def run_assembly(self):
"""Run INDRA's assembly pipeline on the Statements."""
self.eliminate_copies()
stmts = self.get_indra_stmts()
stmts = self.filter_event_association(stmts)
stmts = ac.filter_no_hypothesis(stmts)
if not self.assembly_config.get('skip_map_grounding'):
stmts = ac.map_grounding(stmts)
if self.assembly_config.get('standardize_names'):
ac.standardize_names_groundings(stmts)
if self.assembly_config.get('filter_ungrounded'):
score_threshold = self.assembly_config.get('score_threshold')
stmts = ac.filter_grounded_only(stmts,
score_threshold=score_threshold)
if self.assembly_config.get('merge_groundings'):
stmts = ac.merge_groundings(stmts)
if self.assembly_config.get('merge_deltas'):
stmts = ac.merge_deltas(stmts)
relevance_policy = self.assembly_config.get('filter_relevance')
if relevance_policy:
stmts = self.filter_relevance(stmts, relevance_policy)
if not self.assembly_config.get('skip_filter_human'):
stmts = ac.filter_human_only(stmts)
if not self.assembly_config.get('skip_map_sequence'):
stmts = ac.map_sequence(stmts)
# Use WM hierarchies and belief scorer for WM preassembly
preassembly_mode = self.assembly_config.get('preassembly_mode')
if preassembly_mode == 'wm':
hierarchies = get_wm_hierarchies()
belief_scorer = get_eidos_scorer()
stmts = ac.run_preassembly(stmts,
return_toplevel=False,
belief_scorer=belief_scorer,
hierarchies=hierarchies)
else:
stmts = ac.run_preassembly(stmts, return_toplevel=False)
belief_cutoff = self.assembly_config.get('belief_cutoff')
if belief_cutoff is not None:
stmts = ac.filter_belief(stmts, belief_cutoff)
stmts = ac.filter_top_level(stmts)
if self.assembly_config.get('filter_direct'):
stmts = ac.filter_direct(stmts)
stmts = ac.filter_enzyme_kinase(stmts)
stmts = ac.filter_mod_nokinase(stmts)
stmts = ac.filter_transcription_factor(stmts)
if self.assembly_config.get('mechanism_linking'):
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.reduce_activities()
ml.gather_modifications()
ml.reduce_modifications()
ml.gather_explicit_activities()
ml.replace_activations()
ml.require_active_forms()
stmts = ml.statements
self.assembled_stmts = stmts
def test_reduce_mods1():
phos1 = Phosphorylation(Agent('b'), Agent('a'))
phos2 = Phosphorylation(Agent('c'), Agent('a'), 'T')
phos3 = Phosphorylation(Agent('d'), Agent('a'), 'T', '143')
ml = MechLinker([phos1, phos2, phos3])
ml.gather_modifications()
ml.reduce_modifications()
assert len(ml.statements) == 3
for st in ml.statements:
assert st.residue == 'T'
assert st.position == '143'
def test_reduce_mods1():
phos1 = Phosphorylation(Agent('b'), Agent('a'))
phos2 = Phosphorylation(Agent('c'), Agent('a'), 'T')
phos3 = Phosphorylation(Agent('d'), Agent('a'), 'T', '143')
ml = MechLinker([phos1, phos2, phos3])
ml.gather_modifications()
ml.reduce_modifications()
assert len(ml.statements) == 3
for st in ml.statements:
assert st.residue == 'T'
assert st.position == '143'
def test_reduce_mods2():
mc1 = ModCondition('phosphorylation', 'S', '123', False)
mc2 = ModCondition('phosphorylation', 'S', None, True)
mc3 = ModCondition('phosphorylation', 'T')
mc4 = ModCondition('phosphorylation', 'T', '111')
mc5 = ModCondition('phosphorylation', 'T', '999')
mc6 = ModCondition('phosphorylation')
mc7 = ModCondition('phosphorylation', None, '999')
st1 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc1]))
st2 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc2]))
