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ppin.py
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ppin.py
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#! /usr/bin/env python
import pandas as pd
import argparse
import requests
import json
import sys
import os
import stringdb_params as sdb
from io import StringIO
import time
import logger
def get_string_id(genes, sdb_version):
''' Get STRING IDs of a list of gene
Returns a pandas DataFrame with columns, queryItem and stringId
'''
ids = []
chunksize = 800
gene_chunks = [genes[x:x+chunksize] for x in range(0, len(genes), chunksize)]
for chunk in gene_chunks:
params = sdb.get_params(chunk)
request_url = sdb.get_request_ids_url(sdb.get_stringapi_info(sdb_version)[0])
res = requests.post(request_url, data=params)
time.sleep(60)
inp = StringIO(res.text.strip(), newline="\n")
df = pd.read_csv(inp, sep="\t")
ids.append(df)
if len(ids) == 0:
return None
ids = pd.concat(ids)
try:
return ids[['queryItem', 'stringId']]
except:
return None
def get_string_interaction_partners(genes, sdb_version, score_cutoff, level):
''' Get STRING IDs of a list of gene
Returns a pandas DataFrame with columns, queryItem and stringId
'''
interactions = []
chunksize = 800
gene_chunks = [genes[x:x+chunksize] for x in range(0, len(genes), chunksize)]
for chunk in gene_chunks:
params = sdb.get_params(chunk)
request_url = sdb.get_request_interaction_url(sdb.get_stringapi_info(sdb_version)[0])
try:
res = requests.post(request_url, data=params)
time.sleep(60)
inp = StringIO(res.text.strip(), newline="\n")
df = pd.read_csv(inp, sep="\t")
if df.empty:
continue
df = df[['stringId_A', 'stringId_B', 'preferredName_A', 'preferredName_B', 'score']]
df = df[df.score >= score_cutoff]
interactions.append(df)
except ConnectionError as e:
exit(e)
if len(interactions) == 0:
return None
interactions = pd.concat(interactions)
if interactions.empty:
return None
interactions = interactions.rename(columns={
'stringId_A': f'id_{level}',
'stringId_B': f'id_{level + 1}',
'preferredName_A': f'level{level}',
'preferredName_B': f'level{level + 1}',
'score': f'score_{level}'})
return interactions
def query_string(genes, sdb_version, score, level):
ids_df = get_string_id(genes, sdb_version)
if ids_df is None:
return pd.DataFrame(columns=['geneA', 'geneB'])
df = get_string_interaction_partners(ids_df['stringId'].tolist(), sdb_version, score, level)
if df is None:
return pd.DataFrame(columns=['geneA', 'geneB'])
if df.empty:
return pd.DataFrame(columns=['geneA', 'geneB'])
try:
df['token'] = df.apply(
lambda row: ''.join(sorted((row[f'level{level}'], row[f'level{level+1}']))), axis=1)
except:
print(df)
exit()
df = (df
.drop_duplicates('token')
#.drop(columns=['token', f'id_{level}', f'id_{level+1}'])
.rename(columns={f'level{level}': 'geneA',
f'level{level+1}': 'geneB',
f'score_{level}': 'score'})
.assign(db = 'string')
)[['geneA', 'geneB', 'score', 'db']]
return df
def query_proper(genes, proper):
df1 = proper.loc[proper['Gene1'].isin(genes)]
#df1 = df1.rename(columns={'Gene1': f'level{level}', 'Gene2': f'level{level + 1}'})
df1 = df1.rename(columns={'Gene1': f'geneA', 'Gene2': f'geneB'})
df2 = proper.loc[proper['Gene2'].isin(genes)]
df2 = df2.rename(columns={'Gene2': f'geneA', 'Gene1': f'geneB'})
df = pd.concat([df1, df2]).reset_index(drop=True)
df = df[df['background_contamination'] == 0]
if df.empty:
return pd.DataFrame(columns=['geneA', 'geneB'])
df['token'] = df.apply(lambda row: ''.join(sorted((row['geneA'], row['geneB']))), axis=1)
df = (df
.drop_duplicates('token')
.drop(columns=['token'])
.assign(db = 'proper')
)
return df
def make_ppin(gene_df, levels, output_dir, ppin_db, sdb_version,
