Source code for indra.sources.trrust.processor

from copy import deepcopy
from indra.databases import hgnc_client
from indra.statements import Agent, IncreaseAmount, DecreaseAmount, Evidence


[docs]class TrrustProcessor(object): """Processor to extract INDRA Statements from Trrust data frame. Attributes ---------- df : pandas.DataFrame The Trrust table to process. statements : list[indra.statements.Statement] The list of INDRA Statements extracted from the table. """ def __init__(self, df): self.df = df self.statements = []
[docs] def extract_statements(self): """Process the table to extract Statements.""" for _, (tf, target, effect, refs) in self.df.iterrows(): tf_agent = get_grounded_agent(tf) target_agent = get_grounded_agent(target) if effect == 'Activation': stmt_cls = IncreaseAmount elif effect == 'Repression': stmt_cls = DecreaseAmount else: continue pmids = refs.split(';') for pmid in pmids: stmt = make_stmt(stmt_cls, tf_agent, target_agent, pmid) self.statements.append(stmt)
[docs]def make_stmt(stmt_cls, tf_agent, target_agent, pmid): """Return a Statement based on its type, agents, and PMID.""" ev = Evidence(source_api='trrust', pmid=pmid) return stmt_cls(deepcopy(tf_agent), deepcopy(target_agent), evidence=[ev])
[docs]def get_grounded_agent(gene_name): """Return a grounded Agent based on an HGNC symbol.""" db_refs = {'TEXT': gene_name} if gene_name in hgnc_map: gene_name = hgnc_map[gene_name] hgnc_id = hgnc_client.get_hgnc_id(gene_name) if hgnc_id: db_refs['HGNC'] = hgnc_id up_id = hgnc_client.get_uniprot_id(hgnc_id) if up_id: db_refs['UP'] = up_id agent = Agent(gene_name, db_refs=db_refs) return agent
hgnc_map = { 'CTGF': 'CCN2', 'CYR61': 'CCN1', 'MKL1': 'MRTFA', 'NOV': 'CCN3', 'RFWD2': 'COP1', 'SALL4A': 'SALL4', 'STAT5': 'STAT5A', 'TRAP': 'ACP5', }