コード例 #1
0
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)
コード例 #2
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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)
コード例 #3
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ファイル: findComponents.py プロジェクト: ffrancis/hotnet2
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)
コード例 #4
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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)
コード例 #5
0
ファイル: simpleRun.py プロジェクト: LaconicPneumonic/hotnet2
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)