def setUp(self):
# 0. id
# 1. quality score (FDR)
# 2. retention time (normalized)
# 3. intensity
m = Multipeptide()
m.set_nr_runs(2)
# Run 1
r = MockRun("0_1")
p = precursor.Precursor("precursor_1", r)
pg_tuple = ("someID_1", 0.1, 100, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), r)
prgr.addPrecursor(p)
m.insert("0_1", prgr)
# Run 2:
# - peakgroup 2 : RT = 105 seconds
# - peakgroup 3 : RT = 120 seconds
r = MockRun("0_2")
p = precursor.Precursor("precursor_1", r)
pg_tuple = ("peakgroup2", 0.2, 105, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
pg_tuple = ("peakgroup3", 0.18, 130, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), r)
prgr.addPrecursor(p)
m.insert("0_2", prgr)
self.mpep = m
self.al = algo.AlignmentAlgorithm()
self.al.verbose = True
def test_all_above_cutoff(self):
m = Multipeptide()
m.set_nr_runs(2)
self.assertFalse(m.all_above_cutoff(0.4))
help_insert(m)
self.assertTrue(m.all_above_cutoff(0.4))
self.assertFalse(m.all_above_cutoff(0.15))
def test_find_best_peptide_pg(self):
m = Multipeptide()
m.set_nr_runs(2)
self.assertIsNone(m.find_best_peptide_pg())
help_insert(m)
self.assertAlmostEqual(m.find_best_peptide_pg().get_fdr_score(), 0.1)
def test_all_above_cutoff(self):
m = Multipeptide()
m.set_nr_runs(2)
self.assertFalse(m.all_above_cutoff(0.4))
help_insert(m)
self.assertTrue(m.all_above_cutoff(0.4))
self.assertFalse(m.all_above_cutoff(0.15))
def test_find_best_peptide_pg(self):
m = Multipeptide()
m.set_nr_runs(2)
self.assertIsNone(m.find_best_peptide_pg())
help_insert(m)
self.assertAlmostEqual(m.find_best_peptide_pg().get_fdr_score(), 0.1)
def test_more_than_fraction_selected(self):
m = Multipeptide()
self.assertIsNone(m.get_id())
m.insert("42_0", self.mockPrecursorGroup)
m.set_nr_runs(1)
self.assertTrue(m.more_than_fraction_selected(0.1))
self.assertTrue(m.more_than_fraction_selected(0.6))
m.set_nr_runs(2)
self.assertTrue(m.more_than_fraction_selected(0.1))
self.assertFalse(m.more_than_fraction_selected(0.6))
def test_all_selected(self):
m = Multipeptide()
m.set_nr_runs(2)
self.assertIsNone(m.find_best_peptide_pg())
help_insert(m)
self.assertTrue(m.all_selected())
m.set_nr_runs(3)
self.assertFalse(m.all_selected())
def test_more_than_fraction_selected(self):
m = Multipeptide()
self.assertIsNone(m.get_id())
m.insert("42_0", self.mockPrecursorGroup)
m.set_nr_runs(1)
self.assertTrue(m.more_than_fraction_selected(0.1))
self.assertTrue(m.more_than_fraction_selected(0.6))
m.set_nr_runs(2)
self.assertTrue(m.more_than_fraction_selected(0.1))
self.assertFalse(m.more_than_fraction_selected(0.6))
def test_all_selected(self):
m = Multipeptide()
m.set_nr_runs(2)
self.assertIsNone(m.find_best_peptide_pg())
help_insert(m)
self.assertTrue(m.all_selected())
m.set_nr_runs(3)
self.assertFalse(m.all_selected())
def testNrRuns(self):
m = Multipeptide()
m.set_nr_runs(42)
self.assertEqual(m.get_nr_runs(), 42)
def setUp(self):
import msproteomicstoolslib.data_structures.Precursor as precursor
import msproteomicstoolslib.data_structures.PrecursorGroup as precursor_group
import msproteomicstoolslib.format.TransformationCollection as transformations
from msproteomicstoolslib.algorithms.alignment.SplineAligner import SplineAligner
import msproteomicstoolslib.algorithms.alignment.AlignmentHelper as helper
# 0. id
# 1. quality score (FDR)
# 2. retention time (normalized)
# 3. intensity
mpeps = [Multipeptide() for i in range(3)]
[m.set_nr_runs(5) for m in mpeps]
