def run(args):
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)
if args.perm_type == "heat":
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.parallel)
elif args.perm_type == "mutations":
deltas = get_deltas_for_mutations(args, infmat, infmat_index, heat_params)
else:
raise ValueError("Invalid mutation permutation type: %s" % args.perm_type)
#find the multiple of the median delta s.t. the size of the largest CC in the real data
#is <= MAX_CC_SIZE
medianDelta = np.median(deltas[MIN_CC_SIZE])
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)
for i in range(1, 11):
G = hn.weighted_graph(sim, gene_index, i*medianDelta)
max_cc_size = max([len(cc) for cc in hn.connected_components(G)])
if max_cc_size <= MAX_CC_SIZE:
break
#and recommend running HotNet with that multiple and the next 4 multiples
recommended_deltas = [i*medianDelta for i in range(i, i+5)]
output_file = open(args.output_file, 'w') if args.output_file else sys.stdout
json.dump({"parameters": vars(args), "heat_parameters": heat_params,
"recommended_deltas": recommended_deltas}, output_file, indent=4)
if (args.output_file): output_file.close()
def run(args):
# create output directory if doesn't exist; warn if output files already exist
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
dir_contents = os.listdir(args.output_directory)
if JSON_OUTPUT in dir_contents or COMPONENTS_TSV in dir_contents or SIGNIFICANCE_TSV in dir_contents:
print("WARNING: Output directory already contains HotNet results file(s), which 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()), quiet=False)
h = hn.heat_vec(heat, gene_index)
sim = hn.similarity_matrix(M, h)
G = hn.weighted_graph(sim, gene_index, args.delta)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance
if args.permutation_type != "none":
if args.permutation_type == "heat":
sizes2stats = heat_permutation_significance(args, heat, infmat, infmat_index, G)
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.")
sizes2stats = mutation_permutation_significance(args, infmat, infmat_index, G, heat_params)
else:
raise ValueError("Unrecognized permutation type %s" % (args.permutation_type))
#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
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(args.output_directory) + "/" + SIGNIFICANCE_TSV,
sizes2stats)
json_out = open(os.path.abspath(args.output_directory) + "/" + JSON_OUTPUT, 'w')
json.dump(output_dict, json_out, indent=4)
json_out.close()
hnio.write_components_as_tsv(os.path.abspath(args.output_directory) + "/" + COMPONENTS_TSV, ccs)
def run(args):
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)
if args.perm_type == "heat":
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.parallel)
elif args.perm_type == "mutations":
deltas = get_deltas_for_mutations(args, infmat, infmat_index,
heat_params)
else:
raise ValueError("Invalid mutation permutation type: %s" %
args.perm_type)
#find the multiple of the median delta s.t. the size of the largest CC in the real data
#is <= MAX_CC_SIZE
medianDelta = np.median(deltas[MIN_CC_SIZE])
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)
for i in range(1, 11):
G = hn.weighted_graph(sim, gene_index, i * medianDelta)
max_cc_size = max([len(cc) for cc in hn.connected_components(G)])
if max_cc_size <= MAX_CC_SIZE:
break
#and recommend running HotNet with that multiple and the next 4 multiples
recommended_deltas = [i * medianDelta for i in range(i, i + 5)]
output_file = open(args.output_file,
'w') if args.output_file else sys.stdout
json.dump(
{
"parameters": vars(args),
"heat_parameters": heat_params,
"recommended_deltas": recommended_deltas
},
output_file,
indent=4)
if (args.output_file):
output_file.close()
def run(args):
# create output directory if doesn't exist; warn if output files already exist
if not os.path.exists(args.output_directory):
os.makedirs(args.output_directory)
dir_contents = os.listdir(args.output_directory)
if JSON_OUTPUT in dir_contents or COMPONENTS_TSV in dir_contents or SIGNIFICANCE_TSV in dir_contents:
print(
"WARNING: Output directory already contains HotNet results file(s), which 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()),
quiet=False)
h = hn.heat_vec(heat, gene_index)
sim = hn.similarity_matrix(M, h)
G = hn.weighted_graph(sim, gene_index, args.delta)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance
if args.permutation_type != "none":
if args.permutation_type == "heat":
sizes2stats = heat_permutation_significance(
args, heat, infmat, infmat_index, G)
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."
)
sizes2stats = mutation_permutation_significance(
args, infmat, infmat_index, G, heat_params)
else:
raise ValueError("Unrecognized permutation type %s" %
(args.permutation_type))
#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
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(args.output_directory) + "/" + SIGNIFICANCE_TSV,
sizes2stats)
json_out = open(
os.path.abspath(args.output_directory) + "/" + JSON_OUTPUT, 'w')
json.dump(output_dict, json_out, indent=4)
json_out.close()
hnio.write_components_as_tsv(
os.path.abspath(args.output_directory) + "/" + COMPONENTS_TSV, ccs)
