#!/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 os
import datetime
import collections
import bs4
import pypath.resources.urls as urls
import pypath.share.curl as curl
import pypath.share.cache as cache
import pypath.utils.taxonomy as taxonomy
import pypath.utils.mapping as mapping
[docs]
def celltalkdb_download(filename = 'lr_pair', organism = 9606):
"""
Downloads a file from CellTalkDB.
:param str filename:
The file name of the dataset to download. Possible values:
lr_pair, gene_info, gene2ensembl, uniprot.
:param int,str organism:
Human and mouse supported, in case of incomprehensible value will
fall back to human.
:return:
Generator yielding dataset specific records as named tuples.
"""
taxid = taxonomy.ensure_common_name(organism)
organism = taxid.lower() if taxid in {'Mouse', 'Human'} else 'human'
cache_fname = 'celltalkdb_%s_%s' % (organism, filename)
cache_path = os.path.join(cache.get_cachedir(), cache_fname)
if os.path.exists(cache_path):
result = curl.FileOpener(cache_path).result
else:
url = urls.urls['celltalkdb']['url']
ref_url = urls.urls['celltalkdb']['ref_url']
init_url = urls.urls['celltalkdb']['init_url']
cookie = ''
c_init = curl.Curl(
init_url,
silent = True,
large = True,
cache = False,
follow = False,
bypass_url_encoding = True,
retries = 1,
empty_attempt_again = False,
)
for h in c_init.resp_headers:
if h.lower().startswith(b'set-cookie'):
cookie = h.decode().split(':')[1].split(';')[0]
soup = bs4.BeautifulSoup(c_init.fileobj, 'html.parser')
form = soup.find('form', {'action': 'handler/download.php'})
inputs = dict(
(
field.attrs['name'],
field.attrs['value']
)
for field in form.find_all('input')
)
inputs['ref'] = ref_url
inputs['filename'] = '%s_%s.txt' % (organism, filename)
c = curl.Curl(
url = url,
cache = cache_path,
post = inputs,
silent = False,
large = True,
req_headers = [
'Cookie: %s' % cookie,
'Referer: http://tcm.zju.edu.cn/celltalkdb/download.php',
],
)
result = c.result
header = next(result).strip().split('\t')
record = collections.namedtuple('CellTalkDbRecord', header)
for values in result:
yield record(*values.strip().split('\t'))
[docs]
def celltalkdb_interactions(organism = 9606):
"""
Retrieves ligand-receptor interactions from CellTalkDB
http://tcm.zju.edu.cn/celltalkdb/index.php
:param int,str organism:
Human and mouse supported, in case of incomprehensible value will
fall back to human.
:return:
List of interactions as named tuples.
"""
CellTalkDBInteraction = collections.namedtuple(
'CellTalkDBInteraction',
[
'ligand_genesymbol',
'receptor_genesymbol',
'reference',
]
)
return [
CellTalkDBInteraction(
ligand_genesymbol = rec.ligand_gene_symbol,
receptor_genesymbol = rec.receptor_gene_symbol,
reference = rec.evidence,
)
for rec in celltalkdb_download(organism = organism)
]
[docs]
def celltalkdb_annotations(organism = 9606):
"""
Retrieves annotation of protein ligand and receptor roles from CellTalkDB
http://tcm.zju.edu.cn/celltalkdb/index.php
:param int,str organism:
Human and mouse supported, in case of incomprehensible value will
fall back to human.
:return:
Dictionary of annotations with UniProt IDs as keys.
"""
CellTalkDBAnnotation = collections.namedtuple(
'CellTalkDBAnnotation',
[
'role',
'pmid',
]
)
ncbi_tax_id = taxonomy.ensure_ncbi_tax_id(organism)
ncbi_tax_id = ncbi_tax_id if ncbi_tax_id in {9606, 10090} else 9606
annot = collections.defaultdict(set)
for rec in celltalkdb_download(organism = ncbi_tax_id):
for role in ('ligand', 'receptor'):
uniprots = mapping.map_name(
getattr(rec, '%s_gene_symbol' % role),
'genesymbol',
'uniprot',
ncbi_tax_id = ncbi_tax_id,
)
for uniprot in uniprots:
annot[uniprot].add(
CellTalkDBAnnotation(
role = role,
pmid = rec.evidence,
)
)
return dict(annot)
