#!/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__ import annotations
from typing import Generator, Literal
from collections import namedtuple
import pandas as pd
import pypath.share.curl as curl
import pypath.resources.urls as urls
import pypath.share.common as common
_NUMERIC_FIELDS = {
'z_score',
'confidence',
'confidence',
'source_score',
}
[docs]
def diseases_general(
data_origin: Literal['textmining', 'knowledge', 'experiments'],
filtered: bool = False,
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Retrieve a dataset from the DISEASES database from Jensen Lab.
Warning: The "textmining" datasets are enormous!
Args:
data_origin:
The data collection method.
filtered:
Download the filtered dataset instead of the full.
return_df:
Return a pandas data frame.
"""
result = _diseases_general(data_origin, filtered)
return pd.DataFrame(result) if return_df else result
def _diseases_general(
data_origin: Literal['textmining', 'knowledge', 'experiments'],
filtered: bool = False,
) -> Generator[tuple]:
"""
Args:
data_origin:
The data collection method.
filtered:
Download the filtered dataset instead of the full.
"""
query_type = 'filtered' if filtered else 'full'
url = urls.urls['diseases']['url'] % (data_origin, query_type)
query_fields = {
'textmining':
[
'z_score',
'confidence',
'url'
],
'knowledge':
[
'resource',
'evidence_type',
'confidence'
],
'experiments':
[
'resource',
'source_score',
'confidence'
],
}
fields = [
'gene_id',
'genesymbol',
'disease_id',
'disease',
] + query_fields[data_origin]
record = namedtuple('DiseasesInteraction', fields)
c = curl.Curl(url, silent = False, large = True)
interactions = list()
def proc_field(value, key):
if key == 'source_score':
value = value.split('=')[1]
if key in _NUMERIC_FIELDS:
if '.' in value:
num_type = float
else:
num_type = int if common.is_int(value) else float
value = num_type(value)
return (key, value)
for line in c.result:
line = line.strip('\n ').split('\t')
line = dict(
proc_field(value, key)
for key, value in zip(fields, line)
)
yield record(**line)
[docs]
def textmining_full(
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Full textmining dataset of the DISEASES database from Jensen Lab.
Warning: The "textmining" datasets are enormous!
Args:
return_df:
Return a pandas data frame.
"""
return diseases_general(
data_origin = 'textmining',
filtered = False,
return_df = return_df,
)
[docs]
def textmining_filtered(
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Filtered textmining dataset of the DISEASES database from Jensen Lab.
Warning: The "textmining" datasets are enormous!
Args:
return_df:
Return a pandas data frame.
"""
return diseases_general(
data_origin = 'textmining',
filtered = True,
return_df = return_df,
)
[docs]
def knowledge_filtered(
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Filtered knowledge dataset of the DISEASES database from Jensen Lab.
Args:
return_df:
Return a pandas data frame.
"""
return diseases_general(
data_origin = 'knowledge',
filtered = True,
return_df = return_df,
)
[docs]
def knowledge_full(
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Full knowledge dataset of the DISEASES database from Jensen Lab.
Args:
return_df:
Return a pandas data frame.
"""
return diseases_general(
data_origin = 'knowledge',
filtered = False,
return_df = return_df,
)
[docs]
def experiments_filtered(
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Filtered experiments dataset of the DISEASES database from Jensen Lab.
Args:
return_df:
Return a pandas data frame.
"""
return diseases_general(
data_origin = 'experiments',
filtered = True,
return_df = return_df,
)
[docs]
def experiments_full(
return_df: bool = False,
) -> Generator[tuple] | pd.DataFrame:
"""
Full experiments dataset of the DISEASES database from Jensen Lab.
Args:
return_df:
Return a pandas data frame.
"""
return diseases_general(
data_origin = 'experiments',
filtered = False,
return_df = return_df,
)
