def load_mutation_heat(args):
samples = hnio.load_samples(args.sample_file) if args.sample_file else None
genes = hnio.load_genes(args.gene_file) if args.gene_file else None
snvs = hnio.load_snvs(args.snv_file, genes, samples)
cnas = hnio.load_cnas(args.cna_file, genes, samples) if args.cna_file else []
if args.cna_filter_threshold:
cnas = hnheat.filter_cnas(cnas, args.cna_filter_threshold)
if not samples:
samples = set([snv.sample for snv in snvs] + [cna.sample for cna in cnas])
return hnheat.mut_heat(len(samples), snvs, cnas, args.min_freq), None
def load_mutation_heat(args):
genes = hnio.load_genes(args.gene_file) if args.gene_file else None
samples = hnio.load_samples(args.sample_file) if args.sample_file else None
snvs = hnio.load_snvs(args.snv_file, genes, samples)
cnas = hnio.load_cnas(args.cna_file, genes,
samples) if args.cna_file else []
if args.cna_filter_threshold:
cnas = hnheat.filter_cnas(cnas, args.cna_filter_threshold)
if not samples:
samples = set([snv.sample
for snv in snvs] + [cna.sample for cna in cnas])
if not genes:
genes = set([snv.gene for snv in snvs] + [cna.gene for cna in cnas])
return hnheat.mut_heat(genes, len(samples), snvs, cnas, args.min_freq)
def run(args):
subnetworks_file = '%s/viz_files/%s' % (str(hotnet2.__file__).rsplit('/', 1)[0], VIZ_SUBNETWORKS)
# create output directory if doesn't exist; warn if it exists and is not empty
outdir = args.output_directory
if not os.path.exists(outdir):
os.makedirs(outdir)
if len(os.listdir(outdir)) > 0:
print("WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
ks = set()
output = dict(deltas=[], subnetworks=dict(), mutation_matrices=dict(), stats=dict())
subnetworks = dict()
for results_file in args.results_files:
results = json.load(open(results_file))
ccs = results['components']
heat_file = json.load(open(results['parameters']['heat_file']))
gene2heat = heat_file['heat']
heat_parameters = heat_file['parameters']
d_score = hnio.load_display_score_tsv(args.display_score_file) if args.display_score_file else None
d_name = hnio.load_display_name_tsv(args.display_name_file) if args.display_name_file else dict()
edges = hnio.load_ppi_edges(args.edge_file, hnio.load_index(results['parameters']['infmat_index_file']))
delta = format(results['parameters']['delta'], 'g')
output['deltas'].append(delta)
subnetworks[delta] = ccs
output["subnetworks"][delta] = []
for cc in ccs:
output['subnetworks'][delta].append(viz.get_component_json(cc, gene2heat, edges,
args.network_name, d_score, d_name))
# make oncoprints if heat file was generated from mutation data
if 'heat_fn' in heat_parameters and heat_parameters['heat_fn'] == 'load_mutation_heat':
output['mutation_matrices'][delta] = list()
samples = hnio.load_samples(heat_parameters['sample_file']) if heat_parameters['sample_file'] else None
genes = hnio.load_genes(heat_parameters['gene_file']) if heat_parameters['gene_file'] else None
snvs = hnio.load_snvs(heat_parameters['snv_file'], genes, samples) if heat_parameters['snv_file'] else []
cnas = hnio.load_cnas(heat_parameters['cna_file'], genes, samples) if heat_parameters['cna_file'] else []
for cc in ccs:
output['mutation_matrices'][delta].append(viz.get_oncoprint_json(cc, snvs, cnas, d_name))
if heat_parameters.get('sample_type_file'):
with open(heat_parameters['sample_type_file']) as f:
output['sampleToTypes'] = dict(l.rstrip().split() for l in f if not l.startswith("#") )
output['typeToSamples'] = dict((t, []) for t in set(output['sampleToTypes'].values()))
for s, ty in output['sampleToTypes'].iteritems():
output['typeToSamples'][ty].append( s )
else:
output['sampleToTypes'] = dict( (s, "Cancer") for s in samples )
