from typing import List, Optional
from indra.sources import tas
from indra.statements import Agent
from indra.databases import hgnc_client
from . import SearchTerm, get_drugs_for_gene
from . prior_stmts import get_stmts_for_gene_list
from emmaa.statements import EmmaaStatement
from emmaa.model import EmmaaModel, save_config_to_s3
[docs]class GeneListPrior(object):
"""Class to manage the construction of a model from a list of genes.
Parameters
----------
gene_list : list[str]
A list of HGNC gene symbols
name : str
The name of the model (all lower case, no spaces or special characters)
human_readable_name : str
The human readable name (display name) of the model
"""
def __init__(self, gene_list, name, human_readable_name, description: Optional[str] = None):
self.name = name
self.gene_list = gene_list
self.human_readable_name = human_readable_name
self.stmts = []
self.search_terms = []
self.description = description
[docs] def make_search_terms(self, drug_gene_stmts=None):
"""Generate search terms from the gene list."""
if not drug_gene_stmts:
drug_gene_stmts = tas.process_from_web().statements
already_added = set()
terms = []
for gene in self.gene_list:
# Gene search term
agent = agent_from_gene_name(gene)
term = SearchTerm(type='gene', name=agent.name,
search_term=f'"{agent.name}"',
db_refs={'HGNC': agent.db_refs['HGNC'],
'UP': agent.db_refs['UP']})
terms.append(term)
# Drug search term
drug_terms = get_drugs_for_gene(drug_gene_stmts,
agent.db_refs['HGNC'])
for drug_term in drug_terms:
if drug_term.name not in already_added:
terms.append(drug_term)
already_added.add(drug_term.name)
self.search_terms = terms
return terms
[docs] def make_gene_statements(self) -> List[EmmaaStatement]:
"""Generate Statements from the gene list."""
if self.stmts:
return self.stmts
def is_internal(stmt):
# If all the agents are gene names, this is an internal statement.
# We classify any statements with drugs in them as external.
return all([a.name in self.gene_list
for a in stmt.real_agent_list()])
drug_names = [st.name for st in self.search_terms if
st.type == 'drug']
indra_stmts = get_stmts_for_gene_list(self.gene_list, drug_names)
estmts = [EmmaaStatement(stmt, datetime.datetime.now(), [],
{'internal': is_internal(stmt)})
for stmt in indra_stmts]
self.stmts = estmts
return self.stmts
[docs] def make_config(self):
"""Generate a configuration based on attributes."""
if not self.search_terms:
self.make_search_terms()
if not self.stmts:
self.make_gene_statements()
config = dict()
config['name'] = self.name
config['human_readable_name'] = self.human_readable_name
config['search_terms'] = [st.to_json() for st in self.search_terms]
config['assembly'] = {
'belief_cutoff': 0.8,
'filter_ungrounded': True
}
if self.description:
config['description'] = self.description
return config
[docs] def make_model(self):
"""Make an EmmaaModel and upload it along with the config to S3."""
config = self.make_config()
em = EmmaaModel(self.name, config)
em.stmts = self.stmts
ndex_uuid = em.upload_to_ndex()
config['ndex'] = {'network': ndex_uuid}
save_config_to_s3(self.name, config)
em.save_to_s3()
[docs]def agent_from_gene_name(gene_name):
"""Return an Agent based on a gene name."""
hgnc_id = hgnc_client.get_hgnc_id(gene_name)
up_id = hgnc_client.get_uniprot_id(hgnc_id)
agent = Agent(gene_name, db_refs={'HGNC': hgnc_id,
'UP': up_id})
return agent