def run(args):
#if l not specified, set default based on test statistic
if not args.sizes:
args.sizes = [5,10,15,20] if args.test_statistic == MAX_CC_SIZE else [3]
#disallow finding delta by # of CCs of size >= l for HotNet2, since this is not currently
#implemented correctly (and is non-trivial to implement)
if not args.classic and args.test_statistic != MAX_CC_SIZE:
raise ValueError("For HotNet2, the largest CC size test statistic must be used.")
infmat_index = hnio.load_index(args.infmat_index_file)
heat, heat_params = hnio.load_heat_json(args.heat_file)
if args.perm_type == "heat":
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
addtl_genes = hnio.load_genes(args.permutation_genes_file) if args.permutation_genes_file else None
deltas = get_deltas_for_heat(infmat, infmat_index, heat, addtl_genes, args.num_permutations,
args.test_statistic, args.sizes, args.classic, args.num_cores)
elif args.perm_type == "mutations":
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
deltas = get_deltas_for_mutations(args, infmat, infmat_index, heat_params)
elif args.perm_type == "network":
deltas = get_deltas_for_network(args.permuted_networks_path, heat, args.infmat_name,
infmat_index, args.test_statistic, args.sizes,
args.classic, args.num_permutations, args.num_cores)
else:
raise ValueError("Invalid mutation permutation type: %s" % args.perm_type)
output_file = open(args.output_file, 'w') if args.output_file else sys.stdout
json.dump({"parameters": vars(args), "heat_parameters": heat_params,
"deltas": deltas}, output_file, indent=4)
if (args.output_file): output_file.close()
def run(args):
heat = args.heat_fn(args)
if args.heat_fn != load_mutation_heat and args.gene_filter_file:
heat = hnheat.reconcile_heat_with_tested_genes(heat, hnio.load_genes(args.gene_filter_file))
args.heat_fn = args.heat_fn.__name__
output_dict = {"parameters": vars(args), "heat": heat}
output_file = open(args.output_file, 'w') if args.output_file else sys.stdout
json.dump(output_dict, output_file, indent=4)
if (args.output_file): output_file.close()
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 run(args):
heat = args.heat_fn(args)
if args.heat_fn != load_mutation_heat and args.gene_filter_file:
heat = hnheat.reconcile_heat_with_tested_genes(
heat, hnio.load_genes(args.gene_filter_file))
args.heat_fn = args.heat_fn.__name__
output_dict = {"parameters": vars(args), "heat": heat}
output_file = open(args.output_file,
'w') if args.output_file else sys.stdout
json.dump(output_dict, output_file, indent=4)
if (args.output_file): output_file.close()
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):
# if l not specified, set default based on test statistic
if not args.sizes:
args.sizes = [5, 10, 15, 20] if args.test_statistic == MAX_CC_SIZE else [3]
# disallow finding delta by # of CCs of size >= l for HotNet2, since this is not currently
# implemented correctly (and is non-trivial to implement)
if not args.classic and args.test_statistic != MAX_CC_SIZE:
raise ValueError("For HotNet2, the largest CC size test statistic must be used.")
infmat_index = hnio.load_index(args.infmat_index_file)
heat, heat_params = hnio.load_heat_json(args.heat_file)
if args.perm_type == "heat":
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
addtl_genes = hnio.load_genes(args.permutation_genes_file) if args.permutation_genes_file else None
deltas = get_deltas_for_heat(
infmat,
infmat_index,
heat,
addtl_genes,
args.num_permutations,
args.test_statistic,
args.sizes,
args.classic,
args.num_cores,
)
elif args.perm_type == "mutations":
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
deltas = get_deltas_for_mutations(args, infmat, infmat_index, heat_params)
elif args.perm_type == "network":
deltas = get_deltas_for_network(
args.permuted_networks_path,
heat,
args.infmat_name,
infmat_index,
args.test_statistic,
args.sizes,
args.classic,
args.num_permutations,
args.num_cores,
)
else:
raise ValueError("Invalid mutation permutation type: %s" % args.perm_type)
output_file = open(args.output_file, "w") if args.output_file else sys.stdout
json.dump({"parameters": vars(args), "heat_parameters": heat_params, "deltas": deltas}, output_file, indent=4)
if args.output_file:
output_file.close()
def run(args):
heat, heat_excluded_genes = args.heat_fn(args)
filter_excluded_genes = []
if args.heat_fn != load_mutation_heat and args.gene_filter_file:
heat, filter_excluded_genes = hnheat.expr_filter_heat(heat,
hnio.load_genes(args.gene_filter_file))
args.heat_fn = args.heat_fn.__name__
output_dict = {"parameters": vars(args), "heat": heat}
if args.heat_fn == "load_direct_heat":
output_dict["excluded_genes"] = list(set().union(heat_excluded_genes, filter_excluded_genes))
if args.excluded_genes_output_file:
hnio.write_gene_list(args.excluded_genes_output_file, heat_excluded_genes)
output_file = open(args.output_file, 'w') if args.output_file else sys.stdout
json.dump(output_dict, output_file, indent=4)
if (args.output_file): output_file.close()
def run(args):
# create output directory if doesn't exist; warn if it exists and is not empty
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
if len(os.listdir(args.output_directory)) > 0:
print(
"WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
# load data
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
full_index2gene = hnio.load_index(args.infmat_index_file)
heat, heat_params = hnio.load_heat_json(args.heat_file)
# compute similarity matrix
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat,
not args.classic)
# only calculate permuted data sets for significance testing once
if args.permutation_type != "none":
if args.permutation_type == "heat":
print "* Generating heat permutations for statistical significance testing"
extra_genes = hnio.load_genes(args.permutation_genes_file) \
if args.permutation_genes_file else None
heat_permutations = p.permute_heat(heat, full_index2gene.values(),
args.num_permutations,
extra_genes, args.num_cores)
elif args.permutation_type == "mutations":
if heat_params["heat_fn"] != "load_mutation_heat":
raise RuntimeError(
"Heat scores must be based on mutation data to perform\
significance testing based on mutation data permutation."