st3 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc3]))
st4 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc4]))
st5 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc5]))
st6 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc6]))
st7 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc7]))
ml = MechLinker([st1, st2, st3, st4, st5, st6, st7])
ml.gather_modifications()
ml.reduce_modifications()
assert len(ml.statements) == 7
mc_red1 = ml.statements[0].obj.mods[0]
mc_red2 = ml.statements[1].obj.mods[0]
mc_red3 = ml.statements[2].obj.mods[0]
mc_red4 = ml.statements[3].obj.mods[0]
mc_red5 = ml.statements[4].obj.mods[0]
mc_red6 = ml.statements[5].obj.mods[0]
mc_red7 = ml.statements[6].obj.mods[0]
# These ones stay the same because they shouldn't be reduced
assert mc_red1.__dict__ == mc1.__dict__
assert mc_red3.__dict__ == mc3.__dict__
assert mc_red4.__dict__ == mc4.__dict__
assert mc_red5.__dict__ == mc5.__dict__
assert mc_red6.__dict__ == mc6.__dict__
# mc2 has to be reduced to have position '123'
assert mc_red2.mod_type == 'phosphorylation'
assert mc_red2.residue == 'S'
assert mc_red2.position == '123'
assert mc_red2.is_modified == True
# mc7 has to be reduced to have residue 'T'
assert mc_red7.mod_type == 'phosphorylation'
assert mc_red7.residue == 'T'
assert mc_red7.position == '999'
assert mc_red7.is_modified == True
def test_reduce_mods2():
mc1 = ModCondition('phosphorylation', 'S', '123', False)
mc2 = ModCondition('phosphorylation', 'S', None, True)
mc3 = ModCondition('phosphorylation', 'T')
mc4 = ModCondition('phosphorylation', 'T', '111')
mc5 = ModCondition('phosphorylation', 'T', '999')
mc6 = ModCondition('phosphorylation')
mc7 = ModCondition('phosphorylation', None, '999')
st1 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc1]))
st2 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc2]))
st3 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc3]))
st4 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc4]))
st5 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc5]))
st6 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc6]))
st7 = Activation(Agent('KRAS'), Agent('BRAF', mods=[mc7]))
ml = MechLinker([st1, st2, st3, st4, st5, st6, st7])
ml.gather_modifications()
ml.reduce_modifications()
assert len(ml.statements) == 7
mc_red1 = ml.statements[0].obj.mods[0]
mc_red2 = ml.statements[1].obj.mods[0]
mc_red3 = ml.statements[2].obj.mods[0]
mc_red4 = ml.statements[3].obj.mods[0]
mc_red5 = ml.statements[4].obj.mods[0]
mc_red6 = ml.statements[5].obj.mods[0]
mc_red7 = ml.statements[6].obj.mods[0]
# These ones stay the same because they shouldn't be reduced
assert mc_red1.__dict__ == mc1.__dict__
assert mc_red3.__dict__ == mc3.__dict__
assert mc_red4.__dict__ == mc4.__dict__
assert mc_red5.__dict__ == mc5.__dict__
assert mc_red6.__dict__ == mc6.__dict__
# mc2 has to be reduced to have position '123'
assert mc_red2.mod_type == 'phosphorylation'
assert mc_red2.residue == 'S'
assert mc_red2.position == '123'
assert mc_red2.is_modified == True
# mc7 has to be reduced to have residue 'T'
assert mc_red7.mod_type == 'phosphorylation'
assert mc_red7.residue == 'T'
assert mc_red7.position == '999'
assert mc_red7.is_modified == True
def preprocess_stmts(stmts, data_genes):
# Filter the INDRA Statements to be put into the model
stmts = ac.filter_mutation_status(stmts,
{'BRAF': [('V', '600', 'E')]}, ['PTEN'])
