def _make_famplex_lookup():
"""Create a famplex lookup dictionary.
Keys are sorted tuples of HGNC gene names and values are
the corresponding FamPlex ID.
"""
fplx_lookup = {}
bio_ontology.initialize()
for node in bio_ontology.nodes:
ns, id = bio_ontology.get_ns_id(node)
if ns == 'FPLX':
children = bio_ontology.get_children(ns, id)
hgnc_children = [
bio_ontology.get_name(*c) for c in children if c[0] == 'HGNC'
]
fplx_lookup[tuple(sorted(hgnc_children))] = id
return fplx_lookup
def _add_node(self, agent, uuid=None):
node_key = agent.name
node_id = self._existing_nodes.get(node_key)
# if the node already exists we do not want to add it again
# we must however add its uuid
if node_id is not None:
# fetch the appropriate node
n = [x for x in self._nodes if x['data']['id'] == node_id][0]
uuid_list = n['data']['uuid_list']
if uuid not in uuid_list:
uuid_list.append(uuid)
return node_id
db_refs = _get_db_refs(agent)
node_id = self._get_new_id()
self._existing_nodes[node_key] = node_id
node_name = agent.name
node_name = node_name.replace('_', ' ')
if 'FPLX' in db_refs:
expanded_families = bio_ontology.get_children(
*agent.get_grounding(), ns_filter={'HGNC'})
else:
expanded_families = []
members = {}
for member in expanded_families:
member_db_refs = {member[0]: member[1]}
member_db_refs = standardize_db_refs(member_db_refs)
gene_name = bio_ontology.get_name(*member)
members[gene_name] = {'db_refs': {}}
for dbns, dbid in member_db_refs.items():
url = get_identifiers_url(dbns, dbid)
if url:
members[gene_name]['db_refs'][dbns] = url
node = {
'data': {
'id': node_id,
'name': node_name,
'db_refs': db_refs,
'parent': '',
'members': members,
'uuid_list': [uuid]
}
}
self._nodes.append(node)
return node_id
def get_pain_mol():
PAIN_SIGNAL_MOL = {
"Prostaglandins": "CHEBI:26333",
"Brandykinin": "CHEBI:3165"
}
CHEBI_LIST = {}
CHEBI_NAMES = {}
for compounds, chebi_id in PAIN_SIGNAL_MOL.items():
CHEBI_LIST[compounds] = \
[children[1] for children in
bio_ontology.get_children('CHEBI',
chebi_id)]
CHEBI_NAMES[compounds] = \
[bio_ontology.get_name('CHEBI', ids)
for ids in CHEBI_LIST[compounds]]
return CHEBI_NAMES
def get_genes_for_family(family_agent):
"""Return agents corresponding to specific genes in a given family agent"""
from indra.ontology.bio import bio_ontology
from indra.ontology.standardize \
import standardize_agent_name
family_grounding = family_agent.db_refs.get('FPLX')
if not family_grounding:
return []
children = bio_ontology.get_children('FPLX', family_grounding)
children = [c for c in children if c[0] == 'HGNC']
child_agents = []
for _, hgnc_id in children:
child_agent = Agent(None,
db_refs={
'HGNC': hgnc_id,
'TYPE': 'ONT::GENE-PROTEIN'
})
standardize_agent_name(child_agent, standardize_refs=True)
child_agents.append(child_agent)
child_agents = sorted(child_agents, key=lambda x: x.name)
return child_agents
def set_style_expression_mutation(self, model, cell_line='A375_SKIN'):
"""Sets the fill color of each node based on its expression level
on the given cell line, and the stroke color based on whether it is
a mutation.
