void MapAlignmentTransformer::applyToFeature_(Feature & feature,
const TransformationDescription & trafo)
{
applyToBaseFeature_(feature, trafo);
// loop over all convex hulls
vector<ConvexHull2D> & convex_hulls = feature.getConvexHulls();
for (vector<ConvexHull2D>::iterator chiter = convex_hulls.begin();
chiter != convex_hulls.end(); ++chiter)
{
// transform all hull point positions within convex hull
ConvexHull2D::PointArrayType points = chiter->getHullPoints();
chiter->clear();
for (ConvexHull2D::PointArrayType::iterator points_iter = points.begin();
points_iter != points.end();
++points_iter
)
{
DoubleReal rt = (*points_iter)[Feature::RT];
(*points_iter)[Feature::RT] = trafo.apply(rt);
}
chiter->setHullPoints(points);
}
// recurse into subordinates
for (vector<Feature>::iterator subiter = feature.getSubordinates().begin();
subiter != feature.getSubordinates().end();
++subiter)
{
applyToFeature_(*subiter, trafo);
}
}
END_SECTION
START_SECTION( double elutionModelFit(ConvexHull2D::PointArrayType current_section, bool smooth_data) )
{
// test a single feature
double elution_model_fit_score;
EmgScoring emgscore;
MRMFeature feature = OpenSWATH_Test::createMockFeature();
Feature f = feature.getFeature("tr1");
elution_model_fit_score = emgscore.elutionModelFit(f.getConvexHulls()[0].getHullPoints() , false);
TEST_REAL_SIMILAR(elution_model_fit_score, 0.981013417243958)
}
bool overlaps_(const Feature& feature, const double rt, const double pc_mz, const double rt_tolerance) const
{
if (feature.getConvexHulls().empty())
{
LOG_WARN << "HighResPrecursorMassCorrector warning: at least one feature has no convex hull - omitting feature for matching" << std::endl;
}
// get bounding box and extend by retention time tolerance
DBoundingBox<2> box = feature.getConvexHull().getBoundingBox();
DPosition<2> extend_rt(rt_tolerance, 0.01);
box.setMin(box.minPosition() - extend_rt);
box.setMax(box.maxPosition() + extend_rt);
DPosition<2> pc_pos(rt, pc_mz);
if (box.encloses(pc_pos))
{
return true;
}
else
{
return false;
}
}
ExitCodes main_(int, const char**) override
{
//-------------------------------------------------------------
// parameter handling
//-------------------------------------------------------------
String in = getStringOption_("in");
String out = getStringOption_("out");
FileTypes::Type out_type = FileTypes::nameToType(getStringOption_("out_type"));
if (out_type == FileTypes::UNKNOWN)
{
out_type = FileHandler().getTypeByFileName(out);
}
//-------------------------------------------------------------
// loading input
//-------------------------------------------------------------
MzMLFile mz_data_file;
mz_data_file.setLogType(log_type_);
PeakMap ms_peakmap;
std::vector<Int> ms_level(1, 1);
(mz_data_file.getOptions()).setMSLevels(ms_level);
mz_data_file.load(in, ms_peakmap);
if (ms_peakmap.size() == 0)
{
LOG_WARN << "The given file does not contain any conventional peak data, but might"
" contain chromatograms. This tool currently cannot handle them, sorry.";
return INCOMPATIBLE_INPUT_DATA;
}
// make sure that the spectra are sorted by m/z
ms_peakmap.sortSpectra(true);
//-------------------------------------------------------------
// get params for MTD and EPD algorithms
//-------------------------------------------------------------
Param com_param = getParam_().copy("algorithm:common:", true);
writeDebug_("Common parameters passed to both sub-algorithms (mtd and epd)", com_param, 3);
Param mtd_param = getParam_().copy("algorithm:mtd:", true);
writeDebug_("Parameters passed to MassTraceDetection", mtd_param, 3);
Param epd_param = getParam_().copy("algorithm:epd:", true);
writeDebug_("Parameters passed to ElutionPeakDetection", epd_param, 3);
//-------------------------------------------------------------
// configure and run MTD
//-------------------------------------------------------------
MassTraceDetection mt_ext;
mtd_param.insert("", com_param);
mtd_param.remove("chrom_fwhm");
mt_ext.setParameters(mtd_param);
vector<MassTrace> m_traces;
mt_ext.run(ms_peakmap, m_traces);
vector<MassTrace> m_traces_final;
bool use_epd = epd_param.getValue("enabled").toBool();
if (!use_epd)
{
swap(m_traces_final, m_traces);
}
else
{
ElutionPeakDetection ep_det;
epd_param.remove("enabled"); // artificially added above
epd_param.insert("", com_param);
ep_det.setParameters(epd_param);
std::vector<MassTrace> split_mtraces;
// note: this step will destroy any meta data annotation (e.g. FWHM_mz_avg)
ep_det.detectPeaks(m_traces, split_mtraces);
if (ep_det.getParameters().getValue("width_filtering") == "auto")
{
m_traces_final.clear();
ep_det.filterByPeakWidth(split_mtraces, m_traces_final);
LOG_INFO << "Notice: " << split_mtraces.size() - m_traces_final.size()
<< " of total " << split_mtraces.size()