# Parameters
self.initial_alignment_cutoff = 0.001
runs = [MockRun("0_%s" % (i + 1)) for i in range(5)]
ids = 0
for i in range(5):
# Two alignment peptides
p = precursor.Precursor("anchorpeptide_1", runs[i])
pg_tuple = ("id_%s" % ids, 0.0001, 100 + i * 10, 10000)
p.add_peakgroup_tpl(pg_tuple, "anchorpeptide_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[i])
prgr.addPrecursor(p)
mpeps[0].insert(runs[i].get_id(), prgr)
ids += 1
p = precursor.Precursor("anchorpeptide_2", runs[i])
pg_tuple = ("id_%s" % ids, 0.0001, 1000 + i * 100, 10000)
p.add_peakgroup_tpl(pg_tuple, "anchorpeptide_2", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[i])
prgr.addPrecursor(p)
mpeps[1].insert(runs[i].get_id(), prgr)
ids += 1
# The noise peptide
p = precursor.Precursor("anchorpeptide_3", runs[i])
pg_tuple = ("id_%s" % ids, 0.0001, 500 + i * 40, 10000)
p.add_peakgroup_tpl(pg_tuple, "anchorpeptide_3", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[i])
prgr.addPrecursor(p)
mpeps[2].insert(runs[i].get_id(), prgr)
ids += 1
m = Multipeptide()
m.set_nr_runs(5)
# Run 1
# - peakgroup 1 : RT = 110 seconds [correct]
p = precursor.Precursor("precursor_1", runs[0])
pg_tuple = ("peakgroup1", 0.01, 100, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[0])
prgr.addPrecursor(p)
m.insert(runs[0].get_id(), prgr)
# Run 2:
# - peakgroup 2 : RT = 115 seconds [correct]
# - peakgroup 3 : RT = 130 seconds
p = precursor.Precursor("precursor_1", runs[1])
pg_tuple = ("peakgroup2", 0.2, 112, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
pg_tuple = ("peakgroup3", 0.18, 130, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[1])
prgr.addPrecursor(p)
m.insert(runs[1].get_id(), prgr)
# Run 3:
# - peakgroup 4 : RT = 120 seconds [correct]
# - peakgroup 5 : RT = 130 seconds
p = precursor.Precursor("precursor_1", runs[2])
pg_tuple = ("peakgroup4", 0.2, 120, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
pg_tuple = ("peakgroup5", 0.17, 130, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[2])
prgr.addPrecursor(p)
m.insert(runs[2].get_id(), prgr)
# Run 4:
# - peakgroup 6 : missing [correct]
# - peakgroup 7 : RT = 145 seconds
p = precursor.Precursor("precursor_1", runs[3])
pg_tuple = ("peakgroup7", 0.18, 145, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[3])
prgr.addPrecursor(p)
m.insert(runs[3].get_id(), prgr)
# Run 5:
# - peakgroup 8 : RT = 140 seconds [correct]
# - peakgroup 9 : missing
p = precursor.Precursor("precursor_1", runs[4])
pg_tuple = ("peakgroup8", 0.1, 139, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[4])
prgr.addPrecursor(p)
m.insert(runs[4].get_id(), prgr)
self.mpep = m
self.exp = Dummy()
self.exp.runs = runs
mpeps.append(m)
self.multipeptides = mpeps
# Align all against all
self.tr_data = transformations.LightTransformationData()
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
for run_0 in self.exp.runs:
for run_1 in self.exp.runs:
helper.addDataToTrafo(self.tr_data, run_0, run_1, spl_aligner,
self.multipeptides, "linear", 30)
def setUp(self):
import msproteomicstoolslib.data_structures.Precursor as precursor
import msproteomicstoolslib.data_structures.PrecursorGroup as precursor_group
import msproteomicstoolslib.format.TransformationCollection as transformations
from msproteomicstoolslib.algorithms.alignment.SplineAligner import SplineAligner
import msproteomicstoolslib.algorithms.alignment.AlignmentHelper as helper
# 0. id
# 1. quality score (FDR)
# 2. retention time (normalized)
# 3. intensity
mpeps = [Multipeptide() for i in range(3)]
