def network_delta_wrapper((network_path, infmat_name, index2gene, heat, sizes, directed,
selection_function)):
permuted_mat = hnio.load_infmat(network_path, infmat_name)
sim, index2gene = hn.similarity_matrix(permuted_mat, index2gene, heat, directed)
if selection_function is find_best_delta_by_largest_cc:
return selection_function(sim, index2gene, sizes, directed)
elif selection_function is find_best_delta_by_num_ccs:
return selection_function(sim, sizes)
else:
raise ValueError("Unknown delta selection function: %s" % (selection_function))
def network_delta_wrapper((network_path, infmat_name, index2gene, heat, sizes,
directed, selection_function)):
permuted_mat = hnio.load_infmat(network_path, infmat_name)
sim, index2gene = hn.similarity_matrix(permuted_mat, index2gene, heat,
directed)
if selection_function is find_best_delta_by_largest_cc:
return selection_function(sim, index2gene, sizes, directed)
elif selection_function is find_best_delta_by_num_ccs:
return selection_function(sim, sizes)
else:
raise ValueError("Unknown delta selection function: %s" %
(selection_function))
def run_helper(args, infmat_name, get_deltas_fn, extra_delta_args):
"""Helper shared by simpleRun and simpleRunClassic.
"""
# 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 = hnio.load_infmat(args.infmat_file, 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')
deltas = get_deltas_fn(full_index2gene, heat, args.delta_permutations, args.num_cores, infmat, addtl_genes, *extra_delta_args)
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, True)
results_files = []
for delta in 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, index2gene, delta, directed=True)
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, full_index2gene.values(),
args.significance_permutations, addtl_genes,
args.num_cores)
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, full_index2gene,
heat_permutations, delta, sizes, True,
args.num_cores)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
sizes2stats = stats.compute_statistics(size2real_counts, sizes2counts,
args.significance_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
if not using_json_heat:
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 # include delta in parameters section of output JSON
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()
results_files.append( os.path.abspath(delta_out_dir) + "/" + JSON_OUTPUT )
hnio.write_components_as_tsv(os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
# create the hierarchy if necessary
if args.output_hierarchy:
from bin import createDendrogram as CD
hierarchy_out_dir = '{}/hierarchy/'.format(args.output_directory)
if not os.path.isdir(hierarchy_out_dir): os.mkdir(hierarchy_out_dir)
params = vars(args)
CD.createDendrogram( sim, index2gene.values(), hierarchy_out_dir, params, verbose=False)
hierarchyFile = '{}/viz_files/{}'.format(str(hn.__file__).rsplit('/', 1)[0], HIERARCHY_WEB_FILE)
shutil.copy(hierarchyFile, '{}/index.html'.format(hierarchy_out_dir))
# write visualization output if edge file given
if args.edge_file:
from bin import makeResultsWebsite as MRW
viz_args = [ "-r" ] + results_files
viz_args += ["-ef", args.edge_file, "-o", args.output_directory + "/viz" ]
if args.network_name: viz_args += [ "-nn", args.network_name ]
if args.display_score_file: viz_args += [ "-dsf", args.display_score_file ]
if args.display_name_file: viz_args += [ "-dnf", args.display_name_file ]
MRW.run( MRW.get_parser().parse_args(viz_args) )
def run_helper(args, infmat_name, get_deltas_fn, extra_delta_args):
"""Helper shared by simpleRun and simpleRunClassic.
"""
# 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 = hnio.load_infmat(args.infmat_file, 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')
deltas = get_deltas_fn(full_index2gene, heat, args.delta_permutations,
args.num_cores, infmat, addtl_genes,
*extra_delta_args)
sim, index2gene = hn.similarity_matrix(infmat, full_index2gene, heat, True)
results_files = []
for delta in 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, index2gene, delta, directed=True)
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, full_index2gene.values(),
args.significance_permutations,
addtl_genes, args.num_cores)
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, full_index2gene, heat_permutations, delta, sizes, True,
args.num_cores)
real_counts = stats.num_components_min_size(G, sizes)
size2real_counts = dict(zip(sizes, real_counts))
sizes2stats = stats.compute_statistics(size2real_counts, sizes2counts,
args.significance_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
if not using_json_heat:
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 # include delta in parameters section of output JSON
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()
results_files.append(
os.path.abspath(delta_out_dir) + "/" + JSON_OUTPUT)
hnio.write_components_as_tsv(
os.path.abspath(delta_out_dir) + "/" + COMPONENTS_TSV, ccs)
# create the hierarchy if necessary
if args.output_hierarchy:
from bin import createDendrogram as CD
hierarchy_out_dir = '{}/hierarchy/'.format(args.output_directory)
if not os.path.isdir(hierarchy_out_dir): os.mkdir(hierarchy_out_dir)
params = vars(args)
CD.createDendrogram(sim,
index2gene.values(),
hierarchy_out_dir,
params,
verbose=False)
hierarchyFile = '{}/viz_files/{}'.format(
str(hn.__file__).rsplit('/', 1)[0], HIERARCHY_WEB_FILE)
shutil.copy(hierarchyFile, '{}/index.html'.format(hierarchy_out_dir))
# write visualization output if edge file given
if args.edge_file:
from bin import makeResultsWebsite as MRW
viz_args = ["-r"] + results_files
viz_args += [
"-ef", args.edge_file, "-o", args.output_directory + "/viz"
]
if args.network_name: viz_args += ["-nn", args.network_name]
if args.display_score_file:
viz_args += ["-dsf", args.display_score_file]
if args.display_name_file: viz_args += ["-dnf", args.display_name_file]
MRW.run(MRW.get_parser().parse_args(viz_args))
