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Midnighter/Pipfile

Created September 2, 2018 22:00
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Perform a single gene deletion study using COBRApy and record the flux distributions.
Raw
Pipfile
[[source]]
url = "https://pypi.org/simple"
verify_ssl = true
name = "pypi"
[packages]
cobra = "*"
python-libsbml = "*"
optlang = "*"
pandas = "*"
tqdm = "*"
[dev-packages]
Raw
Pipfile.lock
{
"_meta": {
"hash": {
"sha256": "198c2466447616b9f325ee547e789de0e6c8ad89622722ad752cb82609309110"
},
"pipfile-spec": 6,
"requires": {},
"sources": [
{
"name": "pypi",
"url": "https://pypi.org/simple",
"verify_ssl": true
}
]
},
"default": {
"cobra": {
"hashes": [
"sha256:17d0fbdafadabacd9ddc728dc6494839b84f01db3f319d9cfcd1a6486199de17",
"sha256:90f1d498543f615b53357269a2ae5fd6aa27daaf3de8cdcc6fbdac4ec48ad58c"
],
"index": "pypi",
"version": "==0.13.4"
},
"depinfo": {
"hashes": [
"sha256:1798f577a61249b04d672cec8d97143542ca24b524bf8abd2a22bc7d0598f678",
"sha256:33eb3f43d82e198bb5c6164472a108a66dc969aca930ccbab4480f469af3def8"
],
"markers": "python_version != '3.0.*' and python_version != '3.2.*' and python_version != '3.1.*' and python_version >= '2.7' and python_version != '3.3.*'",
"version": "==1.4.0"
},
"future": {
"hashes": [
"sha256:e39ced1ab767b5936646cedba8bcce582398233d6a627067d4c6a454c90cfedb"
],
"version": "==0.16.0"
},
"mpmath": {
"hashes": [
"sha256:04d14803b6875fe6d69e6dccea87d5ae5599802e4b1df7997bddd2024001050c"
],
"version": "==1.0.0"
},
"numpy": {
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"version": "==1.15.1"
},
"optlang": {
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],
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},
"pandas": {
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"version": "==0.23.4"
},
"pipdeptree": {
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}
},
"develop": {}
}
Raw
single_gene_deletion.py
# -*- coding: utf-8 -*-
# Copyright 2018 Novo Nordisk Foundation Center for Biosustainability,
# Technical University of Denmark.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Perform single deletion studies in parallel."""
from __future__ import absolute_import
import logging
import multiprocessing
import sys
from cobra.core import Solution
from cobra.exceptions import OptimizationError
from cobra.flux_analysis import moma, pfba, room
from cobra.io import read_sbml_model
from optlang.exceptions import SolverError
from pandas import Series
from tqdm import tqdm
LOGGER = logging.getLogger()
def _init_worker(model, reference):
global _model
global _reference
_model = model
_reference = reference
def _pfba_gene_deletion_study(model, gene):
with model:
model_gene = model.genes.get_by_id(gene)
model_gene.knock_out()
try:
sol = pfba(model)
except (OptimizationError, SolverError) as err:
sol = Solution(objective_value=None, status=str(err),
fluxes=Series([]))
return gene, sol
def _lmoma_gene_deletion_study(model, gene, reference):
with model:
model_gene = model.genes.get_by_id(gene)
model_gene.knock_out()
try:
sol = moma(model, solution=reference, linear=True)
except (OptimizationError, SolverError) as err:
sol = Solution(objective_value=None, status=str(err),
fluxes=Series([]))
return gene, sol
def _room_gene_deletion_study(model, gene, reference):
with model:
model_gene = model.genes.get_by_id(gene)
model_gene.knock_out()
try:
sol = room(model, solution=reference)
except (OptimizationError, SolverError) as err:
sol = Solution(objective_value=None, status=str(err),
fluxes=Series([]))
return gene, sol
def _pfba_worker(gene):
global _model
return _pfba_gene_deletion_study(_model, gene)
def _lmoma_worker(gene):
global _model
global _reference
return _lmoma_gene_deletion_study(_model, gene, _reference)
def _room_worker(gene):
global _model
global _reference
return _room_gene_deletion_study(_model, gene, _reference)
def single_gene_deletion_study(model, method, processes=1):
reference = pfba(model)
genes = [g.id for g in model.genes]
if len(genes) == 0:
LOGGER.info("Everything complete.")
return
if method == "linear moma":
worker = _lmoma_worker
elif method == "room":
worker = _room_worker
elif method == "pfba":
worker = _pfba_worker
else:
raise ValueError("Unknown method '{}'.".format(method))
# Compute the results potentially using multiprocessing.
LOGGER.info("Calculating knock-out predictions.")
if processes > 1:
LOGGER.debug("Using multiprocessing.")
chunk_size = max(len(genes) // processes, 1)
pool = multiprocessing.Pool(
processes, initializer=_init_worker, initargs=(model, reference)
)
results = pool.imap_unordered(worker, genes, chunksize=chunk_size)
pool.close()
else:
_init_worker(model, reference)
results = map(worker, genes)
# Iterate over the results and store the flux distributions.
for gene, sol in tqdm(results, total=len(genes),
desc="{} - Genes".format(method)):
if sol.status != "optimal":
LOGGER.warning(
"'%s' knock-out caused a non-optimal solver status: %s.",
gene, sol.status)
# TODO: Do something useful with the results here.
sol.fluxes.to_csv("{}.csv".format(gene), header=True)
if processes > 1:
pool.join()
def main(argv):
model = read_sbml_model(argv[0])
single_gene_deletion_study(model, argv[1], int(argv[2]))
if __name__ == "__main__":
logging.basicConfig(level="INFO",
format="[%(levelname)s] %(message)s")
if len(sys.argv) != 4:
LOGGER.critical("Usage:\n%s <model> <method> <processes>",
sys.argv[0])
sys.exit(2)
else:
sys.exit(main(sys.argv[1:]))
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