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()