)
print "* Generating mutation permutations for statistical significance testing"
heat_permutations = p.generate_mutation_permutation_heat(
heat_params["heat_fn"],
heat_params["sample_file"], heat_params["gene_file"],
full_index2gene.values(), heat_params["snv_file"],
args.gene_length_file, args.bmr, args.bmr_file,
heat_params["cna_file"], args.gene_order_file,
heat_params["cna_filter_threshold"], heat_params["min_freq"],
args.num_permutations, args.num_cores)
elif args.permutation_type == "network":
pass #nothing to do right now
elif args.permutation_type == "precomputed":
heat_file_paths = [
args.datasets_path.replace(ITERATION_REPLACEMENT_TOKEN, str(i))
for i in range(1, args.num_permutations + 1)
]
heat_permutations = [
hnio.load_heat_tsv(heat_file) for heat_file in heat_file_paths
]
else:
raise ValueError("Unrecognized permutation type %s" %
(args.permutation_type))
for delta in args.deltas:
delta_out_dir = args.output_directory + "/delta_" + str(delta)
if not os.path.isdir(delta_out_dir):
os.mkdir(delta_out_dir)
G = hn.weighted_graph(sim, index2gene, delta, not args.classic)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance
if args.permutation_type != "none":
if args.permutation_type == "network":
sizes2stats = calculate_significance_network(
args, args.permuted_networks_path, full_index2gene, G,
heat, delta, args.num_permutations)
else:
sizes2stats = calculate_significance(args, infmat,
full_index2gene, G, delta,
heat_permutations)
#sort ccs list such that genes within components are sorted alphanumerically, and components
#are sorted first by length, then alphanumerically by name of the first gene in the component
ccs = [sorted(cc) for cc in ccs]
ccs.sort(key=lambda comp: comp[0])
ccs.sort(key=len, reverse=True)
#write output
hnio.write_components_as_tsv(
os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
args.delta = delta # include delta in parameters section of output JSON
output_dict = {
"parameters": vars(args),
"heat_parameters": heat_params,
"sizes": hn.component_sizes(ccs),
"components": ccs
}
if args.permutation_type != "none":
output_dict["statistics"] = sizes2stats
hnio.write_significance_as_tsv(
os.path.abspath(delta_out_dir) + "/" + SIGNIFICANCE_TSV,
sizes2stats)
json_out = open(
os.path.abspath(delta_out_dir) + "/" + JSON_OUTPUT, 'w')
json.dump(output_dict, json_out, indent=4)
json_out.close()
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):
# create output directory if doesn't exist; warn if it exists and is not empty
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
if len(os.listdir(args.output_directory)) > 0:
print("WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
# load data
infmat = np.array(scipy.io.loadmat(args.infmat_file)[args.infmat_name])
full_index2gene = hnio.load_index(args.infmat_index_file)
heat, heat_params = hnio.load_heat_json(args.heat_file)
# compute similarity matrix
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, not args.classic)
# only calculate permuted data sets for significance testing once
if args.permutation_type != "none":
if args.permutation_type == "heat":
print "* Generating heat permutations for statistical significance testing"
extra_genes = hnio.load_genes(args.permutation_genes_file) \
if args.permutation_genes_file else None
heat_permutations = p.permute_heat(heat, full_index2gene.values(),
args.num_permutations, extra_genes, args.num_cores)
elif args.permutation_type == "mutations":
if heat_params["heat_fn"] != "load_mutation_heat":
raise RuntimeError("Heat scores must be based on mutation data to perform\
significance testing based on mutation data permutation.")