stmts = ac.filter_by_type(stmts, Complex, invert=True)
stmts = ac.filter_direct(stmts)
stmts = ac.filter_belief(stmts, 0.95)
stmts = ac.filter_top_level(stmts)
stmts = ac.filter_gene_list(stmts, data_genes, 'all')
stmts = ac.filter_enzyme_kinase(stmts)
stmts = ac.filter_mod_nokinase(stmts)
stmts = ac.filter_transcription_factor(stmts)
# Simplify activity types
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.reduce_activities()
ml.gather_modifications()
ml.reduce_modifications()
af_stmts = ac.filter_by_type(ml.statements, ActiveForm)
non_af_stmts = ac.filter_by_type(ml.statements, ActiveForm, invert=True)
af_stmts = ac.run_preassembly(af_stmts)
stmts = af_stmts + non_af_stmts
# Replace activations when possible
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.replace_activations()
# Require active forms
ml.require_active_forms()
num_stmts = len(ml.statements)
while True:
# Remove inconsequential PTMs
ml.statements = ac.filter_inconsequential_mods(ml.statements,
get_mod_whitelist())
ml.statements = ac.filter_inconsequential_acts(ml.statements,
get_mod_whitelist())
if num_stmts <= len(ml.statements):
break
num_stmts = len(ml.statements)
stmts = ml.statements
return stmts
def assemble_pysb(stmts, data_genes, contextualize=False):
# Filter the INDRA Statements to be put into the model
stmts = ac.filter_by_type(stmts, Complex, invert=True)
stmts = ac.filter_direct(stmts)
stmts = ac.filter_belief(stmts, 0.95)
stmts = ac.filter_top_level(stmts)
# Strip the extraneous supports/supported by here
strip_supports(stmts)
stmts = ac.filter_gene_list(stmts, data_genes, 'all')
stmts = ac.filter_enzyme_kinase(stmts)
stmts = ac.filter_mod_nokinase(stmts)
stmts = ac.filter_transcription_factor(stmts)
# Simplify activity types
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.reduce_activities()
ml.gather_modifications()
ml.reduce_modifications()
stmts = normalize_active_forms(ml.statements)
# Replace activations when possible
ml = MechLinker(stmts)
ml.gather_explicit_activities()
ml.replace_activations()
# Require active forms
ml.require_active_forms()
num_stmts = len(ml.statements)
while True:
# Remove inconsequential PTMs
ml.statements = ac.filter_inconsequential_mods(ml.statements,
get_mod_whitelist())
ml.statements = ac.filter_inconsequential_acts(ml.statements,
get_mod_whitelist())
if num_stmts <= len(ml.statements):
break
num_stmts = len(ml.statements)
stmts = ml.statements
# Save the Statements here
ac.dump_statements(stmts, prefixed_pkl('pysb_stmts'))
# Add drug target Statements
drug_target_stmts = get_drug_target_statements()
stmts += drug_target_stmts
# Just generate the generic model
pa = PysbAssembler()
pa.add_statements(stmts)
model = pa.make_model()
with open(prefixed_pkl('pysb_model'), 'wb') as f:
pickle.dump(model, f)
# Run this extra part only if contextualize is set to True
if not contextualize:
return
cell_lines_no_data = ['COLO858', 'K2', 'MMACSF', 'MZ7MEL', 'WM1552C']
for cell_line in cell_lines:
if cell_line not in cell_lines_no_data:
stmtsc = contextualize_stmts(stmts, cell_line, data_genes)
else:
stmtsc = stmts
pa = PysbAssembler()
pa.add_statements(stmtsc)
model = pa.make_model()
if cell_line not in cell_lines_no_data:
contextualize_model(model, cell_line, data_genes)
ac.dump_statements(stmtsc, prefixed_pkl('pysb_stmts_%s' % cell_line))
with open(prefixed_pkl('pysb_model_%s' % cell_line), 'wb') as f:
pickle.dump(model, f)