<< " were dropped because of too low peak width." << std::endl;
}
else
{
swap(m_traces_final, split_mtraces);
}
}
//-------------------------------------------------------------
// writing consensus map output
//-------------------------------------------------------------
if (out_type == FileTypes::CONSENSUSXML)
{
ConsensusMap consensus_map;
StringList ms_runs;
ms_peakmap.getPrimaryMSRunPath(ms_runs);
consensus_map.setPrimaryMSRunPath(ms_runs);
for (Size i = 0; i < m_traces_final.size(); ++i)
{
if (m_traces_final[i].getSize() == 0) continue;
ConsensusFeature fcons;
int k = 0;
for (MassTrace::const_iterator it = m_traces_final[i].begin(); it != m_traces_final[i].end(); ++it)
{
FeatureHandle fhandle;
fhandle.setRT(it->getRT());
fhandle.setMZ(it->getMZ());
fhandle.setIntensity(it->getIntensity());
fhandle.setUniqueId(++k);
fcons.insert(fhandle);
}
fcons.setMetaValue(3, m_traces_final[i].getLabel());
fcons.setCharge(0);
fcons.setWidth(m_traces_final[i].estimateFWHM(use_epd));
fcons.setQuality(1 - (1.0 / m_traces_final[i].getSize()));
fcons.setRT(m_traces_final[i].getCentroidRT());
fcons.setMZ(m_traces_final[i].getCentroidMZ());
fcons.setIntensity(m_traces_final[i].getIntensity(false));
consensus_map.push_back(fcons);
}
consensus_map.applyMemberFunction(&UniqueIdInterface::setUniqueId);
addDataProcessing_(consensus_map, getProcessingInfo_(DataProcessing::QUANTITATION));
consensus_map.setUniqueId();
ConsensusXMLFile().store(out, consensus_map);
}
else //(out_type == FileTypes::FEATUREXML)
{
//-----------------------------------------------------------
// convert mass traces to features
//-----------------------------------------------------------
std::vector<double> stats_sd;
FeatureMap ms_feat_map;
StringList ms_runs;
ms_peakmap.getPrimaryMSRunPath(ms_runs);
ms_feat_map.setPrimaryMSRunPath(ms_runs);
for (Size i = 0; i < m_traces_final.size(); ++i)
{
if (m_traces_final[i].getSize() == 0) continue;
m_traces_final[i].updateMeanMZ();
m_traces_final[i].updateWeightedMZsd();
Feature f;
f.setMetaValue(3, m_traces_final[i].getLabel());
f.setCharge(0);
f.setMZ(m_traces_final[i].getCentroidMZ());
f.setIntensity(m_traces_final[i].getIntensity(false));
f.setRT(m_traces_final[i].getCentroidRT());
f.setWidth(m_traces_final[i].estimateFWHM(use_epd));
f.setOverallQuality(1 - (1.0 / m_traces_final[i].getSize()));
f.getConvexHulls().push_back(m_traces_final[i].getConvexhull());
double sd = m_traces_final[i].getCentroidSD();
f.setMetaValue("SD", sd);
f.setMetaValue("SD_ppm", sd / f.getMZ() * 1e6);
if (m_traces_final[i].fwhm_mz_avg > 0) f.setMetaValue("FWHM_mz_avg", m_traces_final[i].fwhm_mz_avg);
stats_sd.push_back(m_traces_final[i].getCentroidSD());
ms_feat_map.push_back(f);
}
// print some stats about standard deviation of mass traces
if (stats_sd.size() > 0)
{
std::sort(stats_sd.begin(), stats_sd.end());
LOG_INFO << "Mass trace m/z s.d.\n"
<< " low quartile: " << stats_sd[stats_sd.size() * 1 / 4] << "\n"
<< " median: " << stats_sd[stats_sd.size() * 1 / 2] << "\n"
<< " upp quartile: " << stats_sd[stats_sd.size() * 3 / 4] << std::endl;
}
ms_feat_map.applyMemberFunction(&UniqueIdInterface::setUniqueId);
//-------------------------------------------------------------
// writing output
//-------------------------------------------------------------
// annotate output with data processing info TODO
addDataProcessing_(ms_feat_map, getProcessingInfo_(DataProcessing::QUANTITATION));
//ms_feat_map.setUniqueId();
FeatureXMLFile().store(out, ms_feat_map);
}
return EXECUTION_OK;
}
p.setQuality( 1, (QualityType)0.0); TEST_REAL_SIMILAR(p.getQuality(0), 0.0) TEST_REAL_SIMILAR(p.getQuality(1), 0.0) TEST_PRECONDITION_VIOLATED(p.setQuality( 10, (QualityType)1.0)) END_SECTION //do not change these datastructures, they are used in the following tests... std::vector< ConvexHull2D > hulls(2); hulls[0].addPoint(DPosition<2>(1.0,2.0)); hulls[0].addPoint(DPosition<2>(3.0,4.0)); hulls[1].addPoint(DPosition<2>(0.5,0.0)); hulls[1].addPoint(DPosition<2>(1.0,1.0)); START_SECTION((const vector<ConvexHull2D>& getConvexHulls() const)) Feature tmp; TEST_EQUAL(tmp.getConvexHulls().size(),0) END_SECTION START_SECTION((vector<ConvexHull2D>& getConvexHulls())) Feature tmp; tmp.setConvexHulls(hulls); TEST_EQUAL(tmp.getConvexHulls().size(),2) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[0][0],1.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[0][1],2.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[1][0],3.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[0].getHullPoints()[1][1],4.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[0][0],0.5) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[0][1],0.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[1][0],1.0) TEST_REAL_SIMILAR(tmp.getConvexHulls()[1].getHullPoints()[1][1],1.0) END_SECTION
