def test_shortestDistance_1(self):
rid = "0_0"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
dist_matrix = getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner)
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
rmap = dict([(r.get_id(),i) for i,r in enumerate(self.new_exp.runs) ])
border_l, border_r = integrationBorderShortestDistance(selected_pg,
rid, self.tr_data, dist_matrix, rmap)
# Direct transformation from 0_2 to 0_0
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_0").predict([ 240.0 ])[0])
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_0").predict([ 260.0 ])[0])
self.assertAlmostEqual(border_l, 77.992277992277934)
self.assertAlmostEqual(border_r, 84.1698841699)
def test_shortestDistance_2(self):
rid = "0_1"
spl_aligner = SplineAligner(self.initial_alignment_cutoff)
dist_matrix = getDistanceMatrix(self.new_exp, self.multipeptides, spl_aligner)
# Select peakgroups, compute left/right border
selected_pg = [pg for p in self.current_mpep1.getAllPeptides() for pg in p.get_all_peakgroups() if pg.get_cluster_id() == 1]
rmap = dict([(r.get_id(),i) for i,r in enumerate(self.new_exp.runs) ])
border_l, border_r = integrationBorderShortestDistance(selected_pg,
rid, self.tr_data, dist_matrix, rmap)
# Shortest distance means that we transformed directly from 0_2 to 0_1
self.assertAlmostEqual(border_l, self.tr_data.getTrafo("0_2", "0_1").predict([ 240.0 ])[0])
self.assertAlmostEqual(border_r, self.tr_data.getTrafo("0_2", "0_1").predict([ 260.0 ])[0])
self.assertAlmostEqual(border_l, 168.03088803088787)
self.assertAlmostEqual(border_r, 183.32046332)
def analyze_multipeptide_cluster(current_mpep, cnt, new_exp, swath_chromatograms,
transformation_collection_, border_option, selected_pg, cluster_id,
onlyExtractFromRun=None, tree=None, mat=None, is_test=False):
for rid in [r.get_id() for r in new_exp.runs]:
if VERBOSE: print "Shall work on run ", rid
cnt.peakgroups += 1
# Check whether we have a peakgroup for this run id, if yes we mark
# this peakgroup as selected for output, otherwise we try to impute.
if not any([pg.peptide.run.get_id() == rid for pg in selected_pg]):
# Skip if we should not extract from this run
if not onlyExtractFromRun is None:
if onlyExtractFromRun != rid:
continue
cnt.imputations += 1
# Select current run, compute right/left integration border and then integrate
current_run = [r for r in new_exp.runs if r.get_id() == rid][0]
rmap = dict([(r.get_id(),i) for i,r in enumerate(new_exp.runs) ])
if VERBOSE:
print "Will try to fill NA in run", current_run.get_id(), "for peptide", selected_pg[0].peptide.get_id()
print tree, mat
inferred_from_isotope = False
if current_mpep.hasPrecursorGroup(current_run.get_id()):
# If another precursor was already picked for this run,
# check if any peakgroups of the same cluster exist and if
# so, try to use the one with the minimal FDR score to
# infer the boundaries.
prgr = current_mpep.getPrecursorGroup(current_run.get_id())
pgs = [(pg_.get_fdr_score(), pg_) for prec_ in prgr for pg_ in prec_.peakgroups if pg_.get_cluster_id() == cluster_id]
if len(pgs) > 0:
# TODO if the boundaries were inferred (e.g. fdr score > 1.0), should we not use them?
best_pg = min(pgs)[1]
border_l = float(best_pg.get_value("leftWidth"))
border_r = float(best_pg.get_value("rightWidth"))
inferred_from_isotope = True
if VERBOSE:
print "Will try to infer from isotopically modified version of the same run!"
print "Precursor is", prgr
for prec in prgr:
print " --", prec
for pg in prec.peakgroups:
print " * ", pg
print "Min fdr pg", best_pg, " border %s / %s" % (border_l, border_r)
if inferred_from_isotope:
# All good
pass
elif tree is not None:
## Use the closest path approach
border_l, border_r = integrationBorderShortestPath(selected_pg,
rid, transformation_collection_, tree)
elif mat is not None:
## Use the closest overall run approach
border_l, border_r = integrationBorderShortestDistance(selected_pg,
rid, transformation_collection_, mat, rmap)
else:
## Use the refernce-based approach
border_l, border_r = integrationBorderReference(new_exp, selected_pg, rid, transformation_collection_, border_option)
newpg = integrate_chromatogram(selected_pg[0], current_run, swath_chromatograms,
border_l, border_r, cnt, is_test)
if newpg != "NA":
if VERBOSE:
print "Managed to fill NA in run", current_run.get_id(), \
"with value", newpg.get_value("Intensity"), "/ borders", border_l, border_r #, "for cluster", newpg.get_value("align_clusterid")
cnt.imputation_succ += 1
transition_group_id = newpg.get_value("transition_group_id")
try:
peptide_label = newpg.get_value("peptide_group_label")
except KeyError:
peptide_label = transition_group_id
# Create new precursor
precursor = GeneralPrecursor(transition_group_id, current_run)
newpg.setClusterID(cluster_id)
newpg.peptide = precursor
precursor.add_peakgroup(newpg)
precursor.sequence = selected_pg[0].peptide.sequence
precursor.protein_name = selected_pg[0].peptide.protein_name
if current_mpep.hasPrecursorGroup(current_run.get_id()):
prec_group = current_mpep.getPrecursorGroup(current_run.get_id())
if prec_group.getPrecursor(transition_group_id) is None:
