#!/usr/bin/env python
# -*- coding: utf-8 -*-
#
# This file is part of the `pypath` python module
#
# Copyright 2014-2023
# EMBL, EMBL-EBI, Uniklinik RWTH Aachen, Heidelberg University
#
# Authors: see the file `README.rst`
# Contact: Dénes Türei (turei.denes@gmail.com)
#
# Distributed under the GPLv3 License.
# See accompanying file LICENSE.txt or copy at
# https://www.gnu.org/licenses/gpl-3.0.html
#
# Website: https://pypath.omnipathdb.org/
#
from typing import List, Literal, Union
from numbers import Number
import re
import collections
import pypath.resources.urls as urls
import pypath.share.curl as curl
[docs]
def stitch_actions_interactions(threshold: Number = None) -> List[tuple]:
StitchActionsInteraction = collections.namedtuple(
'StitchActionsInteraction',
(
'partner_a',
'partner_b',
'mechanism',
'action',
'score',
),
)
url = urls.urls['stitch']['actions']
c = curl.Curl(url, silent = False, large = True, slow = True)
_ = next(c.result)
sep = re.compile(r'[sm\.]')
for l in c.result:
if hasattr(l, 'decode'):
l = l.decode('utf-8')
l = l.strip().split('\t')
score = int(l[5])
if threshold is not None and score < threshold:
continue
a = sep.split(l[0])[1]
b = sep.split(l[1])[1]
if l[4] == 'f':
a, b = b, a
yield StitchActionsInteraction(
partner_a = a,
partner_b = b,
mechanism = l[2],
action = l[3] or None,
score = int(l[5]),
)
[docs]
def stitch_links_interactions(
ncbi_tax_id: int = 9606,
score_threshold: Union[
Number,
Literal[
'highest_confidence',
'high_confidence',
'medium_confidence',
'low_confidence',
]
] = 'highest_confidence',
physical_interaction_score: bool = True,
) -> List[tuple]:
"""
Downloads chemical-protein links (with detailed subscores).
The combined physical interaction score is defined
between the interactors for which we have evidence of their binding.
Args
score_threshold: Minimum required interaction score. user can use
pre-defined confidence limits or can define a custom value.
"""
confidence = {
'highest_confidence': 900,
'high_confidence': 700,
'medium_confidence': 400,
'low_confidence': .150,
}
min_score = confidence.get(score_threshold, score_threshold)
StitchLinksInteraction = collections.namedtuple(
'StitchLinksInteraction',
(
'partner_a',
'partner_b',
'experimental',
'prediction',
'database',
'textmining',
'combined_score',
'physical_combined_score',
),
)
if physical_interaction_score:
phy_links = dict(
(
(s.partner_a, s.partner_b),
s.score
)
for s in stitch_actions_interactions()
if s.mechanism == 'binding'
)
url = urls.urls['stitch']['links'] % ncbi_tax_id
c = curl.Curl(url, silent = False, large = True)
_ = next(c.result)
sep = re.compile(r'[sm\.]')
for l in c.result:
if hasattr(l, 'decode'):
l = l.decode('utf-8')
l = l.strip().split('\t')
if int(l[6]) < min_score:
continue
a = sep.split(l[0])[1]
b = sep.split(l[1])[1]
phy_score = (
phy_links.get((a,b), phy_links.get((b, a), None))
if physical_interaction_score else
None
)
yield StitchLinksInteraction(
partner_a = a,
partner_b = b,
experimental = int(l[2]),
prediction = int(l[3]),
database = int(l[4]),
textmining = int(l[5]),
combined_score = int(l[6]),
physical_combined_score = phy_score,
)
