def _read_peakgroup_files(self, aligned_pg_files, swathfiles):
"""
The peakgroup files have to have the following columns:
- FullPeptideName
- Charge
- leftWidth
- rightWidth
- m_score
- Intensity
- align_runid
- transition_group_id
"""
# Read in the peakgroup files, parse them and map across runs
reader = SWATHScoringReader.newReader(aligned_pg_files,
"openswath",
readmethod="gui",
errorHandling="loose")
new_exp = Experiment()
new_exp.runs = reader.parse_files(REALIGN_RUNS)
multipeptides = new_exp.get_all_multipeptides(FDR_CUTOFF,
verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
for m in multipeptides:
pg = m.find_best_peptide_pg()
identifier = pg.get_value("FullPeptideName") + "/" + pg.get_value(
"Charge")
peakgroup_map[identifier] = m
for swathrun in swathfiles.getSwathFiles():
if ONLY_SHOW_QUANTIFIED:
intersection = set(
swathrun.get_all_precursor_ids()).intersection(
peakgroup_map.keys())
todelete = set(
swathrun.get_all_precursor_ids()).difference(intersection)
if len(intersection) == 0:
print "Could not find any intersection between identifiers in your transition file and the provided chromatograms"
print len(intersection)
swathrun.remove_precursors(todelete)
# for each precursor in this run, identify the best peakgroup and store the value
for precursor_id in swathrun.get_all_precursor_ids():
if not peakgroup_map.has_key(precursor_id):
continue
m = peakgroup_map[precursor_id]
if m.hasPrecursorGroup(swathrun.runid):
for pg in m.getPrecursorGroup(
swathrun.runid).getAllPeakgroups():
l, r = [
float(pg.get_value("leftWidth")),
float(pg.get_value("rightWidth"))
]
fdrscore = float(pg.get_value("m_score"))
intensity = float(pg.get_value("Intensity"))
swathrun.add_peakgroup_data(precursor_id, l, r,
fdrscore, intensity)
def test_parse_files(self):
filename = os.path.join(self.datadir_gui, "dataset3.csv")
filename_mzml = os.path.join(self.datadir_gui, "dataset3.mzML")
r = reader.SWATHScoringReader.newReader([filename], "openswath", readmethod="gui", errorHandling="loose")
new_exp = Experiment()
new_exp.runs = r.parse_files(True)
multipeptides = new_exp.get_all_multipeptides(1.0, verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
mapper.buildPeakgroupMap(multipeptides, peakgroup_map)
self.assertEqual(len(peakgroup_map.keys()), 2)
self.assertEqual(sorted(list(peakgroup_map.keys())), ['testpeptide/0', 'testpeptide/0_pr'])
def _read_peakgroup_files(self, aligned_pg_files, swathfiles):
"""
The peakgroup files have to have the following columns:
- FullPeptideName
- Charge
- leftWidth
- rightWidth
- m_score
- Intensity
- align_runid
- transition_group_id
"""
# Read in the peakgroup files, parse them and map across runs
reader = SWATHScoringReader.newReader(aligned_pg_files, "openswath", readmethod="gui", errorHandling="loose")
new_exp = Experiment()
new_exp.runs = reader.parse_files(REALIGN_RUNS)
multipeptides = new_exp.get_all_multipeptides(FDR_CUTOFF, verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
for m in multipeptides:
pg = m.find_best_peptide_pg()
identifier = pg.get_value("FullPeptideName") + "/" + pg.get_value("Charge")
peakgroup_map[ identifier ] = m
for swathrun in swathfiles.getSwathFiles():
if ONLY_SHOW_QUANTIFIED:
intersection = set(swathrun.get_all_precursor_ids()).intersection( peakgroup_map.keys() )
todelete = set(swathrun.get_all_precursor_ids()).difference(intersection)
if len(intersection) == 0:
print "Could not find any intersection between identifiers in your transition file and the provided chromatograms"
print len(intersection)
swathrun.remove_precursors(todelete)
# for each precursor in this run, identify the best peakgroup and store the value
for precursor_id in swathrun.get_all_precursor_ids():
if not peakgroup_map.has_key(precursor_id):
continue
m = peakgroup_map[ precursor_id ]
if m.hasPrecursorGroup(swathrun.runid):
for pg in m.getPrecursorGroup(swathrun.runid).getAllPeakgroups():
l,r = [ float(pg.get_value("leftWidth")), float(pg.get_value("rightWidth")) ]
fdrscore = float(pg.get_value("m_score"))
intensity = float(pg.get_value("Intensity"))
swathrun.add_peakgroup_data(precursor_id,l,r, fdrscore, intensity)
def test_parse_files(self):
filename = os.path.join(self.datadir_gui, "dataset3.csv")
filename_mzml = os.path.join(self.datadir_gui, "dataset3.mzML")
r = reader.SWATHScoringReader.newReader([filename],
"openswath",
readmethod="gui",
errorHandling="loose")
new_exp = Experiment()
new_exp.runs = r.parse_files(True)
multipeptides = new_exp.get_all_multipeptides(1.0, verbose=False)
# Build map of the PeptideName/Charge to the individual multipeptide
peakgroup_map = {}
mapper.buildPeakgroupMap(multipeptides, peakgroup_map)
self.assertEqual(len(peakgroup_map.keys()), 2)
self.assertEqual(sorted(list(peakgroup_map.keys())),
['testpeptide/0', 'testpeptide/0_pr'])
