_cutoff_low)
{
shift_hash_.getData()[index] = 0;
}
}
setProgress(++actual_progress);
// optionally, dump after noise filtering using freq_cutoff
if (do_dump_buckets)
{
dump_buckets_file << "# after freq_cutoff, which is: " << freq_cutoff_low << '\n';
for (Size index = 0; index < shift_hash_.getData().size(); ++index)
{
const double image = shift_hash_.index2key(index);
const double height = shift_hash_.getData()[index];
dump_buckets_file << filtering_stage << '\t' << index << '\t' << image << '\t' << height << '\n';
}
dump_buckets_file << '\n';
}
setProgress(++actual_progress);
// iterative cut-off based on mean and stdev - relies upon scaling_cutoff_stdev_multiplier which is a bit hard to set right.
{
Math::BasicStatistics<double> statistics;
std::vector<double>::const_iterator data_begin = shift_hash_.getData().begin();
const Size data_size = shift_hash_.getData().size();
Size data_range_begin = 0;
Size data_range_end = data_size;
for (UInt loop = 0; loop < loops_mean_stdev_cutoff; ++loop) // MAGIC ALERT: number of loops
{
statistics.update(data_begin + data_range_begin, data_begin + data_range_end);
double mean = statistics.mean() + data_range_begin;
double stdev = sqrt(statistics.variance());
data_range_begin = floor(std::max<double>(mean - scaling_cutoff_stdev_multiplier * stdev, 0));
data_range_end = ceil(std::min<double>(mean + scaling_cutoff_stdev_multiplier * stdev + 1, data_size));
const double outside_mean = shift_hash_.index2key(mean);
const double outside_stdev = stdev * shift_hash_.getScale();
// shift_low = (outside_mean - outside_stdev);
shift_centroid = (outside_mean);
// shift_high = (outside_mean + outside_stdev);
if (do_dump_buckets)
{
dump_buckets_file << "# loop: " << loop << " mean: " << outside_mean << " stdev: " << outside_stdev << " (mean-stdev): "
<< outside_mean - outside_stdev << " (mean+stdev): " << outside_mean + outside_stdev
<< " data_range_begin: " << data_range_begin << " data_range_end: "
<< data_range_end << std::endl;
}
}
setProgress(++actual_progress);
}
if (do_dump_buckets)
{
dump_buckets_file << "# EOF" << std::endl;
dump_buckets_file.close();
}
setProgress(80);
}
while (0);
//************************************************************************************
// Estimate transform
// Compute the shifts at the low and high ends by looking at (around) the fullest bins.
double intercept;
#if 1 // yes of course, use centroids for images of rt_low and rt_high
intercept = shift_centroid;
#else // ooh, use maximum bins instead (Note: this is a fossil which would disregard most of the above computations! The code is left here for developers/debugging only.)
const Size rt_low_max_index = std::distance(shift_hash_.getData().begin(),
std::max_element(shift_hash_.getData().begin(), shift_hash_.getData().end()));
intercept = shift_hash_.index2key(rt_low_max_index);
#endif
VV_(intercept);
setProgress(++actual_progress);
// set trafo
{
Param params;
params.setValue("slope", 1.0);
params.setValue("intercept", intercept);
TransformationDescription trafo;
trafo.fitModel("linear", params);
transformation = trafo;
}
setProgress(++actual_progress);
endProgress();
return;
} // run()