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):
# 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):
# Load the input data
if args.verbose: print('* Loading infmat and heat files...')
infmat = hnio.load_infmat(args.infmat_file, args.infmat_name)
full_index2gene = hnio.load_index(args.infmat_index_file)
using_json_heat = os.path.splitext(args.heat_file.lower())[1] == '.json'
if using_json_heat:
heat = json.load(open(args.heat_file))['heat']
else:
heat = hnio.load_heat_tsv(args.heat_file)
print("* Loaded heat scores for %s genes" % len(heat))
# filter out genes not in the network
heat = hnheat.filter_heat_to_network_genes(heat,
set(full_index2gene.values()))
# genes with score 0 cannot be in output components, but are eligible for heat in permutations
heat, addtl_genes = hnheat.filter_heat(
heat, None, False, 'There are ## genes with heat score 0')
if args.verbose: print('* Creating similarity matrix...')
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, True)
# Create and output the dendrogram
createDendrogram(sim, list(index2gene.values()), args.output_directory,
vars(args), args.verbose)
def run(args): # Load the input data if args.verbose: print '* Loading infmat and heat files...' infmat = hnio.load_infmat(args.infmat_file, args.infmat_name) full_index2gene = hnio.load_index(args.infmat_index_file) using_json_heat = os.path.splitext(args.heat_file.lower())[1] == '.json' if using_json_heat: heat = json.load(open(args.heat_file))['heat'] else: heat = hnio.load_heat_tsv(args.heat_file) print "* Loaded heat scores for %s genes" % len(heat) # filter out genes not in the network heat = hnheat.filter_heat_to_network_genes(heat, set(full_index2gene.values())) # genes with score 0 cannot be in output components, but are eligible for heat in permutations heat, addtl_genes = hnheat.filter_heat(heat, None, False, 'There are ## genes with heat score 0') if args.verbose: print '* Creating similarity matrix...' sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, True) # Create and output the dendrogram createDendrogram( sim, index2gene.values(), args.output_directory, vars(args), args.verbose )
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()