def reduce_activities(stmts_in, **kwargs):
"""Reduce the activity types in a list of statements
Parameters
----------
stmts_in : list[indra.statements.Statement]
A list of statements to reduce activity types in.
save : Optional[str]
The name of a pickle file to save the results (stmts_out) into.
Returns
-------
stmts_out : list[indra.statements.Statement]
A list of reduced activity statements.
"""
logger.info('Reducing activities on %d statements...' % len(stmts_in))
stmts_out = [deepcopy(st) for st in stmts_in]
ml = MechLinker(stmts_out)
ml.gather_explicit_activities()
ml.reduce_activities()
stmts_out = ml.statements
dump_pkl = kwargs.get('save')
if dump_pkl:
dump_statements(stmts_out, dump_pkl)
return stmts_out
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_require_active_forms_mod1():
af = ActiveForm(Agent('a', mods=[ModCondition('phosphorylation')]),
'activity', True)
ph = Phosphorylation(Agent('a'), Agent('b'))
ml = MechLinker([af, ph])
ml.gather_explicit_activities()
ml.require_active_forms()
assert len(ml.statements) == 2
assert ml.statements[1].enz.mods
def test_require_active_forms_act1():
af = ActiveForm(Agent('a', mods=[ModCondition('phosphorylation')]),
'activity', True)
act = Activation(Agent('a'), Agent('b'))
ml = MechLinker([af, act])
ml.gather_explicit_activities()
ml.require_active_forms()
assert (len(ml.statements) == 2)
assert (ml.statements[1].subj.mods)
def test_require_active_forms_mod1():
af = ActiveForm(Agent('a', mods=[ModCondition('phosphorylation')]),
'activity', True)
ph = Phosphorylation(Agent('a'), Agent('b'))
ml = MechLinker([af, ph])
ml.gather_explicit_activities()
ml.require_active_forms()
assert len(ml.statements) == 2
assert ml.statements[1].enz.mods
def test_require_active_forms_mod4():
mc1 = ModCondition('phosphorylation', 'T', '185')
mc2 = ModCondition('phosphorylation', 'Y', '187')
af = ActiveForm(Agent('a', mods=[mc1, mc2]), 'kinase', True)
ph = Phosphorylation(Agent('a', mods=[mc1]), Agent('b'))
ml = MechLinker([af, ph])
ml.gather_explicit_activities()
ml.require_active_forms()
assert len(ml.statements) == 2
assert len(ml.statements[1].enz.mods) == 2
def test_require_active_forms_mod2():
af = ActiveForm(Agent('a', mods=[ModCondition('phosphorylation')]),
'activity', True)
af2 = ActiveForm(Agent('a', location='nucleus'), 'activity', True)
ph = Phosphorylation(Agent('a'), Agent('b'))
ml = MechLinker([af, af2, ph])
ml.gather_explicit_activities()
ml.require_active_forms()
assert (len(ml.statements) == 4)
assert (ml.statements[3].enz.location)
def test_require_active_forms_mod4():
mc1 = ModCondition('phosphorylation', 'T', '185')
mc2 = ModCondition('phosphorylation', 'Y', '187')
af = ActiveForm(Agent('a', mods=[mc1, mc2]),
'kinase', True)
ph = Phosphorylation(Agent('a', mods=[mc1]), Agent('b'))
ml = MechLinker([af, ph])
ml.gather_explicit_activities()
ml.require_active_forms()
assert len(ml.statements) == 2
assert len(ml.statements[1].enz.mods) == 2
def test_reduce_activity_types():
a1 = Agent('a', location='cytoplasm')
a2 = Agent('a', location='nucleus')
af1 = ActiveForm(a1, 'activity', True)
af2 = ActiveForm(a2, 'kinase', True)
af3 = ActiveForm(a1, 'catalytic', True)
ml = MechLinker([af1, af2, af3])
ml.gather_explicit_activities()
ml.reduce_activities()
assert af1.activity == 'kinase'
assert af2.activity == 'kinase'
assert af3.activity == 'kinase'
def test_reduce_activity_types():
a1 = Agent('a', location='cytoplasm')
a2 = Agent('a', location='nucleus')
af1 = ActiveForm(a1, 'activity', True)
af2 = ActiveForm(a2, 'kinase', True)
af3 = ActiveForm(a1, 'catalytic',True)
ml = MechLinker([af1, af2, af3])
ml.gather_explicit_activities()
ml.reduce_activities()
assert af1.activity == 'kinase'
assert af2.activity == 'kinase'
assert af3.activity == 'kinase'
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 get_missing_activities(self):
ml = MechLinker(self.statements)
ml.gather_explicit_activities()
suggestions = []
for stmt in self.statements:
if isinstance(stmt,
(Modification, RegulateActivity, RegulateAmount)):
# The subj here is in an "active" position
subj, obj = stmt.agent_list()
if subj is None:
continue
subj_base = ml._get_base(subj)
# If it has any activities but isn't in an active state
# here
if subj_base.activity_types and not subj.activity:
# We suggest making the subj active in this case
suggestion = deepcopy(stmt)
act_type = subj_base.activity_types[0]
new_subj = deepcopy(subj)
new_subj.activity = ActivityCondition(act_type, True)
suggestion.set_agent_list([new_subj, obj])
suggestions.append(suggestion)
return suggestions
def get_missing_activities(self):
ml = MechLinker(self.statements)
ml.gather_explicit_activities()
suggestions = []
for stmt in self.statements:
if isinstance(stmt, (Modification, RegulateActivity,
RegulateAmount)):
# The subj here is in an "active" position
subj, obj = stmt.agent_list()
if subj is None:
continue
subj_base = ml._get_base(subj)
# If it has any activities but isn't in an active state
# here
if subj_base.activity_types and not subj.activity:
# We suggest making the subj active in this case
suggestion = deepcopy(stmt)
act_type = subj_base.activity_types[0]
new_subj = deepcopy(subj)
new_subj.activity = ActivityCondition(act_type, True)
suggestion.set_agent_list([new_subj, obj])
suggestions.append(suggestion)
return suggestions
def test_base_agent():
af = ActiveForm(Agent('a', mods=[ModCondition('phosphorylation')]),
'activity', True)
ml = MechLinker([af])
ml.gather_explicit_activities()
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)
def test_base_agent():
af = ActiveForm(Agent('a', mods=[ModCondition('phosphorylation')]),
'activity', True)
ml = MechLinker([af])
ml.gather_explicit_activities()