ExitCodes main_(int, const char **) override
{
String in = getStringOption_("in");
String out = getStringOption_("out");
String algo_type = getStringOption_("algorithm_type");
String acc_filter = getStringOption_("accession_filter");
String desc_filter = getStringOption_("description_filter");
double ratio_threshold = getDoubleOption_("ratio_threshold");
ConsensusXMLFile infile;
infile.setLogType(log_type_);
ConsensusMap map;
infile.load(in, map);
//map normalization
if (algo_type == "robust_regression")
{
map.sortBySize();
vector<double> results = ConsensusMapNormalizerAlgorithmThreshold::computeCorrelation(map, ratio_threshold, acc_filter, desc_filter);
ConsensusMapNormalizerAlgorithmThreshold::normalizeMaps(map, results);
}
else if (algo_type == "median")
{
ConsensusMapNormalizerAlgorithmMedian::normalizeMaps(map, ConsensusMapNormalizerAlgorithmMedian::NM_SCALE, acc_filter, desc_filter);
}
else if (algo_type == "median_shift")
{
ConsensusMapNormalizerAlgorithmMedian::normalizeMaps(map, ConsensusMapNormalizerAlgorithmMedian::NM_SHIFT, acc_filter, desc_filter);
}
else if (algo_type == "quantile")
{
if (acc_filter != "" || desc_filter != "")
{
LOG_WARN << endl << "NOTE: Accession / description filtering is not supported in quantile normalization mode. Ignoring filters." << endl << endl;
}
ConsensusMapNormalizerAlgorithmQuantile::normalizeMaps(map);
}
else
{
cerr << "Unknown algorithm type '" << algo_type.c_str() << "'." << endl;
return ILLEGAL_PARAMETERS;
}
//annotate output with data processing info and save output file
addDataProcessing_(map, getProcessingInfo_(DataProcessing::NORMALIZATION));
infile.store(out, map);
return EXECUTION_OK;
}
void FeatureGroupingAlgorithmQT::group_(const vector<MapType>& maps,
ConsensusMap& out)
{
// check that the number of maps is ok:
if (maps.size() < 2)
{
throw Exception::IllegalArgument(__FILE__, __LINE__, OPENMS_PRETTY_FUNCTION,
"At least two maps must be given!");
}
QTClusterFinder cluster_finder;
cluster_finder.setParameters(param_.copy("", true));
cluster_finder.run(maps, out);
StringList ms_run_locations;
// add protein IDs and unassigned peptide IDs to the result map here,
// to keep the same order as the input maps (useful for output later):
for (typename vector<MapType>::const_iterator map_it = maps.begin();
map_it != maps.end(); ++map_it)
{
// add protein identifications to result map:
out.getProteinIdentifications().insert(
out.getProteinIdentifications().end(),
map_it->getProteinIdentifications().begin(),
map_it->getProteinIdentifications().end());
// add unassigned peptide identifications to result map:
out.getUnassignedPeptideIdentifications().insert(
out.getUnassignedPeptideIdentifications().end(),
map_it->getUnassignedPeptideIdentifications().begin(),
map_it->getUnassignedPeptideIdentifications().end());
}
// canonical ordering for checking the results:
out.sortByQuality();
out.sortByMaps();
out.sortBySize();
return;
}
ExitCodes main_(int, const char **)
{
String in = getStringOption_("in");
String out = getStringOption_("out");
String algo_type = getStringOption_("algorithm_type");
double ratio_threshold = getDoubleOption_("ratio_threshold");
ConsensusXMLFile infile;
infile.setLogType(log_type_);
ConsensusMap map;
infile.load(in, map);
//map normalization
if (algo_type == "robust_regression")
{
map.sortBySize();
vector<double> results = ConsensusMapNormalizerAlgorithmThreshold::computeCorrelation(map, ratio_threshold);
ConsensusMapNormalizerAlgorithmThreshold::normalizeMaps(map, results);
}
else if (algo_type == "median")
{
ConsensusMapNormalizerAlgorithmMedian::normalizeMaps(map);
}
else if (algo_type == "quantile")
{
ConsensusMapNormalizerAlgorithmQuantile::normalizeMaps(map);
}
else
{
cerr << "Unknown algorithm type '" << algo_type.c_str() << "'." << endl;
return ILLEGAL_PARAMETERS;
}
//annotate output with data processing info and save output file
addDataProcessing_(map, getProcessingInfo_(DataProcessing::NORMALIZATION));
infile.store(out, map);
return EXECUTION_OK;
}
void FeatureGroupingAlgorithmUnlabeled::group(const std::vector<FeatureMap> & maps, ConsensusMap & out)
{
// check that the number of maps is ok
if (maps.size() < 2)
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__, "At least two maps must be given!");
}
// define reference map (the one with most peaks)
Size reference_map_index = 0;
Size max_count = 0;
for (Size m = 0; m < maps.size(); ++m)
{
if (maps[m].size() > max_count)
{
max_count = maps[m].size();
reference_map_index = m;
}
}
std::vector<ConsensusMap> input(2);
// build a consensus map of the elements of the reference map (contains only singleton consensus elements)
MapConversion::convert(reference_map_index, maps[reference_map_index],
input[0]);
// loop over all other maps, extend the groups
StablePairFinder pair_finder;
pair_finder.setParameters(param_.copy("", true));
for (Size i = 0; i < maps.size(); ++i)
{
if (i != reference_map_index)
{
MapConversion::convert(i, maps[i], input[1]);
// compute the consensus of the reference map and map i
ConsensusMap result;
pair_finder.run(input, result);
input[0].swap(result);
}
}
// replace result with temporary map
out.swap(input[0]);
// copy back the input maps (they have been deleted while swapping)
out.getFileDescriptions() = input[0].getFileDescriptions();
// add protein IDs and unassigned peptide IDs to the result map here,
// to keep the same order as the input maps (useful for output later)
for (std::vector<FeatureMap>::const_iterator map_it = maps.begin();
map_it != maps.end(); ++map_it)
{
// add protein identifications to result map
out.getProteinIdentifications().insert(
out.getProteinIdentifications().end(),
map_it->getProteinIdentifications().begin(),
map_it->getProteinIdentifications().end());
// add unassigned peptide identifications to result map
out.getUnassignedPeptideIdentifications().insert(
out.getUnassignedPeptideIdentifications().end(),
map_it->getUnassignedPeptideIdentifications().begin(),
map_it->getUnassignedPeptideIdentifications().end());
}
// canonical ordering for checking the results, and the ids have no real meaning anyway
#if 1 // the way this was done in DelaunayPairFinder and StablePairFinder
out.sortByMZ();
#else
out.sortByQuality();
out.sortByMaps();
out.sortBySize();
#endif
return;
}