#!/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 future.utils import iteritems
import csv
import collections
import pypath.share.curl as curl
import pypath.resources.urls as urls
import pypath.utils.mapping as mapping
import pypath.inputs.common as inputs_common
import pypath.inputs.science as science
[docs]
def get_proteinatlas(normal = True, pathology = True, cancer = True):
result = {
'normal': collections.defaultdict(dict),
'pathology': collections.defaultdict(dict),
}
def line(l):
return l.strip('\n\r').split('\t')
if normal:
c = curl.Curl(urls.urls['proteinatlas']['normal'],
silent = False, large = True)
fp = list(c.result.values())[0]
hdr = line(fp.readline().strip())
for l in fp:
l = line(l)
uniprots = mapping.map_name(l[1], 'genesymbol', 'uniprot')
tissue = '%s:%s' % (l[2], l[3])
for u in uniprots:
result['normal'][tissue][u] = (l[4], l[5].strip())
if cancer or pathology:
c = curl.Curl(urls.urls['proteinatlas']['pathology'],
silent = False, large = True)
fp = list(c.result.values())[0]
hdr = line(fp.readline())
for l in fp:
l = line(l)
uniprots = mapping.map_name(l[1], 'genesymbol', 'uniprot')
tissue = l[2]
values = dict(
(h, float(l[i + 3]) if '.' in l[i + 3] else int(l[i + 3]))
for i, h in enumerate(hdr[3:])
if len(l) and len(l[i + 3].strip())
)
for u in uniprots:
result['pathology'][tissue][u] = values
return dict((k, dict(v)) for k, v in iteritems(result))
[docs]
def proteinatlas_annotations(normal = True, pathology = True, cancer = True):
LEVELS = ('Not detected', 'Low', 'Medium', 'High')
ProteinatlasAnnotation = collections.namedtuple(
'ProtainatlasAnnotation',
[
'organ',
'tissue',
'level',
'status',
'n_not_detected',
'n_low',
'n_medium',
'n_high',
'prognostic',
'favourable',
'score',
'pathology',
],
)
ProteinatlasAnnotation.__new__.__defaults__ = (
(None,) * 4 + (False, False, None, False)
)
def n_or_none(ex, key):
return ex[key] if key in ex else None
data = get_proteinatlas(
normal = normal,
pathology = pathology,
cancer = cancer,
)
result = collections.defaultdict(set)
if normal:
for tissue, gex in iteritems(data['normal']):
organ = tissue
if ':' in tissue:
organ, tissue = tissue.split(':')
organ = organ.strip()
tissue = tissue.strip()
for uniprot, ex in iteritems(gex):
uniprots = mapping.map_name(uniprot, 'uniprot', 'uniprot')
for _uniprot in uniprots:
result[_uniprot].add(
ProteinatlasAnnotation(
organ = organ,
tissue = tissue,
level = ex[0],
status = ex[1],
)
)
if pathology or cancer:
for condition, gex in iteritems(data['pathology']):
for uniprot, ex in iteritems(gex):
try:
effect, score = next(
i for i in iteritems(ex) if i[0] not in LEVELS
)
prognostic = not effect.startswith('unprognostic')
favourable = not effect.endswith('unfavourable')
except StopIteration:
prognostic, favourable, score = None, None, None
uniprots = mapping.map_name(uniprot, 'uniprot', 'uniprot')
for _uniprot in uniprots:
result[_uniprot].add(
ProteinatlasAnnotation(
organ = condition,
tissue = condition,
level = max(
(i for i in iteritems(ex) if i[0] in LEVELS),
key = lambda i: i[1],
default = (None,),
)[0],
status = None,
n_not_detected = n_or_none(ex, 'Not detected'),
n_low = n_or_none(ex, 'Low'),
n_medium = n_or_none(ex, 'Medium'),
n_high = n_or_none(ex, 'High'),
prognostic = prognostic,
favourable = favourable,
score = score,
pathology = True,
)
)
return dict(result)
[docs]
def proteinatlas_subcellular_annotations():
ProteinatlasSubcellularAnnotation = collections.namedtuple(
'ProteinatlasSubcellularAnnotation',
[
'location',
'status',
],
)
url = urls.urls['proteinatlas']['subcell']
c = curl.Curl(
url,
large = True,
silent = False,
default_mode = 'r',
)
reader = csv.DictReader(
c.files_multipart['subcellular_location.tsv'],
delimiter = '\t',
)
result = collections.defaultdict(set)
for rec in reader:
uniprots = mapping.map_name(rec['Gene name'], 'genesymbol', 'uniprot')
for uniprot in uniprots:
for status in ('Enhanced', 'Supported', 'Uncertain'):
if not rec[status]:
continue
for location in rec[status].split(';'):
result[uniprot].add(ProteinatlasSubcellularAnnotation(
location = location,
status = status,
))
return dict(result)
[docs]
def proteinatlas_secretome_annotations():
ProteinatlasSecretomeAnnotation = collections.namedtuple(
'ProteinatlasSecretomeAnnotation',
[
'mainclass',
'secreted',
],
)
url = urls.urls['proteinatlas']['secretome']
path = science.science_download(url)
reader = inputs_common.read_xls(path)[1:]
result = collections.defaultdict(set)
for rec in reader:
for uniprot_original in rec[2].split(','):
uniprots = mapping.map_name(
uniprot_original,
'uniprot',
'uniprot',
)
for uniprot in uniprots:
result[uniprot].add(ProteinatlasSecretomeAnnotation(
mainclass = rec[3],
secreted = 'secreted' in rec[3].lower(),
))
return dict(result)
