From 62fbbc0f71024d5ea69f3264edb0ef176a5ec0c8 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Wed, 6 Jan 2021 11:16:19 +0000 Subject: [PATCH 01/73] Simple function for converting an MST to newick format --- PopPUNK/network.py | 88 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 88 insertions(+) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 67420dd6..e484f66a 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -20,6 +20,7 @@ from functools import partial from multiprocessing import Pool import graph_tool.all as gt +import dendropy from .sketchlib import addRandom @@ -704,3 +705,90 @@ def printExternalClusters(newClusters, extClusterFile, outPrefix, pd.DataFrame(data=d).to_csv(outPrefix + "_external_clusters.csv", columns = ["sample"] + list(extClusters.keys()), index = False) + +def generate_minimum_spanning_tree(G): + """Generate a minimum spanning tree from a network + + Args: + G (network) + Graph tool network + + Returns: + T (tree) + Newick representation of the minimum spanning tree + """ + # Define sequences names for tree + taxon_namespace = dendropy.TaxonNamespace([str(v) for v in G.vertices()]) + # Get network components + component_assignments, component_frequencies = gt.label_components(G) + mst_list = [] + for component_index in range(len(component_frequencies)): + # Get members of component + component_members = component_assignments.a == component_index + component = gt.GraphView(G, vfilt = component_members) + # Calculate minimum spanning tree + mst_edge_prop_map = gt.min_spanning_tree(component)#, weights = weight) + mst_network = gt.GraphView(component, efilt = mst_edge_prop_map) + # Initialise tree + tree = dendropy.Tree(taxon_namespace=taxon_namespace) + # Process edges to ensure taxa are added in a consistent order + component_edges = np.sort(component.get_edges(),axis=1) # Source always numberically first + edge_sort_order = component_edges[:,0].argsort() # Use to sort edge lengths + component_edges = component_edges[edge_sort_order] + component_edges = np.c_[ component_edges, np.ones(component_edges.shape[0]) ] + # Here should add lengths as extra column + # Iterate over edges + first_edge = True + added_leaves = dict() + for row in component_edges: + # Extract information + source = row[0] + target = row[1] + edge_length = row[2] + # Add first edge + if first_edge: + added_leaves[source] = tree.seed_node.new_child(edge_length=edge_length/2,taxon=taxon_namespace[int(source)]) + added_leaves[target] = tree.seed_node.new_child(edge_length=edge_length/2,taxon=taxon_namespace[int(target)]) + first_edge = False + # Add later edges + else: + if source in added_leaves.keys() and target not in added_leaves.keys(): + added_leaves[target] = dendropy.Node(edge_length=edge_length,taxon=taxon_namespace[int(target)]) + added_leaves[source].add_child(added_leaves[int(target)]) + + # Save tree + mst_list.append(tree) + print(T) + return T + +# https://stackoverflow.com/questions/46444454/save-networkx-tree-in-newick-format +def tree_from_edge_lst(G): + tree = {'1': {}} + for e in G.edges(): + src = e.source() + dst = e.target() + subt = recursive_search(tree, src) + print('src: ' + str(src) + ' dst: ' + str(dst) + ' subt: ' + str(subt) + '\n') + subt[dst] = {} + return tree + +def recursive_search(dict, key): + if key in dict: + return dict[key] + for k, v in dict.items(): + item = recursive_search(v, key) + if item is not None: + return item + +def tree_to_newick(tree): + items = [] + for k in tree.keys(): + s = '' + if len(tree[k].keys()) > 0: + subt = tree_to_newick(tree[k]) + if subt != '': + s += '(' + subt + ')' + s += k + items.append(s) + return ','.join(items) + From 2a91dbdb3872e16b5864913060dae5b7b7088ac3 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Wed, 6 Jan 2021 20:37:02 +0000 Subject: [PATCH 02/73] Generates MST readable by Grapetree --- PopPUNK/network.py | 68 +++++++++++++++++--------------------------- PopPUNK/visualise.py | 20 ++++++++++++- 2 files changed, 45 insertions(+), 43 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index e484f66a..8930962d 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -706,37 +706,52 @@ def printExternalClusters(newClusters, extClusterFile, outPrefix, columns = ["sample"] + list(extClusters.keys()), index = False) -def generate_minimum_spanning_tree(G): +def generate_minimum_spanning_tree(G, names): """Generate