# No precursors exists yet for this transition_group_id - this
# means that we have a new run for which a precursor group already
# exists (e.g. we have found a light version already) and are now
# dealing with the heavy ...
prec_group.addPrecursor(precursor)
else:
# Likely, another peakgroup from the same run and peptide was
# picked already but not from the same cluster
prec_group.getPrecursor(transition_group_id).add_peakgroup(newpg)
else:
# Create new precursor group and insert
precursor_group = PrecursorGroup(peptide_label, current_run)
precursor_group.addPrecursor(precursor)
current_mpep.insert(rid, precursor_group)
def analyze_multipeptide_cluster(current_mpep, cnt, new_exp, swath_chromatograms,
transformation_collection_, border_option, selected_pg, cluster_id,
onlyExtractFromRun=None, tree=None, mat=None, is_test=False):
for rid in [r.get_id() for r in new_exp.runs]:
if VERBOSE: print "Shall work on run ", rid
cnt.peakgroups += 1
# Check whether we have a peakgroup for this run id, if yes we mark
# this peakgroup as selected for output, otherwise we try to impute.
if not any([pg.peptide.run.get_id() == rid for pg in selected_pg]):
# Skip if we should not extract from this run
if not onlyExtractFromRun is None:
if onlyExtractFromRun != rid:
continue
cnt.imputations += 1
# Select current run, compute right/left integration border and then integrate
current_run = [r for r in new_exp.runs if r.get_id() == rid][0]
rmap = dict([(r.get_id(),i) for i,r in enumerate(new_exp.runs) ])
if VERBOSE:
print "Will try to fill NA in run", current_run.get_id(), "for peptide", selected_pg[0].peptide.get_id()
print tree, mat
inferred_from_isotope = False
if current_mpep.hasPrecursorGroup(current_run.get_id()):
# If another precursor was already picked for this run,
# check if any peakgroups of the same cluster exist and if
# so, try to use the one with the minimal FDR score to
# infer the boundaries.
prgr = current_mpep.getPrecursorGroup(current_run.get_id())
pgs = [(pg_.get_fdr_score(), pg_) for prec_ in prgr for pg_ in prec_.peakgroups if pg_.get_cluster_id() == cluster_id]
if len(pgs) > 0:
# TODO if the boundaries were inferred (e.g. fdr score > 1.0), should we not use them?
best_pg = min(pgs)[1]
border_l = float(best_pg.get_value("leftWidth"))
border_r = float(best_pg.get_value("rightWidth"))
inferred_from_isotope = True
if VERBOSE:
print "Will try to infer from isotopically modified version of the same run!"
print "Precursor is", prgr
for prec in prgr:
print " --", prec
for pg in prec.peakgroups:
print " * ", pg
print "Min fdr pg", best_pg, " border %s / %s" % (border_l, border_r)
if inferred_from_isotope:
# All good
pass
elif tree is not None:
## Use the closest path approach
border_l, border_r = integrationBorderShortestPath(selected_pg,
rid, transformation_collection_, tree)
elif mat is not None:
## Use the closest overall run approach
border_l, border_r = integrationBorderShortestDistance(selected_pg,
rid, transformation_collection_, mat, rmap)
else:
## Use the refernce-based approach
border_l, border_r = integrationBorderReference(new_exp, selected_pg, rid, transformation_collection_, border_option)
newpg = integrate_chromatogram(selected_pg[0], current_run, swath_chromatograms,
border_l, border_r, cnt, is_test)
if newpg != "NA":
if VERBOSE:
print "Managed to fill NA in run", current_run.get_id(), \
"with value", newpg.get_value("Intensity"), "/ borders", border_l, border_r #, "for cluster", newpg.get_value("align_clusterid")
cnt.imputation_succ += 1
transition_group_id = newpg.get_value("transition_group_id")
try:
peptide_label = newpg.get_value("peptide_group_label")
except KeyError:
peptide_label = transition_group_id
# Create new precursor
precursor = GeneralPrecursor(transition_group_id, current_run)
newpg.setClusterID(cluster_id)
newpg.peptide = precursor
precursor.add_peakgroup(newpg)
precursor.sequence = selected_pg[0].peptide.sequence
precursor.protein_name = selected_pg[0].peptide.protein_name
if current_mpep.hasPrecursorGroup(current_run.get_id()):
prec_group = current_mpep.getPrecursorGroup(current_run.get_id())
if prec_group.getPrecursor(transition_group_id) is None:
# No precursors exists yet for this transition_group_id - this
# means that we have a new run for which a precursor group already
# exists (e.g. we have found a light version already) and are now
# dealing with the heavy ...
prec_group.addPrecursor(precursor)
else:
# Likely, another peakgroup from the same run and peptide was
# picked already but not from the same cluster
prec_group.getPrecursor(transition_group_id).add_peakgroup(newpg)
else:
# Create new precursor group and insert
precursor_group = PrecursorGroup(peptide_label, current_run)
precursor_group.addPrecursor(precursor)
current_mpep.insert(rid, precursor_group)