void QTClusterFinder::computeClustering_(Grid & grid,
list<QTCluster> & clustering)
{
clustering.clear();
distances_.clear();
// FeatureDistance produces normalized distances (between 0 and 1):
const DoubleReal max_distance = 1.0;
// iterate over all grid cells:
for (Grid::iterator it = grid.begin(); it != grid.end(); ++it)
{
const Grid::CellIndex & act_coords = it.index();
const Int x = act_coords[0], y = act_coords[1];
//cout << x << " " << y << endl;
GridFeature * center_feature = it->second;
QTCluster cluster(center_feature, num_maps_, max_distance, use_IDs_);
// iterate over neighboring grid cells (1st dimension):
for (int i = x - 1; i <= x + 1; ++i)
{
// iterate over neighboring grid cells (2nd dimension):
for (int j = y - 1; j <= y + 1; ++j)
{
try
{
const Grid::CellContent & act_pos = grid.grid_at(Grid::CellIndex(i, j));
for (Grid::const_cell_iterator it_cell = act_pos.begin(); it_cell != act_pos.end(); ++it_cell)
{
GridFeature * neighbor_feature = it_cell->second;
// consider only "real" neighbors, not the element itself:
if (center_feature != neighbor_feature)
{
DoubleReal dist = getDistance_(center_feature,
neighbor_feature);
if (dist == FeatureDistance::infinity)
{
continue; // conditions not satisfied
}
// if neighbor point is a possible cluster point, add it:
if (!use_IDs_ || compatibleIDs_(cluster, neighbor_feature))
{
cluster.add(neighbor_feature, dist);
}
}
}
}
catch (std::out_of_range &)
{
}
}
}
clustering.push_back(cluster);
}
}
void StablePairFinder::run(const std::vector<ConsensusMap>& input_maps,
ConsensusMap& result_map)
{
// empty output destination:
result_map.clear(false);
// sanity checks:
if (input_maps.size() != 2)
{
throw Exception::IllegalArgument(__FILE__, __LINE__, __PRETTY_FUNCTION__,
"exactly two input maps required");
}
checkIds_(input_maps);
// set up the distance functor:
double max_intensity = max(input_maps[0].getMaxInt(),
input_maps[1].getMaxInt());
Param distance_params = param_.copy("");
distance_params.remove("use_identifications");
distance_params.remove("second_nearest_gap");
FeatureDistance feature_distance(max_intensity, false);
feature_distance.setParameters(distance_params);
// keep track of pairing:
std::vector<bool> is_singleton[2];
is_singleton[0].resize(input_maps[0].size(), true);
is_singleton[1].resize(input_maps[1].size(), true);
typedef pair<double, double> DoublePair;
DoublePair init = make_pair(FeatureDistance::infinity,
FeatureDistance::infinity);
// for every element in map 0:
// - index of nearest neighbor in map 1:
vector<UInt> nn_index_0(input_maps[0].size(), UInt(-1));
// - distances to nearest and second-nearest neighbors in map 1:
vector<DoublePair> nn_distance_0(input_maps[0].size(), init);
// for every element in map 1:
// - index of nearest neighbor in map 0:
vector<UInt> nn_index_1(input_maps[1].size(), UInt(-1));
// - distances to nearest and second-nearest neighbors in map 0:
vector<DoublePair> nn_distance_1(input_maps[1].size(), init);
// iterate over all feature pairs, find nearest neighbors:
// TODO: iterate over SENSIBLE RT (and m/z) window -- sort the maps beforehand
// to save a lot of processing time...
// Once done, remove the warning in the description of the 'use_identifications' parameter
for (UInt fi0 = 0; fi0 < input_maps[0].size(); ++fi0)
{
const ConsensusFeature& feat0 = input_maps[0][fi0];
for (UInt fi1 = 0; fi1 < input_maps[1].size(); ++fi1)
{
const ConsensusFeature& feat1 = input_maps[1][fi1];
if (use_IDs_ && !compatibleIDs_(feat0, feat1)) // check peptide IDs
{
continue; // mismatch
}
pair<bool, double> result = feature_distance(feat0, feat1);
double distance = result.second;
// we only care if distance constraints are satisfied for "best
// matches", not for second-best; this means that second-best distances
// can become smaller than best distances
// (e.g. the RT is larger than allowed (->invalid pair), but m/z is perfect and has the most weight --> better score!)
