def integrationBorderShortestPath(selected_pg, target_run, transformation_collection_, tree):
"""Determine the optimal integration border by using the shortest path in the MST
Args:
selected_pg(list(GeneralPeakGroup)): list of selected peakgroups (e.g. those passing the quality threshold)
target_run(String): run id of the target run (where value is missing)
transformation_collection_(:class:`.LightTransformationData`): structure to hold binary transformations between two different retention time spaces
tree(list(tuple)): a minimum spanning tree (MST) represented as list of edges (for example [('0', '1'), ('1', '2')] ). Node names need to correspond to run ids.
Returns:
A tuple of (left_integration_border, right_integration_border)
"""
# Get the shortest path through the MST
available_runs = [pg.peptide.run.get_id() for pg in selected_pg if pg.get_fdr_score() < 1.0]
final_path = graphs.findShortestMSTPath(tree, target_run, available_runs)
# Extract the starting point and the starting borders (left/right)
source_run = final_path[-1]
best_pg = [pg for pg in selected_pg if pg.peptide.run.get_id() == source_run][0]
rwidth = float(best_pg.get_value("rightWidth"))
lwidth = float(best_pg.get_value("leftWidth"))
# if verb: print "Compare from", source_run, "directly to", target_run,\
# transformation_collection_.getTrafo(source_run, target_run).predict([lwidth])[0], \
# transformation_collection_.getTrafo(source_run, target_run).predict([rwidth])[0]
# Traverse path in reverse order
for target_run_ in reversed(final_path[:-1]):
# Transform, go to next step
lwidth = transformation_collection_.getTrafo(source_run, target_run_).predict([lwidth])[0]
rwidth = transformation_collection_.getTrafo(source_run, target_run_).predict([rwidth])[0]
source_run = target_run_
return lwidth, rwidth
def test_findShortestMSTPath(self):
"""Test the findShortestMSTPath algorithm"""
# Test path with one possibility
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_0"])
expected = ['0_2', '0_1', '0_0']
self.assertEqual(expected, res)
# Test path with one possibility
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_1"])
expected = ['0_2', '0_1']
self.assertEqual(expected, res)
# Test path with one possibility
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_5"])
expected = ['0_2', '0_1', '0_4', '0_5']
self.assertEqual(expected, res)
# Test path with two possibilities, the shorter one is 0_0 and should
# be chosen
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_5", "0_0"])
expected = ['0_2', '0_1', '0_0']
self.assertEqual(expected, res)
def test_findShortestMSTPath(self):
"""Test the findShortestMSTPath algorithm"""
# Test path with one possibility
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_0"])
expected = ['0_2', '0_1', '0_0']
self.assertEqual(expected, res)
# Test path with one possibility
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_1"])
expected = ['0_2', '0_1']
self.assertEqual(expected, res)
# Test path with one possibility
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_5"])
expected = ['0_2', '0_1', '0_4', '0_5']
self.assertEqual(expected, res)
# Test path with two possibilities, the shorter one is 0_0 and should
# be chosen
res = graphs.findShortestMSTPath(self.mst3, "0_2", ["0_5", "0_0"])
expected = ['0_2', '0_1', '0_0']
self.assertEqual(expected, res)
def integrationBorderShortestPath(selected_pg, target_run,
transformation_collection_, tree):
"""Determine the optimal integration border by using the shortest path in the MST
Args:
selected_pg(list(GeneralPeakGroup)): list of selected peakgroups (e.g. those passing the quality threshold)
target_run(String): run id of the target run (where value is missing)
transformation_collection_(:class:`.LightTransformationData`): structure to hold binary transformations between two different retention time spaces
tree(list(tuple)): a minimum spanning tree (MST) represented as list of edges (for example [('0', '1'), ('1', '2')] ). Node names need to correspond to run ids.
Returns:
A tuple of (left_integration_border, right_integration_border)
"""
# Get the shortest path through the MST
available_runs = [
pg.peptide.run.get_id() for pg in selected_pg
if pg.get_fdr_score() < 1.0
]
final_path = graphs.findShortestMSTPath(tree, target_run, available_runs)
# Extract the starting point and the starting borders (left/right)
source_run = final_path[-1]
best_pg = [
pg for pg in selected_pg if pg.peptide.run.get_id() == source_run
][0]
rwidth = float(best_pg.get_value("rightWidth"))
lwidth = float(best_pg.get_value("leftWidth"))
# if verb: print "Compare from", source_run, "directly to", target_run,\
# transformation_collection_.getTrafo(source_run, target_run).predict([lwidth])[0], \
# transformation_collection_.getTrafo(source_run, target_run).predict([rwidth])[0]
# Traverse path in reverse order
for target_run_ in reversed(final_path[:-1]):
# Transform, go to next step
lwidth = transformation_collection_.getTrafo(
source_run, target_run_).predict([lwidth])[0]
rwidth = transformation_collection_.getTrafo(
source_run, target_run_).predict([rwidth])[0]
source_run = target_run_
return lwidth, rwidth