Parameters
----------
model: list<indra.statements.Statement>
A list of INDRA statements
cell_line: str
A cell line for which we're interested in protein expression level
"""
labels = self.label_to_glyph_ids.keys()
label_to_agent = {}
for label in labels:
for statement in model:
for agent in statement.agent_list():
if agent is not None and _n(agent.name) == label:
label_to_agent[label] = agent
agent_to_expression_level = {}
for agent in label_to_agent.values():
if 'HGNC' not in agent.db_refs and 'FPLX' not in agent.db_refs:
# This is not a gene
agent_to_expression_level[agent] = 0
continue
if 'FPLX' not in agent.db_refs:
gene_names = [agent.name]
else:
children = bio_ontology.get_children('FPLX',
agent.db_refs['FPLX'])
gene_names = [bio_ontology.get_name(*child) for child
in children]
# Compute mean expression level
expression_levels = []
logger.info('Getting expression status of proteins: %s' %
str(gene_names))
l = self.get_expression(gene_names, cell_line)
for line in l:
for element in l[line]:
level = l[line][element]
if level is not None:
expression_levels.append(l[line][element])
if len(expression_levels) == 0:
mean_level = None
else:
mean_level = sum(expression_levels) / len(expression_levels)
agent_to_expression_level[agent] = mean_level
# Create a normalized expression score between 0 and 1
# Compute min and maximum levels
min_level = None
max_level = None
for agent, level in agent_to_expression_level.items():
if level is None:
continue
if min_level is None:
min_level = level
if max_level is None:
max_level = level
if level < min_level:
min_level = level
if level > max_level:
max_level = level
# Compute scores
agent_to_score = {}
if max_level is not None:
level_span = max_level - min_level
for agent, level in agent_to_expression_level.items():
if level is None or level_span == 0:
agent_to_score[agent] = 0
else:
agent_to_score[agent] = (level - min_level) / level_span
# Map scores to colors and assign colors to labels
agent_to_color = {}
for agent, score in agent_to_score.items():
if 'HGNC' not in agent.db_refs and 'FPLX' not in agent.db_refs:
color = cm.Blues(0.3)
color_str = colors.to_hex(color[:3])
else:
# color = cm.plasma(score)
color = cm.Greens(0.6*score + 0.2)
color_str = colors.to_hex(color[:3])
assert(len(color_str) == 7)
stroke_color = \
self._choose_stroke_color_from_mutation_status(agent.name,
cell_line)
self.set_style(agent.name, stroke_color, color_str)
def test_mtorc_children():
ch1 = bio_ontology.get_children('FPLX', 'mTORC1')
ch2 = bio_ontology.get_children('FPLX', 'mTORC2')
assert ('HGNC', hgnc_client.get_hgnc_id('RICTOR')) not in ch1
assert ('HGNC', hgnc_client.get_hgnc_id('RPTOR')) not in ch2
import pickle
from collections import Counter
from emmaa.model_tests import StatementCheckingTest
from indra.ontology.bio import bio_ontology
from indra.statements.validate import print_validation_report
from indra.ontology.standardize import standardize_agent_name
CTD_CHEMICAL_DISEASE = '/Users/ben/data/ctd/ctd_chemical_disease.pkl'
pain = ('MESH', 'D010146')
pain_and_children = [pain] + bio_ontology.get_children(*pain)
def filter_objects(stmts, object_groundings):
print('Filtering %d statements' % len(stmts))
filtered_stmts = []
for stmt in stmts:
if set(stmt.obj.db_refs.items()) & set(object_groundings):
filtered_stmts.append(stmt)
print('Filtered to %d statements' % len(filtered_stmts))
return filtered_stmts
def get_mappings():
import gilda
mappings = set()
for stmt in stmts:
subj = stmt.subj
if 'CHEBI' in subj.db_refs:
continue
matches = gilda.ground(subj.name)
def expand_with_child_go_terms(terms):
all_terms = set(terms)
for term in terms:
child_terms = bio_ontology.get_children('GO', term)
all_terms |= {c[1] for c in child_terms}
return all_terms
for compound, names in PAIN_MOL_NAMES.items()
if rows[2] in names]
df = pd.DataFrame(celltype_pain_interaction)
return df
if __name__ == "__main__":
df = pd.read_csv(PC_SIF_URL, sep='\t', header=None)
df = df[df[1] == 'controls-production-of']
pain_signal_mol = {
"Prostaglandins": "CHEBI:26333",
"Brandykinin": "CHEBI:3165"
}
chebi_list = {}
for compounds, chebi_id in pain_signal_mol.items():
chebi_list[compounds] = [
children[1]
for children in bio_ontology.get_children('CHEBI', chebi_id)
]
df = df[df[2].isin(chebi_list)]
chebi_stmts = [{
'Enzyme': row[0],
'Statement': row[1],
'CHEBI_ID': row[2],
'CHEBI_Name': bio_ontology.get_name('CHEBI', row[2])
} for _, row in df.iterrows()]
df = pd.DataFrame(chebi_stmts)
df.to_csv("enzyme_interactions.tsv", sep="\t", header=True, index=False)