print "* Generating mutation permutations for statistical significance testing"
heat_permutations = p.generate_mutation_permutation_heat(
heat_params["heat_fn"], heat_params["sample_file"],
heat_params["gene_file"], full_index2gene.values(),
heat_params["snv_file"], args.gene_length_file, args.bmr,
args.bmr_file, heat_params["cna_file"], args.gene_order_file,
heat_params["cna_filter_threshold"], heat_params["min_freq"],
args.num_permutations, args.num_cores)
elif args.permutation_type == "network":
pass #nothing to do right now
elif args.permutation_type == "precomputed":
heat_file_paths = [args.datasets_path.replace(ITERATION_REPLACEMENT_TOKEN, str(i))
for i in range(1, args.num_permutations+1)]
heat_permutations = [hnio.load_heat_tsv(heat_file) for heat_file in heat_file_paths]
else:
raise ValueError("Unrecognized permutation type %s" % (args.permutation_type))
for delta in args.deltas:
delta_out_dir = args.output_directory + "/delta_" + str(delta)
if not os.path.isdir(delta_out_dir):
os.mkdir(delta_out_dir)
G = hn.weighted_graph(sim, index2gene, delta, not args.classic)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance
if args.permutation_type != "none":
if args.permutation_type == "network":
sizes2stats = calculate_significance_network(args, args.permuted_networks_path,
full_index2gene, G, heat, delta,
args.num_permutations)
else:
sizes2stats = calculate_significance(args, infmat, full_index2gene, G, delta,
heat_permutations)
#sort ccs list such that genes within components are sorted alphanumerically, and components
#are sorted first by length, then alphanumerically by name of the first gene in the component
ccs = [sorted(cc) for cc in ccs]
ccs.sort(key=lambda comp: comp[0])
ccs.sort(key=len, reverse=True)
#write output
hnio.write_components_as_tsv(os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
args.delta = delta # include delta in parameters section of output JSON
output_dict = {"parameters": vars(args), "heat_parameters": heat_params,
"sizes": hn.component_sizes(ccs), "components": ccs}
if args.permutation_type != "none":
output_dict["statistics"] = sizes2stats
hnio.write_significance_as_tsv(os.path.abspath(delta_out_dir) + "/" + SIGNIFICANCE_TSV,
sizes2stats)
json_out = open(os.path.abspath(delta_out_dir) + "/" + JSON_OUTPUT, 'w')
json.dump(output_dict, json_out, indent=4)
json_out.close()
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))
def run(args):
# create output directory if doesn't exist; warn if it exists and is not empty
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
if len(os.listdir(args.output_directory)) > 0:
print("WARNING: Output directory is not empty. Any conflicting files will be overwritten. "
"(Ctrl-c to cancel).")
# load data
infmat = scipy.io.loadmat(args.infmat_file)[args.infmat_name]
infmat_index = hnio.load_index(args.infmat_index_file)
heat, heat_params = hnio.load_heat_json(args.heat_file)
# compute similarity matrix and extract connected components
M, gene_index = hn.induce_infmat(infmat, infmat_index, sorted(heat.keys()))
h = hn.heat_vec(heat, gene_index)
sim = hn.similarity_matrix(M, h)
# only calculate permuted data sets for significance testing once
if args.permutation_type != "none":
if args.permutation_type == "heat":
print "* Generating heat permutations for statistical significance testing"
extra_genes = hnio.load_genes(args.permutation_genes_file) if args.permutation_genes_file \
else None
heat_permutations = permutations.permute_heat(heat, args.num_permutations, extra_genes,
args.parallel)
elif args.permutation_type == "precomputed":
heat_file_paths = [args.datasets_path.replace(ITERATION_REPLACEMENT_TOKEN, str(i))
for i in range(1, args.num_permutations+1)]
heat_permutations = [hnio.load_heat_tsv(heat_file) for heat_file in heat_file_paths]
else:
raise ValueError("Unrecognized permutation type %s" % (args.permutation_type))
for delta in args.deltas:
delta_out_dir = args.output_directory + "/delta_" + str(delta)
if not os.path.isdir(delta_out_dir):
os.mkdir(delta_out_dir)
G = hn.weighted_graph(sim, gene_index, delta)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance
if args.permutation_type != "none":
sizes2stats = calculate_significance(args, infmat, infmat_index, G, delta, heat_permutations)
#sort ccs list such that genes within components are sorted alphanumerically, and components
#are sorted first by length, then alphanumerically by name of the first gene in the component
ccs = [sorted(cc) for cc in ccs]
ccs.sort(key=lambda comp: comp[0])
ccs.sort(key=len, reverse=True)
#write output
hnio.write_components_as_tsv(os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
args.delta = delta
output_dict = {"parameters": vars(args), "heat_parameters": heat_params,
"sizes": hn.component_sizes(ccs), "components": ccs}
if args.permutation_type != "none":
output_dict["statistics"] = sizes2stats
hnio.write_significance_as_tsv(os.path.abspath(delta_out_dir) + "/" + SIGNIFICANCE_TSV,
sizes2stats)
json_out = open(os.path.abspath(delta_out_dir) + "/" + JSON_OUTPUT, 'w')
json.dump(output_dict, json_out, indent=4)
json_out.close()