[m.set_nr_runs(5) for m in mpeps]
# Parameters
self.initial_alignment_cutoff = 0.001
runs = [MockRun("0_%s" % (i+1)) for i in range(5)]
ids = 0
for i in range(5):
# Two alignment peptides
p = precursor.Precursor("anchorpeptide_1", runs[i] )
pg_tuple = ("id_%s" % ids, 0.0001, 100 + i*10, 10000)
p.add_peakgroup_tpl(pg_tuple, "anchorpeptide_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[i])
prgr.addPrecursor(p)
mpeps[0].insert(runs[i].get_id(), prgr)
ids += 1
p = precursor.Precursor("anchorpeptide_2", runs[i] )
pg_tuple = ("id_%s" % ids, 0.0001, 1000 + i*100, 10000)
p.add_peakgroup_tpl(pg_tuple, "anchorpeptide_2", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[i])
prgr.addPrecursor(p)
mpeps[1].insert(runs[i].get_id(), prgr)
ids += 1
# The noise peptide
p = precursor.Precursor("anchorpeptide_3", runs[i] )
pg_tuple = ("id_%s" % ids, 0.0001, 500 + i*40, 10000)
p.add_peakgroup_tpl(pg_tuple, "anchorpeptide_3", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[i])
prgr.addPrecursor(p)
mpeps[2].insert(runs[i].get_id(), prgr)
ids += 1
m = Multipeptide()
m.set_nr_runs(5)
# Run 1
# - peakgroup 1 : RT = 110 seconds [correct]
p = precursor.Precursor("precursor_1", runs[0])
pg_tuple = ("peakgroup1", 0.01, 100, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[0])
prgr.addPrecursor(p)
m.insert(runs[0].get_id(), prgr)
# Run 2:
# - peakgroup 2 : RT = 115 seconds [correct]
# - peakgroup 3 : RT = 130 seconds
p = precursor.Precursor("precursor_1", runs[1])
pg_tuple = ("peakgroup2", 0.2, 112, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
pg_tuple = ("peakgroup3", 0.18, 130, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[1])
prgr.addPrecursor(p)
m.insert(runs[1].get_id(), prgr)
# Run 3:
# - peakgroup 4 : RT = 120 seconds [correct]
# - peakgroup 5 : RT = 130 seconds
p = precursor.Precursor("precursor_1", runs[2])
pg_tuple = ("peakgroup4", 0.2, 120, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
pg_tuple = ("peakgroup5", 0.17, 130, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[2])
prgr.addPrecursor(p)
m.insert(runs[2].get_id(), prgr)
# Run 4:
# - peakgroup 6 : missing [correct]
# - peakgroup 7 : RT = 145 seconds
p = precursor.Precursor("precursor_1", runs[3])
pg_tuple = ("peakgroup7", 0.18, 145, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[3])
prgr.addPrecursor(p)
m.insert(runs[3].get_id(), prgr)
# Run 5:
# - peakgroup 8 : RT = 140 seconds [correct]
# - peakgroup 9 : missing
p = precursor.Precursor("precursor_1", runs[4])
pg_tuple = ("peakgroup8", 0.1, 139, 10000)
p.add_peakgroup_tpl(pg_tuple, "precursor_1", -1)
prgr = precursor_group.PrecursorGroup(p.get_id(), runs[4])
prgr.addPrecursor(p)
m.insert(runs[4].get_id(), prgr)
self.mpep = m
self.exp = Dummy()
self.exp.runs = runs
mpeps.append(m)
self.multipeptides = mpeps
# Align all against all
self.tr_data = transformations.LightTransformationData()
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
for run_0 in self.exp.runs:
for run_1 in self.exp.runs:
helper.addDataToTrafo(self.tr_data, run_0, run_1, spl_aligner, self.multipeptides, "linear", 30)
def get_all_multipeptides(self, fdr_cutoff, verbose=False, verbosity=0):
"""Match all precursors in different runs to each other.
Find all precursors that are above the fdr cutoff in each run and build
a union of those precursors. Then search for each of those precursors
in all the other runs and build a multipeptide / multiprecursor.