a minimum spanning tree from a network Args: G (network) Graph tool network + names (list) + List of sequence names Returns: T (tree) Newick representation of the minimum spanning tree """ # Define sequences names for tree - taxon_namespace = dendropy.TaxonNamespace([str(v) for v in G.vertices()]) + taxon_namespace = dendropy.TaxonNamespace(names) # Get network components component_assignments, component_frequencies = gt.label_components(G) + # Test if weighted network + weighted_network = False + if "weight" in G.edge_properties: + weighted_network = True + # Store trees for each component in a list mst_list = [] for component_index in range(len(component_frequencies)): # Get members of component component_members = component_assignments.a == component_index component = gt.GraphView(G, vfilt = component_members) # Calculate minimum spanning tree - mst_edge_prop_map = gt.min_spanning_tree(component)#, weights = weight) + if weighted_network: + mst_edge_prop_map = gt.min_spanning_tree(component, weights = component.ep["weight"]) + else: + mst_edge_prop_map = gt.min_spanning_tree(component) mst_network = gt.GraphView(component, efilt = mst_edge_prop_map) # Initialise tree tree = dendropy.Tree(taxon_namespace=taxon_namespace) # Process edges to ensure taxa are added in a consistent order - component_edges = np.sort(component.get_edges(),axis=1) # Source always numberically first + component_edges = np.sort(mst_network.get_edges(),axis=1) # Source always numerically first edge_sort_order = component_edges[:,0].argsort() # Use to sort edge lengths component_edges = component_edges[edge_sort_order] - component_edges = np.c_[ component_edges, np.ones(component_edges.shape[0]) ] - # Here should add lengths as extra column + print('Edges: ' + str(component_edges)) + if weighted_network: + edge_lengths = np.array(list(mst_network.edge_properties["weight"])) + print('Edge lengths!: ' + str(edge_lengths)) + else: + edge_lengths = np.ones(component_edges.shape[0]) + component_edges = np.c_[component_edges, edge_lengths[edge_sort_order]] # Iterate over edges first_edge = True added_leaves = dict() @@ -755,40 +770,9 @@ def generate_minimum_spanning_tree(G): if source in added_leaves.keys() and target not in added_leaves.keys(): added_leaves[target] = dendropy.Node(edge_length=edge_length,taxon=taxon_namespace[int(target)]) added_leaves[source].add_child(added_leaves[int(target)]) - - # Save tree + # Save trees mst_list.append(tree) - print(T) - return T - -# https://stackoverflow.com/questions/46444454/save-networkx-tree-in-newick-format -def tree_from_edge_lst(G): - tree = {'1': {}} - for e in G.edges(): - src = e.source() - dst = e.target() - subt = recursive_search(tree, src) - print('src: ' + str(src) + ' dst: ' + str(dst) + ' subt: ' + str(subt) + '\n') - subt[dst] = {} - return tree - -def recursive_search(dict, key): - if key in dict: - return dict[key] - for k, v in dict.items(): - item = recursive_search(v, key) - if item is not None: - return item - -def tree_to_newick(tree): - items = [] - for k in tree.keys(): - s = '' - if len(tree[k].keys()) > 0: - subt = tree_to_newick(tree[k]) - if subt != '': - s += '(' + subt + ')' - s += k - items.append(s) - return ','.join(items) - + # Print list of trees + with open('mst_out.tre','w') as mst_out: + [mst_out.write(t.as_string("newick")) for t in mst_list] + diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 21945677..797a26bb 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -16,7 +16,7 @@ from .models import loadClusterFit -from .network import fetchNetwork +from .network import constructNetwork, fetchNetwork, generate_minimum_spanning_tree from .plot import outputsForMicroreact from .plot import outputsForCytoscape @@ -108,6 +108,7 @@ def get_options(): faGroup.add_argument('--cytoscape', help='Generate network output files for Cytoscape', default=False, action='store_true') faGroup.add_argument('--phandango', help='Generate phylogeny and TSV for Phandango visualisation', default=False, action='store_true') faGroup.add_argument('--grapetree', help='Generate phylogeny and CSV for grapetree visualisation', default=False, action='store_true') + faGroup.add_argument('--mst', help='Calculate a minimum spanning tree', default=False, action='store_true') faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') faGroup.add_argument('--perplexity', type=float, default = 20.0, @@ -297,6 +298,7 @@ def generate_visualisations(query_db, isolateClustering = readIsolateTypeFromCsv(cluster_file, mode = mode, return_dict = True) + # Join clusters with query clusters if required if not self: if previous_query_clustering is not None: @@ -310,6 +312,22 @@ def generate_visualisations(query_db, return_dict = True) isolateClustering = joinClusterDicts(isolateClustering, queryIsolateClustering) + # Temporary - put MST here + if qr_distMat is not None: + combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) + else: + combined_dists = rr_distMat + print("WEIGHTS: " + str(combined_dists)) + G = constructNetwork( + combined_seq, + combined_seq, + [0]*combined_dists.shape[0], + 0, + edge_list=False, + weights=combined_dists + ) + generate_minimum_spanning_tree(G, combined_seq) + # Now have all the objects needed to generate selected visualisations if microreact: sys.stderr.write("Writing microreact output\n") From c7beb6f6bb3ed41bf699137d7d619bf63628a05f Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Thu, 7 Jan 2021 11:28:31 +0000 Subject: [PATCH 03/73] Restructure generation of phylogenies --- PopPUNK/network.py | 112 +++++++++++++------------- PopPUNK/plot.py | 183 ++++++++++++++++++++++++++----------------- PopPUNK/visualise.py | 95 +++++++++++++++++----- 3 files changed, 242 insertions(+), 148 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 8930962d..70bf550a 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -264,7 +264,7 @@ def writeReferences(refList, outPrefix): return refFileName def constructNetwork(rlist, qlist, assignments, within_label, - summarise = True, edge_list = False, weights = None): + summarise = True, edge_list = False, weights = None, weights_type = 'euclidean'): """Construct an unweighted, undirected network without self-loops. Nodes are samples and edges where samples are within the same cluster @@ -289,6 +289,9 @@ def constructNetwork(rlist, qlist, assignments, within_label, weights (numpy.array) If passed, the core,accessory distances for each assignment, which will be annotated as an edge attribute + weights_type (str) + Specifies the type of weight to be annotated on the graph - core, accessory + or Euclidean distance Returns: G (graph) @@ -318,7 +321,16 @@ def constructNetwork(rlist, qlist, assignments, within_label, self = self_comparison))): if assignment == within_label: if weights is not None: - dist = np.linalg.norm(weights[row_idx, :]) + if weights_type == 'euclidean': + dist = np.linalg.norm(weights[row_idx, :]) + elif weights_type == 'core': + dist = weights[row_idx, 0] + elif weights_type == 'accessory': + dist = weights[row_idx, 1] + else: + sys.stderr.write('Unable to parse network weight type ' + weights_type + '; ' + 'options are core, accessory or euclidean') + sys.exit(1) edge_tuple = (ref, query, dist) else: edge_tuple = (ref, query) @@ -716,63 +728,57 @@ def generate_minimum_spanning_tree(G, names): List of sequence names Returns: - T (tree) + tree_string (str) Newick representation of the minimum spanning tree """ # Define sequences names for tree taxon_namespace = dendropy.TaxonNamespace(names) - # Get network components - component_assignments, component_frequencies = gt.label_components(G) # Test if weighted network weighted_network = False if "weight" in G.edge_properties: weighted_network = True - # Store trees for each component in a list - mst_list = [] - for component_index in range(len(component_frequencies)): - # Get members of component - component_members = component_assignments.a == component_index - component = gt.GraphView(G, vfilt = component_members) - # Calculate minimum spanning tree - if weighted_network: - mst_edge_prop_map = gt.min_spanning_tree(component, weights = component.ep["weight"]) - else: - mst_edge_prop_map = gt.min_spanning_tree(component) - mst_network = gt.GraphView(component, efilt = mst_edge_prop_map) - # Initialise tree - tree = dendropy.Tree(taxon_namespace=taxon_namespace) - # Process edges to ensure taxa are added in a consistent order - component_edges = np.sort(mst_network.get_edges(),axis=1) # Source always numerically first - edge_sort_order = component_edges[:,0].argsort() # Use to sort edge lengths - component_edges = component_edges[edge_sort_order] - print('Edges: ' + str(component_edges)) - if weighted_network: - edge_lengths = np.array(list(mst_network.edge_properties["weight"])) - print('Edge lengths!: ' + str(edge_lengths)) - else: - edge_lengths = np.ones(component_edges.shape[0]) - component_edges = np.c_[component_edges, edge_lengths[edge_sort_order]] - # Iterate over edges - first_edge = True - added_leaves = dict() - for row in component_edges: - # Extract information - source = row[0] - target = row[1] - edge_length = row[2] - # Add first edge - if first_edge: - added_leaves[source] = tree.seed_node.new_child(edge_length=edge_length/2,taxon=taxon_namespace[int(source)]) - added_leaves[target] = tree.seed_node.new_child(edge_length=edge_length/2,taxon=taxon_namespace[int(target)]) - first_edge = False - # Add later edges - else: - if source in added_leaves.keys() and target not in added_leaves.keys(): - added_leaves[target] = dendropy.Node(edge_length=edge_length,taxon=taxon_namespace[int(target)]) - added_leaves[source].add_child(added_leaves[int(target)]) - # Save trees - mst_list.append(tree) - # Print list of trees - with open('mst_out.tre','w') as mst_out: - [mst_out.write(t.as_string("newick")) for t in mst_list] + # Calculate minimum spanning tree + if weighted_network: + mst_edge_prop_map = gt.min_spanning_tree(G, weights = G.ep["weight"]) + else: + mst_edge_prop_map = gt.min_spanning_tree(G) + mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) + # Initialise tree + tree = dendropy.Tree(taxon_namespace=taxon_namespace) + # Create nodes + tree_nodes = {v:dendropy.Node(taxon=taxon_namespace[int(v)]) for v in mst_network.get_vertices()} + # Identify edges + tree_edges = {v:[] for v in tree_nodes.keys()} + tree_edge_lengths = {v:[] for v in tree_nodes.keys()} + network_edge_weights = list(mst_network.ep["weight"]) + for i,edge in enumerate(mst_network.get_edges()): + # Connectivity + tree_edges[edge[0]].append(edge[1]) + tree_edges[edge[1]].append(edge[0]) + # Length + tree_edge_lengths[edge[0]].append(network_edge_weights[i]) + tree_edge_lengths[edge[1]].append(network_edge_weights[i]) + # Identify seed node as that with most links + max_links = 0 + seed_node_index = None + for vertex in tree_edges.keys(): + if len(tree_edges[vertex]) > max_links: + max_links = len(tree_edges[vertex]) + seed_node_index = vertex + tree.seed_node = tree_nodes[seed_node_index] + # Generate links of tree + parent_node_indices = [seed_node_index] + added_node = set(parent_node_indices) + i = 0 + while i < len(parent_node_indices): + for x,child_node_index in enumerate(tree_edges[parent_node_indices[i]]): + if child_node_index not in added_node: + tree_nodes[parent_node_indices[i]].add_child(tree_nodes[child_node_index]) + tree_nodes[child_node_index].edge_length = tree_edge_lengths[parent_node_indices[i]][x] + added_node.add(child_node_index) + parent_node_indices.append(child_node_index) + i = i + 1 + # Return tree as string + tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) + return tree_string diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 2ba936bf..03497a64 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -623,7 +623,7 @@ def writeClusterCsv(outfile, nodeNames, nodeLabels, clustering, sys.exit(1) -def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, tree_filename): +def buildRapidNJ(rapidnj, refList, coreMat, outPrefix): """Use rapidNJ for more rapid tree building Creates a phylip of core distances, system call to rapidnj executable, loads tree as @@ -636,16 +636,12 @@ def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, tree_filename): Names of sequences in coreMat (same order) coreMat (numpy.array) NxN core distance matrix produced in :func:`~outputsForMicroreact` - outPrefix (int) - Output prefix for temporary files outPrefix (str) Prefix for all generated output files, which will be placed in `outPrefix` subdirectory - tree_filename (str) - Filename for output tree (saved to disk) Returns: - tree (dendropy.Tree) - NJ tree from core distances + tree (str) + Newick-formatted NJ tree from core distances """ # generate phylip matrix phylip_name = outPrefix + "/" + os.path.basename(outPrefix) + "_core_distances.phylip" @@ -657,6 +653,7 @@ def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, tree_filename): pFile.write("\n") # construct tree + tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + "_core_NJ.nwk" rapidnj_cmd = rapidnj + " " + phylip_name + " -n -i pd -o t -x " + tree_filename + ".raw" try: # run command @@ -679,8 +676,8 @@ def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, tree_filename): tree = dendropy.Tree.get(path=tree_filename, schema="newick") return tree -def outputsForMicroreact(combined_list, coreMat, accMat, clustering, perplexity, outPrefix, epiCsv, - rapidnj, queryList = None, overwrite = False): +def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, perplexity, + outPrefix, epiCsv, queryList = None, overwrite = False): """Generate files for microreact Output a neighbour joining tree (.nwk) from core distances, a plot of t-SNE clustering @@ -690,22 +687,21 @@ def outputsForMicroreact(combined_list, coreMat, accMat, clustering, perplexity, combined_list (list) Name of sequences being analysed. The part of the name before the first '.' will be shown in the output - coreMat (numpy.array) - n x n array of core distances for n samples. - accMat (numpy.array) - n x n array of accessory distances for n samples. clustering (dict or dict of dicts) List of cluster assignments from :func:`~PopPUNK.network.printClusters`. Further clusterings (e.g. 1D core only) can be included by passing these as a dict. + nj_tree (str or None) + String representation of a Newick-formatted NJ tree + mst_tree (str or None) + String representation of a Newick-formatted minimum-spanning tree + accMat (numpy.array) + n x n array of accessory distances for n samples. perplexity (int) Perplexity parameter passed to t-SNE outPrefix (str) Prefix for all generated output files, which will be placed in `outPrefix` subdirectory epiCsv (str) A CSV containing other information, to include with the CSV of clusters - rapidnj (str) - A string with the location of the rapidnj executable for tree-building. If None, will - use dendropy by default queryList (list) Optional list of isolates that have been added as a query for colouring in the CSV. (default = None) @@ -722,12 +718,64 @@ def outputsForMicroreact(combined_list, coreMat, accMat, clustering, perplexity, writeClusterCsv(outPrefix + "/" + os.path.basename(outPrefix) + "_microreact_clusters.csv", combined_list, combined_list, clustering, 'microreact', epiCsv, queryList) + # write NJ tree + if nj_tree is not None: + write_tree(nj_tree, outPrefix, "_core_NJ.nwk", overwrite) + + # write MST + if mst_tree is not None: + write_tree(mst_tree, outPrefix, "_core_MST.nwk", overwrite) # write the phylogeny .nwk; t-SNE network .dot; clusters + data .csv - generate_phylogeny(coreMat, seqLabels, outPrefix, "_core_NJ.nwk", rapidnj, overwrite) generate_tsne(seqLabels, accMat, perplexity, outPrefix, overwrite) -def generate_phylogeny(coreMat, seqLabels, outPrefix, tree_suffix, rapidnj, overwrite): +def write_tree(tree, prefix, suffix, overwrite): + """Generate phylogeny using dendropy or RapidNJ + + Writes a neighbour joining tree (.nwk) from core distances. + + Args: + tree (str) + Newick-formatted string representation of tree + prefix (str) + Prefix for output files + suffix (str) + Suffix for output files + overwrite (bool) + Whether to overwrite existing files + """ + fn = prefix + '/' + prefix + tree_filename = prefix + "/" + os.path.basename(prefix) + suffix + if overwrite or not os.path.isfile(tree_filename): + with open(tree_filename,'w') as tree_file: + tree_file.write(tree) + +def check_tree_exists(prefix, type): + """Checks for existing trees from previous runs. + + Args: + prefix (str) + Output prefix used for search + type (str) + Type of tree (NJ or MST) + + Returns: + tree_string (str) + Newick-formatted string of NJ tree + """ + tree_string = None + tree_prefix = prefix + "/" + os.path.basename(prefix) + for suffix in ["_core_" + type + ".tree","_core_" + type + ".nwk"]: + tree_fn = tree_prefix + suffix + if os.path.isfile(tree_fn): + tree = dendropy.Tree.get(path=tree_fn, schema="newick") + tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) + sys.stderr.write("Reading existing tree from " + tree_fn + "\n") + break + + return tree_string + +def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj): """Generate phylogeny using dendropy or RapidNJ Writes a neighbour joining tree (.nwk) from core distances. @@ -738,47 +786,43 @@ def generate_phylogeny(coreMat, seqLabels, outPrefix, tree_suffix, rapidnj, over seqLabels (list) Processed names of sequences being analysed. outPrefix (str) - Prefix