def calculate_significance(args, infmat, infmat_index, G, delta, heat_permutations):
sizes = range(args.cc_start_size, args.cc_stop_size+1)
print "\t- Using no. of components >= k (k \\in",
print "[%s, %s]) as statistic" % (min(sizes), max(sizes))
#size2counts is dict(size -> (list of counts, 1 per permutation))
sizes2counts = stats.calculate_permuted_cc_counts(infmat, infmat_index, heat_permutations,
delta, sizes, args.parallel)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
return stats.compute_statistics(size2real_counts, sizes2counts, args.num_permutations)
def calculate_significance(args, infmat, infmat_index, G, delta,
heat_permutations):
sizes = range(args.cc_start_size, args.cc_stop_size + 1)
print "\t- Using no. of components >= k (k \\in",
print "[%s, %s]) as statistic" % (min(sizes), max(sizes))
#size2counts is dict(size -> (list of counts, 1 per permutation))
sizes2counts = stats.calculate_permuted_cc_counts(infmat, infmat_index,
heat_permutations, delta,
sizes, not args.classic,
args.num_cores)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
return stats.compute_statistics(size2real_counts, sizes2counts,
args.num_permutations)
def calculate_significance_network(args, permuted_networks_path, index2gene, G, heat, delta, num_permutations):
sizes = range(args.cc_start_size, args.cc_stop_size+1)
print "\t- Using no. of components >= k (k \\in",
print "[%s, %s]) as statistic" % (min(sizes), max(sizes))
permuted_network_paths = [permuted_networks_path.replace(ITERATION_REPLACEMENT_TOKEN, str(i))
for i in range(1, num_permutations+1)]
#size2counts is dict(size -> (list of counts, 1 per permutation))
sizes2counts = stats.calculate_permuted_cc_counts_network(permuted_network_paths, args.infmat_name,
index2gene, heat, delta, sizes,
not args.classic, args.num_cores)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
return stats.compute_statistics(size2real_counts, sizes2counts, args.num_permutations)
def calculate_significance_network(args, permuted_networks_path, index2gene, G,
heat, delta, num_permutations):
sizes = range(args.cc_start_size, args.cc_stop_size + 1)
print "\t- Using no. of components >= k (k \\in",
print "[%s, %s]) as statistic" % (min(sizes), max(sizes))
permuted_network_paths = [
permuted_networks_path.replace(ITERATION_REPLACEMENT_TOKEN, str(i))
for i in range(1, num_permutations + 1)
]
#size2counts is dict(size -> (list of counts, 1 per permutation))
sizes2counts = stats.calculate_permuted_cc_counts_network(
permuted_network_paths, args.infmat_name, index2gene, heat, delta,
sizes, not args.classic, args.num_cores)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
return stats.compute_statistics(size2real_counts, sizes2counts,
args.num_permutations)
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).")
infmat = scipy.io.loadmat(args.infmat_file)[INFMAT_NAME]
infmat_index = hnio.load_index(args.infmat_index_file)
heat = hnio.load_heat_tsv(args.heat_file)
# filter out genes with heat score less than min_heat_score
heat, addtl_genes, args.min_heat_score = hnheat.filter_heat(heat, args.min_heat_score)
# find smallest delta
deltas = ft.get_deltas_for_network(args.permuted_networks_path, heat, INFMAT_NAME,
infmat_index, MAX_CC_SIZES,
args.num_permutations, args.parallel)
# and run HotNet with the median delta for each size
run_deltas = [np.median(deltas[size]) for size in deltas]
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)
# load interaction network edges and determine location of static HTML files for visualization
edges = hnio.load_ppi_edges(args.edge_file) if args.edge_file else None
index_file = '%s/viz_files/%s' % (hotnet2.__file__.rsplit('/', 1)[0], VIZ_INDEX)
subnetworks_file = '%s/viz_files/%s' % (hotnet2.__file__.rsplit('/', 1)[0], VIZ_SUBNETWORKS)
gene2index = dict([(gene, index) for index, gene in infmat_index.iteritems()])
for delta in run_deltas:
# create output directory
delta_out_dir = args.output_directory + "/delta_" + str(delta)
if not os.path.isdir(delta_out_dir):
os.mkdir(delta_out_dir)
# find connected components
G = hn.weighted_graph(sim, gene_index, delta)
ccs = hn.connected_components(G, args.min_cc_size)
# calculate significance (using all genes with heat scores)
print "* Performing permuted heat statistical significance..."
heat_permutations = p.permute_heat(heat, args.num_permutations, addtl_genes, args.parallel)
sizes = range(2, 11)
print "\t- Using no. of components >= k (k \\in",
print "[%s, %s]) as statistic" % (min(sizes), max(sizes))
sizes2counts = stats.calculate_permuted_cc_counts(infmat, infmat_index, heat_permutations,
delta, sizes, args.parallel)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
sizes2stats = stats.compute_statistics(size2real_counts, sizes2counts, args.num_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
heat_dict = {"heat": heat, "parameters": {"heat_file": args.heat_file}}
heat_out = open(os.path.abspath(delta_out_dir) + "/" + HEAT_JSON, 'w')
json.dump(heat_dict, heat_out, indent=4)
heat_out.close()
args.heat_file = os.path.abspath(delta_out_dir) + "/" + HEAT_JSON
args.delta = delta
output_dict = {"parameters": vars(args), "sizes": hn.component_sizes(ccs),
"components": ccs, "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()
hnio.write_components_as_tsv(os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
# write visualization output if edge file given
if args.edge_file:
viz_data = {"delta": delta, 'subnetworks': list()}
for cc in ccs:
viz_data['subnetworks'].append(viz.get_component_json(cc, heat, edges, gene2index,
args.network_name))
delta_viz_dir = '%s/viz/delta%s' % (args.output_directory, delta)
if not os.path.isdir(delta_viz_dir):
os.makedirs(delta_viz_dir)
viz_out = open('%s/subnetworks.json' % delta_viz_dir, 'w')
json.dump(viz_data, viz_out, indent=4)
viz_out.close()
shutil.copy(subnetworks_file, delta_viz_dir)
if args.edge_file:
viz.write_index_file(index_file, '%s/viz/%s' % (args.output_directory, VIZ_INDEX), run_deltas)