Parameters
----------
fdr_cutoff : float
A cutoff in fdr (between 0 and 1) to use for the alignment. Each
generated Multipeptide needs to have at least one member who is below
the cutoff.
verbose : bool
Whether to be verbose or not
verbosity : int
How verbose to be
"""
union_transition_groups = []
union_proteins = []
union_target_transition_groups = []
for i,r in enumerate(self.runs):
if verbose or verbosity >= 10:
stdout.write("\rParsing run %s out of %s" % (i+1, len(self.runs) ))
stdout.flush()
union_target_transition_groups.append( [peak.peptide.precursor_group.getPeptideGroupLabel() for peak in r.get_best_peaks_with_cutoff(fdr_cutoff) if not peak.peptide.get_decoy()] )
union_transition_groups.append( [peak.peptide.precursor_group.getPeptideGroupLabel() for peak in r.get_best_peaks_with_cutoff(fdr_cutoff)] )
union_proteins.append( list(set([peak.peptide.protein_name for peak in r.get_best_peaks_with_cutoff(fdr_cutoff) if not peak.peptide.get_decoy()])) )
if verbose or verbosity >= 10: stdout.write("\r\r\n") # clean up
union_target_transition_groups_set = set(union_target_transition_groups[0])
self.union_transition_groups_set = set(union_transition_groups[0])
self.union_proteins_set = set(union_proteins[0])
for groups in union_transition_groups:
self.union_transition_groups_set = self.union_transition_groups_set.union( groups )
for groups in union_target_transition_groups:
union_target_transition_groups_set = union_target_transition_groups_set.union( groups )
for proteins in union_proteins:
self.union_proteins_set = self.union_proteins_set.union( proteins )
all_prec = sum([len(s) for s in union_transition_groups])
target_prec = sum([len(s) for s in union_target_transition_groups])
if verbose or verbosity >= 1:
print "==================================="
print "Finished parsing, number of precursors and peptides per run"
print "All precursors", [len(s) for s in union_transition_groups], "(union of all runs %s)" % len(self.union_transition_groups_set)
print "All target precursors", [len(s) for s in union_target_transition_groups], "(union of all runs %s)" % len(union_target_transition_groups_set)
print "All target proteins", [len(s) for s in union_proteins], "(union of all runs %s)" % len(self.union_proteins_set)
if all_prec > 0:
print "Decoy percentage on precursor level %0.4f%%" % ( (all_prec - target_prec) * 100.0 / all_prec )
self.initial_fdr_cutoff = fdr_cutoff
if all_prec > 0 and all_prec - target_prec != 0:
self.estimated_decoy_pcnt = (all_prec - target_prec) * 100.0 / all_prec
else:
self.estimated_decoy_pcnt = None
multipeptides = []
for peptide_id in self.union_transition_groups_set:
m = Multipeptide()
for r in self.runs:
precursor_group = r.getPrecursorGroup(peptide_id)
m.insert(r.get_id(), precursor_group)
m.set_nr_runs(len(self.runs))
multipeptides.append(m)
return multipeptides
def get_all_multipeptides(self, fdr_cutoff, verbose=False, verbosity=0):
"""Match all precursors in different runs to each other.
Find all precursors that are above the fdr cutoff in each run and build
a union of those precursors. Then search for each of those precursors
in all the other runs and build a multipeptide / multiprecursor.
Parameters
----------
fdr_cutoff : float
A cutoff in fdr (between 0 and 1) to use for the alignment. Each
generated Multipeptide needs to have at least one member who is below
the cutoff.
