#!/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/
#
import collections
from typing import List, Literal, Union
import pypath.resources.urls as urls
import pypath.share.curl as curl
import pypath.share.settings as settings
import pypath.inputs.embopress as embo
import pypath.inputs.ca1 as ca1
import pypath.utils.mapping as mapping
KEY = {
'NA': None,
'Neucleus': 'Nucleus',
'Ribosomes': 'Ribosome',
'Vesicles': 'Vesicle',
'Endoplasmic reticulum': 'ER',
'Not available': None,
'Not Available': None,
'Mitochondrial': 'Mitochondrion',
'Mitochondria': 'Mitochondrion',
'Anti-Apoptic': 'Anti-apoptotic',
}
ca1_interactions = ca1.ca1_interactions
[docs]
def hsn_interactions(
source: Literal['rescued', 'researchgate'] = 'rescued',
) -> List[tuple]:
"""
Downloads and processes HumanSignalingNetwork version 6
(published 2014 Jan by Edwin Wang).
Args
source: The same file is available from two domains: the OmniPath
rescued repository and Research Gate. These both are secondary
sources, the dataset is not available any more from its original
site of publication, which was the old webpage of the Wang Lab.
Details:
This dataset is identical to the one returned by `wang_interactions`,
but it does not contain function and localization details.
"""
effects = {
'Pos': '+',
'Neg': '-',
'Phy': '0',
}
class HsnInteraction(
collections.namedtuple(
'HsnInteractionBase',
(
'genesymbol_source',
'genesymbol_target',
'entrez_source',
'entrez_target',
'effect',
),
),
):
def __new__(cls, *args):
args = args[0] if len(args) == 1 else args
identifiers = dict(zip(
(
'entrez_source',
'genesymbol_source',
'entrez_target',
'genesymbol_target',
),
args[:-1]
))
return super(HsnInteraction, cls).__new__(
cls,
**identifiers,
effect = effects.get(args[-1], args[-1]),
)
url = urls.urls['hsn'][source]
c = curl.Curl(
url,
silent = False,
large = True,
req_headers = [settings.get('user_agent')],
)
_ = next(c.result)
result = [HsnInteraction(*r.strip().split(',')) for r in c.result if r]
return result
[docs]
def wang_interactions() -> List[tuple]:
"""
Downloads and processes Wang Lab HumanSignalingNetwork.
Returns list of interactions as tuples of source, target and effect.
"""
url = urls.urls['wang']['rescued']
c = curl.Curl(url, silent = False)
data = c.result
data = data.split('\n')
return _wang_process(data)
[docs]
def cui_interactions() -> List[tuple]:
"""
Interactions from Supplementary Table 9 of Cui et al. 2007
(https://www.embopress.org/doi/full/10.1038/msb4100200).
"""
raw = embo.embopress_supplementary(
url = urls.urls['wang']['cui'],
init_url = urls.urls['wang']['cui_init'],
sheet = 'Supplementary Table 9',
)
return _wang_process(raw)
def _wang_process(raw: List[Union[List, str]]) -> List[tuple]:
Node = collections.namedtuple(
'Node',
(
'genesymbol',
'entrez',
'function',
'location',
),
)
WangInteraction = collections.namedtuple(
'WangInteraction',
(
'genesymbol_source',
'genesymbol_target',
'entrez_source',
'entrez_target',
'effect',
'function_source',
'location_source',
'function_target',
'location_target',
),
)
key = KEY.copy()
result = []
nodes = {}
reading_nodes = False
reading_edges = False
reading_key = False
_key = lambda y: (lambda x: key.get(x, x))(key.get(y, y))
for l in raw:
if not l or not l[0] or (hasattr(l, 'strip') and not l.strip()):
reading_key = False
reading_nodes = False
reading_edges = False
l = l.split(',') if hasattr(l, 'split') else l
if reading_key:
key[l[0]] = l[1]
elif reading_nodes:
nodes[l[0]] = Node(
genesymbol = l[1],
entrez = l[2].split('.')[0],
function = _key(l[3]),
location = _key(l[4]),
)
elif reading_edges:
src = nodes[l[0]]
tgt = nodes[l[1]]
result.append(
WangInteraction(
genesymbol_source = src.genesymbol,
genesymbol_target = tgt.genesymbol,
entrez_source = src.entrez,
entrez_target = tgt.entrez,
effect = l[2].replace('_', '-').split('.')[0],
function_source = src.function,
location_source = src.location,
function_target = tgt.function,
location_target = tgt.location,
)
)
if l[0].startswith('Node'):
reading_key = False
reading_nodes = True
if l[0].startswith('From'):
reading_key = False
reading_nodes = False
reading_edges = True
if l[0].startswith('Notes:'):
reading_key = True
return result
[docs]
def wang_annotations():
NA_VALUES = {None, 'NA', 'Not available'}
record = collections.namedtuple(
'WangAnnotation',
(
'function',
'location',
),
)
result = collections.defaultdict(set)
for dataset in ('ca1', 'cui', 'wang'):
func = globals()['%s_interactions' % dataset]
data = func()
for i in data:
for side in ('source', 'target'):
a_uniprot = 'uniprot_%s' % side
a_entrez = 'entrez_%s' % side
a_function = 'function_%s' % side
a_location = 'location_%s' % side
function = getattr(i, a_function)
location = getattr(i, a_location)
if function in NA_VALUES and location in NA_VALUES:
continue
uniprots = (
(getattr(i, a_uniprot),)
if hasattr(i, a_uniprot) else
mapping.map_name(getattr(i, a_entrez), 'entrez', 'uniprot')
)
for uniprot in uniprots:
result[uniprot].add(
record(
function = function,
location = location,
)
)
return dict(result)
