Source code for bio2bel.sources.tfregulons

# -*- coding: utf-8 -*-

"""Exporter for TFregulons."""

import logging
from functools import lru_cache
from typing import Set

import pandas as pd
from pyobo import get_name_id_mapping

import pybel.dsl
from pybel import BELGraph
from .. import ensure_path

logger = logging.getLogger(__name__)

MODULE = 'tfregulons'
URL = ''

[docs]@lru_cache() def get_df() -> pd.DataFrame: """Get the TFregulons dataframe.""" path = ensure_path(MODULE, URL) return _read_df(path)
def _read_df(path) -> pd.DataFrame: df = pd.read_csv( path, compression='zip', usecols=['TF', 'target', 'effect', 'score', 'pubmedID_from_curated_resources'], ) df.rename( columns={ 'pubmedID_from_curated_resources': 'pmids', 'TF': 'tf_hgnc_symbol', 'target': 'target_hgnc_symbol', }, inplace=True, ) df = df[df['score'].isin(set('ABC'))] # there are also D and E hgnc_name_to_id = get_name_id_mapping('hgnc') df['tf_hgnc_id'] = df['tf_hgnc_symbol'].map(hgnc_name_to_id.get) df['target_hgnc_id'] = df['target_hgnc_symbol'].map(hgnc_name_to_id.get) tf_missing_id = df['tf_hgnc_id'].isna() if tf_missing_id.any(): missing_tf_symbols = df.loc[tf_missing_id, 'tf_hgnc_symbol'].unique() logger.warning(f'missing HGNC id for {len(missing_tf_symbols)} transcription factors') df = df[~tf_missing_id] target_missing_id = df['target_hgnc_id'].isna() if target_missing_id.any(): missing_target_symbols = df.loc[target_missing_id, 'target_hgnc_symbol'].unique() logger.warning(f'missing HGNC id for {len(missing_target_symbols)} targets') df = df[~target_missing_id] return df
[docs]def get_hgnc_ids(graph: BELGraph) -> Set[str]: """Get HGNC identifiers for nodes in the graph.""" return { node.identifier for node in graph if isinstance(node, pybel.dsl.CentralDogma) and node.namespace.lower() == 'hgnc' }
[docs]def get_bel() -> BELGraph: """Get the entirety of TFregulons as BEL.""" graph = BELGraph(name='TFRegulons') df = get_df() _add_rows(df, graph) return graph
[docs]def enrich_graph(graph: BELGraph) -> None: """Enrich a graph with transcription factors effecting the genes/rnas/proteins in the graph.""" hgnc_ids = get_hgnc_ids(graph) df = get_df() df = df[df['target_hgnc_id'].isin(hgnc_ids)] _add_rows(df, graph)
def _add_rows(df: pd.DataFrame, graph: BELGraph) -> None: for _, row in df.iterrows(): effect = row['effect'] if effect == 0: continue # no binding. Could add negative BEL later tf_protein = pybel.dsl.Protein( namespace='hgnc', identifier=row['tf_hgnc_id'], name=row['tf_hgnc_symbol'], ) target_rna = pybel.dsl.Rna( namespace='hgnc', identifier=row['target_hgnc_id'], name=row['target_hgnc_symbol'], ) target_gene = target_rna.get_gene() if 'pmids' in row: citations = [pmid.strip() for pmid in row['pmids'].split(',')] else: citations = [('database', 'tfregulons')] evidence = 'From TFregulons' for citation in citations: if effect == 1: binds_dna_adder, affects_expression_adder = graph.add_directly_increases, graph.add_increases else: binds_dna_adder, affects_expression_adder = graph.add_directly_decreases, graph.add_decreases binds_dna_adder( pybel.dsl.ComplexAbundance([tf_protein, target_gene]), target_rna, citation=citation, evidence=evidence, ) affects_expression_adder( tf_protein, target_rna, citation=citation, evidence=evidence, ) graph.add_transcription(target_gene, target_rna)