# -*- coding: utf-8 -*-
"""An implementation of chemical-based mechanism enrichment with NeuroMMSig [HoytDomingoFernandez2018]_.
This algorithm has multiple steps:
1. Select NeuroMMSig networks for AD, PD, and epilepsy
2. Select drugs from DrugBank, and their targets
3. Run NeuroMMSig algorithm on target list for each network and each mechanism
4. Store in database
.. [HoytDomingoFernandez2018] Charles Tapley Hoyt, Daniel Domingo-Fernández, Nora Balzer, Anka Güldenpfennig,
Martin Hofmann-Apitius; `A systematic approach for identifying shared mechanisms in epilepsy and its comorbidities
<https://doi.org/10.1093/database/bay050>`_, Database, Volume 2018, 1 January 2018, bay050
"""
import logging
import os
import itertools as itt
from tqdm import tqdm
from pybel.dsl import gene as gene_dsl
from pybel.struct.grouping import get_subgraphs_by_annotation
from pybel.struct.summary import get_annotation_values
from pybel_tools.analysis.neurommsig import get_neurommsig_score, neurommsig_graph_preprocessor
log = logging.getLogger(__name__)
def _get_drug_target_interactions():
"""Get a mapping from drugs to their list of gene.
:rtype: dict[str,list[str]]
"""
import bio2bel_drugbank
drugbank_manager = bio2bel_drugbank.Manager()
if not drugbank_manager.is_populated():
drugbank_manager.populate()
return drugbank_manager.get_drug_to_hgnc_symbols()
def _preprocess_dtis(dtis):
return {
drug: [gene_dsl(namespace='HGNC', name=target).as_tuple() for target in targets]
for drug, targets in dtis.items()
}
[docs]def epicom_on_graph(graph, dtis, preprocess=True):
"""
:param pybel.BELGraph graph:
:param dict[str,list[tuple]] dtis:
:param bool preprocess: If true, preprocess the graph with :func:`neurommsig_graph_preprocessor`.
:rtype: iter[tuple[str,str,float]]
"""
if preprocess:
log.info('preprocessing %s', graph)
graph = neurommsig_graph_preprocessor(graph)
log.info('stratifying %s', graph)
subgraphs = get_subgraphs_by_annotation(graph, annotation='Subgraph', sentinel='UNDEFINED')
log.info('running subgraphs x drugs for %s', graph)
it = itt.product(sorted(subgraphs), sorted(dtis))
it = tqdm(it, total=len(subgraphs) * len(dtis), desc='Calculating scores')
def get_score(s, d):
"""Gets the score
:param str s: name of the subgraph
:param str d: name of the drug
:rtype: Optional[float]
"""
return get_neurommsig_score(subgraphs[s], dtis[d])
for subgraph_name, drug in it:
score = get_score(subgraph_name, drug)
if score is None or score == 0.0:
continue
yield drug, subgraph_name, score
def _multi_run_helper(graphs):
"""
:param iter[pybel.BELGraph] graphs: A BEL Graph
:param dict[str,list[str]] dtis: A dictionary of drugs mapping to their targets
:rtype: iter[tuple[str,str,str,float]]
"""
dtis = _preprocess_dtis(_get_drug_target_interactions())
for graph in graphs:
for drug, subgraph_name, score in epicom_on_graph(graph, dtis):
yield graph.name, subgraph_name, drug, score
def _multi_run_helper_file_wrapper(graphs, file):
for row in _multi_run_helper(graphs):
print(*row, sep='\t', file=file)
[docs]def multi_run_epicom(graphs, path):
"""Run EpiCom analysis on many graphs
:param iter[pybel.BELGraph] graphs:
:param str or file path: output file path
"""
if isinstance(path, str):
with open(path, 'w') as file:
_multi_run_helper_file_wrapper(graphs, file)
else:
_multi_run_helper_file_wrapper(graphs, path)
[docs]def run_epicom(graph, directory):
"""
:param pybel.BELGraph graph: A BEL Graph
:param str directory: The directory in which the algorithm is run
:rtype: iter[tuple[str,str,str,float]]
"""
os.makedirs(directory, exist_ok=True)
dtis = _preprocess_dtis(_get_drug_target_interactions())
drugs = list(dtis)
subgraphs = get_annotation_values(graph, annotation='Subgraph')
subgraph_name_to_id = {name: i for i, name in enumerate(sorted(subgraphs))}
drug_name_to_id = {name: i for i, name in enumerate(sorted(drugs))}
with open(os.path.join(directory, 'subgraphs.tsv'), 'w') as file:
print('id', 'name', sep='\t', file=file)
for i, name in enumerate(sorted(subgraphs)):
print(i, name, sep='\t', file=file)
with open(os.path.join(directory, 'drugs.tsv'), 'w') as file:
print('id', 'name', sep='\t', file=file)
for i, name in enumerate(sorted(dtis)):
print(i, name, sep='\t', file=file)
with open(os.path.join(directory, 'scores.tsv'), 'w') as file:
print('subgraph', 'drug', 'score', sep='\t', file=file)
for drug, subgraph_name, score in epicom_on_graph(graph, dtis):
print(
subgraph_name_to_id[subgraph_name],
drug_name_to_id[drug],
score,
sep='\t',
file=file
)