output['typeToSamples'] = dict(Cancer=list(samples))
output['stats'][delta] = results['statistics']
for k in sorted(map(int, results['statistics'].keys())):
ks.add(k)
continue
stats = results['statistics'][str(k)]
output['stats'][delta].append( dict(k=k, expected=stats['expected'], observed=stats['observed'], pval=stats['pval']))
output['ks'] = range(min(ks), max(ks)+1)
with open('%s/subnetworks.json' % outdir, 'w') as out:
json.dump(output, out, indent=4)
shutil.copy(subnetworks_file, '%s/%s' % (outdir, VIZ_INDEX))
def run(args):
subnetworks_file = '%s/viz_files/%s' % (str(hotnet2.__file__).rsplit('/', 1)[0], VIZ_SUBNETWORKS)
# create output directory if doesn't exist; warn if it exists and is not empty
outdir = args.output_directory
if not os.path.exists(outdir):
os.makedirs(outdir)
if len(os.listdir(outdir)) > 0:
print("WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
ks = set()
output = dict(deltas=[], subnetworks=dict(), mutation_matrices=dict(), stats=dict())
predictions = set()
multipleHeatFiles = False
for results_file in args.results_files:
with open(results_file, 'r') as IN:
results = json.load(IN)
ccs = results['components']
heat_file = json.load(open(results['parameters']['heat_file']))
gene2heat = heat_file['heat']
heat_parameters = heat_file['parameters']
d_score = hnio.load_display_score_tsv(args.display_score_file) if args.display_score_file else None
d_name = hnio.load_display_name_tsv(args.display_name_file) if args.display_name_file else dict()
edges = hnio.load_ppi_edges(args.edge_file, hnio.load_index(results['parameters']['infmat_index_file']))
delta = format(results['parameters']['delta'], 'g')
output['deltas'].append(delta)
output["subnetworks"][delta] = []
predictions |= set( g for cc in ccs for g in cc )
for cc in ccs:
output['subnetworks'][delta].append(viz.get_component_json(cc, gene2heat, edges,
args.network_name, d_score, d_name))
# Record the heat scores
if 'geneToHeat' in output:
if any( output['geneToHeat'][g] != h for g, h in gene2heat.iteritems() ) or len(gene2heat.keys()) != len(output['geneToHeat'].keys()):
multipleHeatFiles = True
output['geneToHeat'] = gene2heat
# make oncoprints if heat file was generated from mutation data
if 'heat_fn' in heat_parameters and heat_parameters['heat_fn'] == 'load_mutation_heat':
output['mutation_matrices'][delta] = list()
samples = hnio.load_samples(heat_parameters['sample_file']) if heat_parameters['sample_file'] else None
genes = hnio.load_genes(heat_parameters['gene_file']) if heat_parameters['gene_file'] else None
snvs = hnio.load_snvs(heat_parameters['snv_file'], genes, samples) if heat_parameters['snv_file'] else []
cnas = hnio.load_cnas(heat_parameters['cna_file'], genes, samples) if heat_parameters['cna_file'] else []
# Get the samples and genes from the mutations directly if they weren't provided
if not samples:
samples = set( m.sample for m in snvs ) | set( m.sample for m in cnas )
if not genes:
genes = set( m.gene for m in snvs) | set( m.gene for m in cnas )
for cc in ccs:
output['mutation_matrices'][delta].append(viz.get_oncoprint_json(cc, snvs, cnas, d_name))
if heat_parameters.get('sample_type_file'):
with open(heat_parameters['sample_type_file']) as f:
output['sampleToTypes'] = dict(l.rstrip().split() for l in f if not l.startswith("#") )
output['typeToSamples'] = dict((t, []) for t in set(output['sampleToTypes'].values()))
for s, ty in output['sampleToTypes'].iteritems():
output['typeToSamples'][ty].append( s )
else:
if not samples:
samples = set( m.sample for m in snvs ) | set( m.sample for m in cnas )
output['sampleToTypes'] = dict( (s, "Cancer") for s in samples )
output['typeToSamples'] = dict(Cancer=list(samples))