for all generated output files, which will be placed in `outPrefix` subdirectory - tree_suffix (str) - String to append to tree file name + Output prefix for core distances file rapidnj (str) A string with the location of the rapidnj executable for tree-building. If None, will use dendropy by default - overwrite (bool) - Overwrite existing output if present (default = False) + + Returns: + tree_string (str) + Newick-formatted string of NJ tree """ # Save distances to file core_dist_file = outPrefix + "/" + os.path.basename(outPrefix) + "_core_dists.csv" np.savetxt(core_dist_file, coreMat, delimiter=",", header = ",".join(seqLabels), comments="") # calculate phylogeny - tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + tree_suffix - if overwrite or not os.path.isfile(tree_filename): - sys.stderr.write("Building phylogeny\n") - if rapidnj is not None: - tree = buildRapidNJ(rapidnj, seqLabels, coreMat, outPrefix, tree_filename) - else: - pdm = dendropy.PhylogeneticDistanceMatrix.from_csv(src=open(core_dist_file), - delimiter=",", - is_first_row_column_names=True, - is_first_column_row_names=False) - tree = pdm.nj_tree() - - # Not sure why, but seems that this needs to be run twice to get - # what I would think of as a midpoint rooted tree - tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False) - tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False) - tree.write(path=tree_filename, - schema="newick", - suppress_rooting=True, - unquoted_underscores=True) + sys.stderr.write("Building phylogeny\n") + if rapidnj is not None: + tree = buildRapidNJ(rapidnj, seqLabels, coreMat, outPrefix) else: - sys.stderr.write("NJ phylogeny already exists; add --overwrite to replace\n") + pdm = dendropy.PhylogeneticDistanceMatrix.from_csv(src=open(core_dist_file), + delimiter=",", + is_first_row_column_names=True, + is_first_column_row_names=False) + tree = pdm.nj_tree() + + # Midpoint root tree and write outout + suppress_rooting=True, + unquoted_underscores=True + tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False) # remove file as it can be large os.remove(core_dist_file) + + # return Newick string + tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) + return tree_string -def outputsForPhandango(combined_list, coreMat, clustering, outPrefix, epiCsv, rapidnj, +def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, rapidnj, queryList = None, overwrite = False, microreact = False): """Generate files for Phandango @@ -789,25 +833,22 @@ def outputsForPhandango(combined_list, coreMat, clustering, outPrefix, epiCsv, r combined_list (list) Name of sequences being analysed. The part of the name before the first '.' will be shown in the output - coreMat (numpy.array) - n x n array of core distances for n samples. clustering (dict or dict of dicts) List of cluster assignments from :func:`~PopPUNK.network.printClusters`. Further clusterings (e.g. 1D core only) can be included by passing these as a dict. + nj_tree (str or None) + String representation of a Newick-formatted NJ tree + mst_tree (str or None) + String representation of a Newick-formatted minimum-spanning tree outPrefix (str) Prefix for all generated output files, which will be placed in `outPrefix` subdirectory epiCsv (str) A CSV containing other information, to include with the CSV of clusters - rapidnj (str) - A string with the location of the rapidnj executable for tree-building. If None, will - use dendropy by default queryList (list) Optional list of isolates that have been added as a query for colouring in the CSV. (default = None) overwrite (bool) Overwrite existing output if present (default = False) - microreact (bool) - Avoid regenerating tree if already built for microreact (default = False) """ # generate sequence labels seqLabels = isolateNameToLabel(combined_list) @@ -816,17 +857,14 @@ def outputsForPhandango(combined_list, coreMat, clustering, outPrefix, epiCsv, r writeClusterCsv(outPrefix + "/" + os.path.basename(outPrefix) + "_phandango_clusters.csv", combined_list, combined_list, clustering, 'phandango', epiCsv, queryList) - # calculate phylogeny, or copy existing microreact file - microreact_tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + "_core_NJ.nwk" - phandango_tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + "_core_NJ.tree" - if microreact and os.path.isfile(microreact_tree_filename): - sys.stderr.write('Copying