def get_deltas_for_mutations(args, infmat, index2gene, heat_params): print "* Performing permuted mutation data delta selection..." index2gene = hnio.load_index(args.infmat_index_file) heat_permutations = permutations.generate_mutation_permutation_heat( heat_params["heat_fn"], heat_params["sample_file"], heat_params["gene_file"], 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) return get_deltas_from_heat_permutations(infmat, index2gene, heat_permutations, args.test_statistic, args.sizes, args.classic, args.num_cores)
def get_deltas_for_mutations(args, infmat, index2gene, heat_params): print "* Performing permuted mutation data delta selection..." index2gene = hnio.load_index(args.infmat_index_file) heat_permutations = permutations.generate_mutation_permutation_heat( heat_params["heat_fn"], heat_params["sample_file"], heat_params["gene_file"], 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, ) return get_deltas_from_heat_permutations( infmat, index2gene, heat_permutations, args.test_statistic, args.sizes, args.classic, args.num_cores )
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): # 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()