bool valid = result.first;
// update entries for map 0:
if (distance < nn_distance_0[fi0].second)
{
if (valid && (distance < nn_distance_0[fi0].first))
{
nn_distance_0[fi0].second = nn_distance_0[fi0].first;
nn_distance_0[fi0].first = distance;
nn_index_0[fi0] = fi1;
}
else
{
nn_distance_0[fi0].second = distance;
}
}
// update entries for map 1:
if (distance < nn_distance_1[fi1].second)
{
if (valid && (distance < nn_distance_1[fi1].first))
{
nn_distance_1[fi1].second = nn_distance_1[fi1].first;
nn_distance_1[fi1].first = distance;
nn_index_1[fi1] = fi0;
}
else
{
nn_distance_1[fi1].second = distance;
}
}
}
}
// if features from the two maps are nearest neighbors of each other, they
// can become a pair:
for (UInt fi0 = 0; fi0 < input_maps[0].size(); ++fi0)
{
UInt fi1 = nn_index_0[fi0]; // nearest neighbor of "fi0" in map 1
// cout << "index: " << fi0 << ", RT: " << input_maps[0][fi0].getRT()
// << ", MZ: " << input_maps[0][fi0].getMZ() << endl
// << "neighbor: " << fi1 << ", RT: " << input_maps[1][fi1].getRT()
// << ", MZ: " << input_maps[1][fi1].getMZ() << endl
// << "d(i,j): " << nn_distance_0[fi0].first << endl
// << "d2(i): " << nn_distance_0[fi0].second << endl
// << "d2(j): " << nn_distance_1[fi1].second << endl;
// criteria set by the parameters must be fulfilled:
if ((nn_distance_0[fi0].first < FeatureDistance::infinity) &&
(nn_distance_0[fi0].first * second_nearest_gap_ <= nn_distance_0[fi0].second))
{
// "fi0" satisfies constraints...
if ((nn_index_1[fi1] == fi0) &&
(nn_distance_1[fi1].first * second_nearest_gap_ <= nn_distance_1[fi1].second))
{
// ...nearest neighbor of "fi0" also satisfies constraints (yay!)
// cout << "match!" << endl;
result_map.push_back(ConsensusFeature());
ConsensusFeature& f = result_map.back();
f.insert(input_maps[0][fi0]);
f.getPeptideIdentifications().insert(f.getPeptideIdentifications().end(),
input_maps[0][fi0].getPeptideIdentifications().begin(),
input_maps[0][fi0].getPeptideIdentifications().end());
f.insert(input_maps[1][fi1]);
f.getPeptideIdentifications().insert(f.getPeptideIdentifications().end(),
input_maps[1][fi1].getPeptideIdentifications().begin(),
input_maps[1][fi1].getPeptideIdentifications().end());
f.computeConsensus();
double quality = 1.0 - nn_distance_0[fi0].first;
double quality0 = 1.0 - nn_distance_0[fi0].first * second_nearest_gap_ / nn_distance_0[fi0].second;
double quality1 = 1.0 - nn_distance_1[fi1].first * second_nearest_gap_ / nn_distance_1[fi1].second;
quality = quality * quality0 * quality1; // TODO other formula?
// incorporate existing quality values:
Size size0 = max(input_maps[0][fi0].size(), size_t(1));
Size size1 = max(input_maps[1][fi1].size(), size_t(1));
// quality contribution from first map:
quality0 = input_maps[0][fi0].getQuality() * (size0 - 1);
// quality contribution from second map:
quality1 = input_maps[1][fi1].getQuality() * (size1 - 1);
f.setQuality((quality + quality0 + quality1) / (size0 + size1 - 1));
is_singleton[0][fi0] = false;
is_singleton[1][fi1] = false;
}
}
}
// write out unmatched consensus features
for (UInt input = 0; input <= 1; ++input)
{
for (UInt index = 0; index < input_maps[input].size(); ++index)
{
if (is_singleton[input][index])
{
result_map.push_back(input_maps[input][index]);
if (result_map.back().size() < 2) // singleton consensus feature
{
result_map.back().setQuality(0.0);
}
}
}
}
// canonical ordering for checking the results, and the ids have no real meaning anyway
result_map.sortByMZ();
// protein IDs and unassigned peptide IDs are added to the result by the
// FeatureGroupingAlgorithm!
}