score, logger, bootstrap=False):
graph = []
graph_filtered = []
gene_list = [gene_df['gene'].drop_duplicates().tolist()]
for level in range(1, levels + 1):
level_df = []
if ppin_db == 'string':
level_df.append(query_string(gene_list[level-1], sdb_version[0], score, level-1))
if ppin_db == 'proper':
proper_fp = os.path.join(os.path.dirname(__file__), 'data/PROPER_v1.csv')
proper = pd.read_csv(proper_fp).rename(columns={
'Cell line specificity': f'cell_line',
'Odds ratio': f'odds_ratio',
'BH-corrected p-value': f'pval',
'Read count': 'read_count',
'Potential background contamination': 'background_contamination'})
level_df.append(query_proper(gene_list[level-1], proper))
level_df = pd.concat(level_df)
if level_df.empty:
gene_list.append([])
continue
level_df['level'] = level
if not bootstrap:
graph.append(level_df)
for i in range(len(gene_list)):
level_df = level_df[~(level_df[f'geneB'].isin(gene_list[i]))]
graph_filtered.append(level_df)
gene_list.append(level_df[f'geneB'].drop_duplicates().tolist())
if not bootstrap:
if len(graph) == 0:
return gene_list, pd.DataFrame(columns=['geneA', 'geneB'])
graph = pd.concat(graph)
graph_filtered = pd.concat(graph_filtered)
write_results(graph.drop_duplicates(),
os.path.join(output_dir, 'graph.txt'))
write_results(graph_filtered.drop_duplicates(),
os.path.join(output_dir, 'graph_filtered.txt'))
del graph
for level in range(len(gene_list)):
write_results(pd.DataFrame({'gene': gene_list[level]}),
os.path.join(output_dir, f'level{level}_genes.txt'))
return gene_list
def write_results(df, output_fp):
out_dir = os.path.dirname(output_fp)
os.makedirs(out_dir, exist_ok=True)
#logger.write('Writing PPIN...')
df.to_csv(output_fp, sep='\t', index=False)
def parse_input(inputs, logger):
'''Return a dataframe of gene input.'''
#logger.write('Parsing input...')
df = pd.DataFrame()
if os.path.isfile(inputs[0]): # Input is file.
df = pd.read_csv(inputs[0], sep='\t')
if not 'gene' in df.columns:
sys.exit('EXITING: No column named "gene" found in input.')
df = df[['gene']].drop_duplicates()
else: # Input probably space-separated list of genes.
df = pd.DataFrame({'gene': [i.upper() for i in inputs]})
df = df[['gene']].drop_duplicates()
return df
def parse_args():
parser = argparse.ArgumentParser(
description='Query STRINGdb for proteins that interact with input proteins.')
parser.add_argument(
'-i', '--input', required=True, nargs='+',
help='''A space-separated list of gene symbols or filepath to a file
containing gene symbols in the 'gene' column.''' )
parser.add_argument(
'-o', '--output-dir', required=True,
help='Directory to write results.')
parser.add_argument(
'-l', '--levels', default=1, type=int,
help='Path length (i.e. number of nodes) to query. Default: 1')
parser.add_argument(
'-p', '--ppin', nargs='+', default=['string', 'proper'], choices=['string', 'proper'],
help='PPIN database(s) to query. Default: "all"')
parser.add_argument(
'-s', '--score', default=0.7, type=float,
help='Cut-off score for STRING interactions. Default: 0.7')
return parser.parse_args()
if __name__=='__main__':
pd.options.mode.chained_assignment = None
start_time = time.time()
args = parse_args()
os.makedirs(args.output, exist_ok=True)
logger = logger.Logger(logfile=os.path.join(args.output, 'ppin.log'))
logger.write('SETTINGS\n========')
for arg in vars(args):
logger.write(f'{arg}:\t {getattr(args, arg)}')
gene_df = parse_input(args.input, logger)
gene_list, graph = make_ppin(gene_df, args.levels, args.output, args.ppin, args.score, logger)
logger.write('Done.')
logger.write(f'Time elapsed: {(time.time() - start_time) / 60: .2f} minutes.')