output['stats'][delta] = results['statistics']
ks |= set(map(int, results['statistics'].keys()))
# Print a warning if there were multiple heat files referenced by
# the results files
if multipleHeatFiles:
sys.stderr.write('Warning: results files used multiple heat files. Only the last heat file will be used to tabulate scores.\n')
# Output to file
output['predictions'] = sorted(predictions) # list of nodes found in any run
output['ks'] = range(min(ks), max(ks)+1)
with open('%s/subnetworks.json' % outdir, 'w') as out:
json.dump(output, out, indent=4)
shutil.copy(subnetworks_file, '%s/%s' % (outdir, VIZ_INDEX))
def run(args):
subnetworks_file = '%s/viz_files/%s' % (str(hotnet2.__file__).rsplit(
'/', 1)[0], VIZ_SUBNETWORKS)
# create output directory if doesn't exist; warn if it exists and is not empty
outdir = args.output_directory
if not os.path.exists(outdir):
os.makedirs(outdir)
if len(os.listdir(outdir)) > 0:
print(
"WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
ks = set()
output = dict(deltas=[],
subnetworks=dict(),
mutation_matrices=dict(),
stats=dict())
subnetworks = dict()
for results_file in args.results_files:
results = json.load(open(results_file))
ccs = results['components']
heat_file = json.load(open(results['parameters']['heat_file']))
gene2heat = heat_file['heat']
heat_parameters = heat_file['parameters']
d_score = hnio.load_display_score_tsv(
args.display_score_file) if args.display_score_file else None
d_name = hnio.load_display_name_tsv(
args.display_name_file) if args.display_name_file else dict()
edges = hnio.load_ppi_edges(
args.edge_file,
hnio.load_index(results['parameters']['infmat_index_file']))
delta = format(results['parameters']['delta'], 'g')
output['deltas'].append(delta)
subnetworks[delta] = ccs
output["subnetworks"][delta] = []
for cc in ccs:
output['subnetworks'][delta].append(
viz.get_component_json(cc, gene2heat, edges, args.network_name,
d_score, d_name))
# make oncoprints if heat file was generated from mutation data
if 'heat_fn' in heat_parameters and heat_parameters[
'heat_fn'] == 'load_mutation_heat':
output['mutation_matrices'][delta] = list()
samples = hnio.load_samples(
heat_parameters['sample_file']
) if heat_parameters['sample_file'] else None
genes = hnio.load_genes(heat_parameters['gene_file']
) if heat_parameters['gene_file'] else None
snvs = hnio.load_snvs(
heat_parameters['snv_file'], genes,
samples) if heat_parameters['snv_file'] else []
cnas = hnio.load_cnas(
heat_parameters['cna_file'], genes,
samples) if heat_parameters['cna_file'] else []
# Get the samples and genes from the mutations directly if they weren't provided
if not samples:
samples = set(m.sample for m in snvs) | set(m.sample
for m in cnas)
if not genes:
genes = set(m.gene for m in snvs) | set(m.gene for m in cnas)
for cc in ccs:
output['mutation_matrices'][delta].append(
viz.get_oncoprint_json(cc, snvs, cnas, d_name))
if heat_parameters.get('sample_type_file'):
with open(heat_parameters['sample_type_file']) as f:
output['sampleToTypes'] = dict(l.rstrip().split()
for l in f
if not l.startswith("#"))
output['typeToSamples'] = dict(
(t, []) for t in set(output['sampleToTypes'].values()))
for s, ty in output['sampleToTypes'].iteritems():
output['typeToSamples'][ty].append(s)
else:
output['sampleToTypes'] = dict((s, "Cancer") for s in samples)
output['typeToSamples'] = dict(Cancer=list(samples))
output['stats'][delta] = results['statistics']
ks |= set(map(int, results['statistics'].keys()))
output['ks'] = range(min(ks), max(ks) + 1)
with open('%s/subnetworks.json' % outdir, 'w') as out:
json.dump(output, out, indent=4)
shutil.copy(subnetworks_file, '%s/%s' % (outdir, VIZ_INDEX))