def main(args):
# This file takes about 32 GB to load
if not args.infile:
args.infile = './Data/indra_raw/bioexp_all_raw.pkl'
if not args.outfile:
args.outfile = './filtered_indra_network.sif'
# Load statements from file
stmts_raw = assemble_corpus.load_statements(args.infile)
# Expand families, fix grounding errors and run run preassembly
stmts_fixed = assemble_corpus.run_preassembly(
assemble_corpus.map_grounding(
assemble_corpus.expand_families(stmts_raw)))
# Default filtering: specific (unique) genes that are grounded.
stmts_filtered = assemble_corpus.filter_grounded_only(
assemble_corpus.filter_genes_only(stmts_fixed, specific_only=True))
# Custom filters
if args.human_only:
stmts_filtered = assemble_corpus.filter_human_only(stmts_filtered)
if args.filter_direct:
stmts_filtered = assemble_corpus.filter_direct(stmts_filtered)
binary_stmts = [s for s in stmts_filtered if len(s.agent_list()) == 2 and s.agent_list()[0] is not None]
rows = []
for s in binary_stmts:
rows.append([ag.name for ag in s.agent_list()])
# Write rows to .sif file
with open(args.outfile, 'w', newline='') as csvfile:
wrtr = csv.writer(csvfile, delimiter='\t')
for row in rows:
wrtr.writerow(row)
def run_assembly(stmts, save_file):
stmts = ac.map_grounding(stmts)
stmts = ac.filter_grounded_only(stmts)
stmts = ac.filter_human_only(stmts)
stmts = ac.expand_families(stmts)
stmts = ac.filter_gene_list(stmts, gene_names, 'one')
stmts = ac.map_sequence(stmts)
stmts = ac.run_preassembly(stmts, return_toplevel=False)
stmts = ac.filter_belief(stmts, 0.95)
stmts = ac.filter_top_level(stmts)
stmts = ac.filter_direct(stmts)
stmts = ac.filter_enzyme_kinase(stmts)
ac.dump_statements(stmts, save_file)
return stmts
def get_indra_phos_stmts():
stmts = by_gene_role_type(stmt_type='Phosphorylation')
stmts += by_gene_role_type(stmt_type='Dephosphorylation')
stmts = ac.map_grounding(stmts)
# Expand families before site mapping
stmts = ac.expand_families(stmts)
stmts = ac.filter_grounded_only(stmts)
stmts = ac.map_sequence(stmts)
ac.dump_statements(stmts, 'sources/indra_phos_sitemap.pkl')
stmts = ac.run_preassembly(stmts,
poolsize=4,
save='sources/indra_phos_stmts_pre.pkl')
stmts = ac.filter_human_only(stmts)
stmts = ac.filter_genes_only(stmts, specific_only=True)
ac.dump_statements(stmts, 'sources/indra_phos_stmts.pkl')
return stmts
def test_expand_families():
st_out = ac.expand_families([st10])
assert (len(st_out) == 2)
def test_expand_families():
st_out = ac.expand_families([st10])
assert len(st_out) == 2
prior_stmts = ac.map_grounding(prior_stmts,
save=pjoin(outf, 'gmapped_prior.pkl'))
reach_stmts = ac.load_statements(pjoin(outf, 'phase3_stmts.pkl'))
reach_stmts = ac.filter_no_hypothesis(reach_stmts)
#extra_stmts = ac.load_statements(pjoin(outf, 'extra_stmts.pkl'))
extra_stmts = read_extra_sources(pjoin(outf, 'extra_stmts.pkl'))
reading_stmts = reach_stmts + extra_stmts
reading_stmts = ac.map_grounding(reading_stmts,
save=pjoin(outf,
'gmapped_reading.pkl'))
stmts = prior_stmts + reading_stmts + extra_stmts
stmts = ac.filter_grounded_only(stmts)
stmts = ac.filter_genes_only(stmts, specific_only=False)
stmts = ac.filter_human_only(stmts)
stmts = ac.expand_families(stmts)
stmts = ac.filter_gene_list(stmts, data_genes, 'one')
stmts = ac.map_sequence(stmts, save=pjoin(outf, 'smapped.pkl'))
#stmts = ac.load_statements(pjoin(outf, 'smapped.pkl'))
stmts = ac.run_preassembly(stmts,
return_toplevel=False,
save=pjoin(outf, 'preassembled.pkl'),
poolsize=4)
### PySB assembly
if 'pysb' in assemble_models:
pysb_model = assemble_pysb(stmts, data_genes,
pjoin(outf, 'korkut_model_pysb.py'))
### SIF assembly
if 'sif' in assemble_models:
sif_str = assemble_sif(stmts, data, pjoin(outf,
stmts = ac.load_statements(pjoin(outf, 'preassembled.pkl'))
#stmts = ac.load_statements(pjoin(outf, 'prior.pkl'))
else:
#prior_stmts = build_prior(data_genes, pjoin(outf, 'prior.pkl'))
prior_stmts = ac.load_statements(pjoin(outf, 'prior.pkl'))
prior_stmts = ac.map_grounding(prior_stmts,
save=pjoin(outf, 'gmapped_prior.pkl'))
reading_stmts = ac.load_statements(pjoin(outf, 'phase3_stmts.pkl'))
reading_stmts = ac.map_grounding(reading_stmts,
save=pjoin(outf, 'gmapped_reading.pkl'))
stmts = prior_stmts + reading_stmts
stmts = ac.filter_grounded_only(stmts)
stmts = ac.filter_genes_only(stmts, specific_only=False)
stmts = ac.filter_human_only(stmts)
stmts = ac.expand_families(stmts)
stmts = ac.filter_gene_list(stmts, data_genes, 'one')
stmts = ac.map_sequence(stmts, save=pjoin(outf, 'smapped.pkl'))
stmts = ac.run_preassembly(stmts, return_toplevel=False,
save=pjoin(outf, 'preassembled.pkl'))
assemble_models = []
assemble_models.append('sif')
assemble_models.append('pysb')
assemble_models.append('cx')
### PySB assembly
if 'pysb' in assemble_models:
pysb_model = assemble_pysb(stmts, data_genes,
pjoin(outf, 'korkut_model_pysb.py'))
### SIF assembly