verbose : bool
Whether to be verbose or not
verbosity : int
How verbose to be
"""
# Identify across all runs which transition groups are above the cutoff
union_transition_groups = []
union_target_transition_groups = []
union_proteins = []
self.union_transition_groups_set = set([])
self.union_proteins_set = set([])
self.union_target_transition_groups_set = set()
for i, r in enumerate(self.runs):
gr = []
gr_target = []
gr_protein = []
for precursor_group in r:
for peptide_precursor in precursor_group:
if (peptide_precursor.get_best_peakgroup().get_fdr_score()
< fdr_cutoff):
gr.append(precursor_group.getPeptideGroupLabel())
if not precursor_group.get_decoy():
gr_target.append(
precursor_group.getPeptideGroupLabel())
gr_protein.append(
peptide_precursor.getProteinName())
union_transition_groups.append(gr)
union_target_transition_groups.append(gr_target)
union_proteins.append(list(set(gr_protein)))
self.union_target_transition_groups_set = self.union_target_transition_groups_set.union(
gr_target)
self.union_transition_groups_set = self.union_transition_groups_set.union(
gr)
self.union_proteins_set = self.union_proteins_set.union(gr_protein)
if verbose or verbosity >= 10:
stdout.write("\r\r\n") # clean up
all_prec = sum([len(s) for s in union_transition_groups])
target_prec = sum([len(s) for s in union_target_transition_groups])
if verbose or verbosity >= 1:
print("===================================")
print(
"Finished parsing, number of precursors and peptides per run")
print(
"All precursors", [len(s) for s in union_transition_groups],
"(union of all runs %s)" %
len(self.union_transition_groups_set))
print(
"All target precursors",
[len(s) for s in union_target_transition_groups],
"(union of all runs %s)" %
len(self.union_target_transition_groups_set))
print("All target proteins", [len(s) for s in union_proteins],
"(union of all runs %s)" % len(self.union_proteins_set))
if all_prec > 0:
print("Decoy percentage on precursor level %0.4f%%" %
((all_prec - target_prec) * 100.0 / all_prec))
self.initial_fdr_cutoff = fdr_cutoff
if all_prec > 0 and all_prec - target_prec != 0:
self.estimated_decoy_pcnt = (all_prec -
target_prec) * 100.0 / all_prec
else:
self.estimated_decoy_pcnt = None
multipeptides = []
for peptide_id in self.union_transition_groups_set:
m = Multipeptide()
for r in self.runs:
precursor_group = r.getPrecursorGroup(peptide_id)
m.insert(r.get_id(), precursor_group)
m.set_nr_runs(len(self.runs))
multipeptides.append(m)
# Return sorted multipeptides for consistency across all Python versions
return (sorted(multipeptides, key=lambda x: str(x)))
def testNrRuns(self):
m = Multipeptide()
m.set_nr_runs(42)
self.assertEqual(m.get_nr_runs(), 42)
def get_all_multipeptides(self, fdr_cutoff, verbose=False, verbosity=0):
"""Match all precursors in different runs to each other.
Find all precursors that are above the fdr cutoff in each run and build
a union of those precursors. Then search for each of those precursors
in all the other runs and build a multipeptide / multiprecursor.
Parameters
----------
fdr_cutoff : float
A cutoff in fdr (between 0 and 1) to use for the alignment. Each
generated Multipeptide needs to have at least one member who is below
the cutoff.
verbose : bool
Whether to be verbose or not
verbosity : int
How verbose to be
"""
union_transition_groups = []
union_proteins = []
union_target_transition_groups = []
for i, r in enumerate(self.runs):
if verbose or verbosity >= 10:
stdout.write("\rParsing run %s out of %s" %
(i + 1, len(self.runs)))
stdout.flush()
union_target_transition_groups.append([
peak.peptide.precursor_group.getPeptideGroupLabel()
for peak in r.get_best_peaks_with_cutoff(fdr_cutoff)
if not peak.peptide.get_decoy()
])
union_transition_groups.append([
peak.peptide.precursor_group.getPeptideGroupLabel()
for peak in r.get_best_peaks_with_cutoff(fdr_cutoff)
])
union_proteins.append(
list(
set([
peak.peptide.protein_name
for peak in r.get_best_peaks_with_cutoff(fdr_cutoff)
if not peak.peptide.get_decoy()
])))
if verbose or verbosity >= 10: stdout.write("\r\r\n") # clean up
union_target_transition_groups_set = set(
union_target_transition_groups[0])
self.union_transition_groups_set = set(union_transition_groups[0])
self.union_proteins_set = set(union_proteins[0])
for groups in union_transition_groups:
self.union_transition_groups_set = self.union_transition_groups_set.union(
groups)
for groups in union_target_transition_groups:
union_target_transition_groups_set = union_target_transition_groups_set.union(
groups)
for proteins in union_proteins:
self.union_proteins_set = self.union_proteins_set.union(proteins)
all_prec = sum([len(s) for s in union_transition_groups])
target_prec = sum([len(s) for s in union_target_transition_groups])
if verbose or verbosity >= 1:
print "==================================="
print "Finished parsing, number of precursors and peptides per run"
print "All precursors", [
len(s) for s in union_transition_groups
], "(union of all runs %s)" % len(self.union_transition_groups_set)
print "All target precursors", [
len(s) for s in union_target_transition_groups
], "(union of all runs %s)" % len(
union_target_transition_groups_set)
print "All target proteins", [
len(s) for s in union_proteins
], "(union of all runs %s)" % len(self.union_proteins_set)
if all_prec > 0:
print "Decoy percentage on precursor level %0.4f%%" % (
(all_prec - target_prec) * 100.0 / all_prec)
self.initial_fdr_cutoff = fdr_cutoff
if all_prec > 0 and all_prec - target_prec != 0:
self.estimated_decoy_pcnt = (all_prec -
target_prec) * 100.0 / all_prec
else:
self.estimated_decoy_pcnt = None
multipeptides = []
for peptide_id in self.union_transition_groups_set:
m = Multipeptide()
for r in self.runs:
precursor_group = r.getPrecursorGroup(peptide_id)
m.insert(r.get_id(), precursor_group)
m.set_nr_runs(len(self.runs))
multipeptides.append(m)
return multipeptides
def get_all_multipeptides(self, fdr_cutoff, verbose=False, verbosity=0):
"""Match all precursors in different runs to each other.
Find all precursors that are above the fdr cutoff in each run and build
a union of those precursors. Then search for each of those precursors
in all the other runs and build a multipeptide / multiprecursor.
Parameters
----------
fdr_cutoff : float
A cutoff in fdr (between 0 and 1) to use for the alignment. Each
generated Multipeptide needs to have at least one member who is below
the cutoff.
verbose : bool
Whether to be verbose or not
verbosity : int
How verbose to be
"""
# Identify across all runs which transition groups are above the cutoff
union_transition_groups = []
union_target_transition_groups = []
union_proteins = []
self.union_transition_groups_set = set([])
self.union_proteins_set = set([])
self.union_target_transition_groups_set = set()
for i,r in enumerate(self.runs):
gr = []
gr_target = []
gr_protein = []
for precursor_group in r:
for peptide_precursor in precursor_group:
if (peptide_precursor.get_best_peakgroup().get_fdr_score() < fdr_cutoff):
gr.append( precursor_group.getPeptideGroupLabel() )
if not precursor_group.get_decoy():
gr_target.append(precursor_group.getPeptideGroupLabel())
gr_protein.append(peptide_precursor.getProteinName())
union_transition_groups.append(gr)
union_target_transition_groups.append(gr_target)
union_proteins.append(list(set(gr_protein)))
self.union_target_transition_groups_set = self.union_target_transition_groups_set.union(gr_target)
self.union_transition_groups_set = self.union_transition_groups_set.union(gr)
self.union_proteins_set = self.union_proteins_set.union(gr_protein)
if verbose or verbosity >= 10:
stdout.write("\r\r\n") # clean up
all_prec = sum([len(s) for s in union_transition_groups])
target_prec = sum([len(s) for s in union_target_transition_groups])
if verbose or verbosity >= 1:
print("===================================")
print("Finished parsing, number of precursors and peptides per run")
print("All precursors", [len(s) for s in union_transition_groups], "(union of all runs %s)" % len(self.union_transition_groups_set))
print("All target precursors", [len(s) for s in union_target_transition_groups], "(union of all runs %s)" % len(self.union_target_transition_groups_set))
print("All target proteins", [len(s) for s in union_proteins], "(union of all runs %s)" % len(self.union_proteins_set))
if all_prec > 0:
print("Decoy percentage on precursor level %0.4f%%" % ( (all_prec - target_prec) * 100.0 / all_prec ))
self.initial_fdr_cutoff = fdr_cutoff
if all_prec > 0 and all_prec - target_prec != 0:
self.estimated_decoy_pcnt = (all_prec - target_prec) * 100.0 / all_prec
else:
self.estimated_decoy_pcnt = None
multipeptides = []
for peptide_id in self.union_transition_groups_set:
m = Multipeptide()
for r in self.runs:
precursor_group = r.getPrecursorGroup(peptide_id)
m.insert(r.get_id(), precursor_group)
m.set_nr_runs(len(self.runs))
multipeptides.append(m)
# Return sorted multipeptides for consistency across all Python versions
return(sorted(multipeptides, key=lambda x: str(x)))