microreact tree') - copyfile(microreact_tree_filename, phandango_tree_filename) + # write NJ tree + if nj_tree is not None: + write_tree(nj_tree, outPrefix, "_core_NJ.tree", overwrite) else: - generate_phylogeny(coreMat, seqLabels, outPrefix, "_core_NJ.tree", rapidnj, overwrite) + sys.stderr.write("Need an NJ tree for a Phandango output") -def outputsForGrapetree(combined_list, coreMat, clustering, outPrefix, epiCsv, rapidnj, - queryList = None, overwrite = False, microreact = False): +def outputsForGrapetree(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, + queryList = None, overwrite = False): """Generate files for Grapetree Write a neighbour joining tree (.nwk) from core distances @@ -836,20 +874,19 @@ def outputsForGrapetree(combined_list, coreMat, clustering, outPrefix, epiCsv, r combined_list (list) Name of sequences being analysed. The part of the name before the first '.' will be shown in the output - coreMat (numpy.array) - n x n array of core distances for n samples. clustering (dict or dict of dicts) List of cluster assignments from :func:`~PopPUNK.network.printClusters`. Further clusterings (e.g. 1D core only) can be included by passing these as a dict. + nj_tree (str or None) + String representation of a Newick-formatted NJ tree + mst_tree (str or None) + String representation of a Newick-formatted minimum-spanning tree outPrefix (str) Prefix for all generated output files, which will be placed in `outPrefix` subdirectory. epiCsv (str) A CSV containing other information, to include with the CSV of clusters - rapidnj (str) - A string with the location of the rapidnj executable for tree-building. - If None, will use dendropy by default queryList (list) Optional list of isolates that have been added as a query for colouring in the CSV. @@ -857,8 +894,6 @@ def outputsForGrapetree(combined_list, coreMat, clustering, outPrefix, epiCsv, r (default = None) overwrite (bool) Overwrite existing output if present (default = False). - microreact (bool) - Avoid regenerating tree if already built for microreact (default = False). """ # generate sequence labels seqLabels = isolateNameToLabel(combined_list) @@ -868,10 +903,10 @@ def outputsForGrapetree(combined_list, coreMat, clustering, outPrefix, epiCsv, r combined_list, combined_list, clustering, 'grapetree', epiCsv, queryList) # calculate phylogeny, or copy existing microreact file - microreact_tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + "_core_NJ.nwk" - if microreact and os.path.isfile(microreact_tree_filename): - sys.stderr.write('Using microreact tree') - else: - generate_phylogeny(coreMat, seqLabels, outPrefix, "_core_NJ.nwk", rapidnj, overwrite) - + # write NJ tree + if nj_tree is not None: + write_tree(nj_tree, outPrefix, "_core_NJ.nwk", overwrite) + # write MST + if mst_tree is not None: + write_tree(mst_tree, outPrefix, "_core_MST.nwk", overwrite) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 797a26bb..4e67e1bf 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -7,6 +7,7 @@ import sys # additional import numpy as np +import dendropy # required from v2.1.1 onwards (no mash support) import pp_sketchlib @@ -23,6 +24,8 @@ from .plot import outputsForPhandango from .plot import outputsForGrapetree from .plot import writeClusterCsv +from .plot import generate_nj_tree +from .plot import check_tree_exists from .prune_db import prune_distance_matrix @@ -110,6 +113,7 @@ def get_options(): faGroup.add_argument('--grapetree', help='Generate phylogeny and CSV for grapetree visualisation', default=False, action='store_true') faGroup.add_argument('--mst', help='Calculate a minimum spanning tree', default=False, action='store_true') faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') + faGroup.add_argument('--no-nj', help='Do no calculate an NJ tree', default=False, action='store_true') faGroup.add_argument('--perplexity', type=float, default = 20.0, help='Perplexity used to calculate t-SNE projection (with --microreact) [default=20.0]') @@ -161,6 +165,8 @@ def generate_visualisations(query_db, previous_query_clustering, info_csv, rapidnj, + no_nj, + mst, overwrite, core_only, accessory_only, @@ -312,35 +318,80 @@ def generate_visualisations(query_db, return_dict = True) isolateClustering = joinClusterDicts(isolateClustering, queryIsolateClustering) - # Temporary - put MST here - if qr_distMat is not None: - combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) - else: - combined_dists = rr_distMat - print("WEIGHTS: " + str(combined_dists)) - G = constructNetwork( - combined_seq, - combined_seq, - [0]*combined_dists.shape[0], - 0, - edge_list=False, - weights=combined_dists - ) - generate_minimum_spanning_tree(G, combined_seq) + # Generate MST + mst_tree = None + if mst: + existing_tree = None + if not overwrite: + existing_tree = check_tree_exists(output, "MST") + if existing_tree is None: + if qr_distMat is not None: + combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) + else: + combined_dists = rr_distMat + G = constructNetwork( + combined_seq, + combined_seq, + [0]*combined_dists.shape[0], + 0, + edge_list=False, + weights=combined_dists, + weights_type='core' + ) + mst_tree = generate_minimum_spanning_tree(G, combined_seq) + else: + mst_tree = existing_tree + + # Generate NJ tree + nj_tree = None + if (microreact or phandango or grapetree) and not no_nj: + existing_tree = None + if not overwrite: + existing_tree = check_tree_exists(output, "NJ") + if existing_tree is None: + nj_tree = generate_nj_tree(core_distMat, + combined_seq, + output, + rapidnj) + else: + nj_tree = existing_tree # Now have all the objects needed to generate selected visualisations if microreact: sys.stderr.write("Writing microreact output\n") - outputsForMicroreact(combined_seq, core_distMat, acc_distMat, isolateClustering, perplexity, - output, info_csv, rapidnj, queryList = qlist, overwrite = overwrite) + outputsForMicroreact(combined_seq, + isolateClustering, + nj_tree, + mst_tree, + acc_distMat, + perplexity, + output, + info_csv, + queryList = qlist, + overwrite = overwrite) + if phandango: sys.stderr.write("Writing phandango output\n") - outputsForPhandango(combined_seq, core_distMat, isolateClustering, output, info_csv, rapidnj, - queryList = qlist, overwrite = overwrite, microreact = microreact) + outputsForPhandango(combined_seq, + isolateClustering, + nj_tree, + mst_tree, + output, + info_csv, + queryList = qlist, + overwrite = overwrite) + if grapetree: sys.stderr.write("Writing grapetree output\n") - outputsForGrapetree(combined_seq, core_distMat, isolateClustering, output, info_csv, rapidnj, - queryList = qlist, overwrite = overwrite, microreact = microreact) + outputsForGrapetree(combined_seq, + isolateClustering, + nj_tree, + mst_tree, + output, + info_csv, + queryList = qlist, + overwrite = overwrite) + if cytoscape: sys.stderr.write("Writing cytoscape output\n") genomeNetwork, cluster_file = fetchNetwork(prev_clustering, model, rlist, False, core_only, accessory_only) @@ -375,6 +426,8 @@ def main(): args.previous_query_clustering, args.info_csv, args.rapidnj, + args.no_nj, + args.mst, args.overwrite, args.core_only, args.accessory_only, From 22cbf809655353b0bd0700e5d9eb54cc3ce1f48d Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Thu, 7 Jan 2021 14:51:42 +0000 Subject: [PATCH 04/73] Enable construction of network from sparse distance matrix --- PopPUNK/network.py | 22 +++++++++++++++++----- PopPUNK/visualise.py | 36 ++++++++++++++++++++++++++---------- 2 files changed, 43 insertions(+), 15 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 70bf550a..fa0651d1 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -47,7 +47,6 @@ def fetchNetwork(network_dir, model, refList, ref_graph = False, Names of references that should be in the network ref_graph (bool) Use ref only graph, if available - [default = False] core_only (bool) Return the network created using only core distances @@ -264,7 +263,8 @@ def writeReferences(refList, outPrefix): return refFileName def constructNetwork(rlist, qlist, assignments, within_label, - summarise = True, edge_list = False, weights = None, weights_type = 'euclidean'): + summarise = True, edge_list = False, weights = None, + weights_type = 'euclidean', sparse = False, sparse_input = None): """Construct an unweighted, undirected network without self-loops. Nodes are samples and edges where samples are within the same cluster @@ -282,7 +282,6 @@ def constructNetwork(rlist, qlist, assignments, within_label, from :func:`~PopPUNK.bgmm.findWithinLabel` summarise (bool) Whether to calculate and print network summaries with :func:`~networkSummary` - (default = True) edge_list (bool) Whether input is edges, tuples of (v1, v2). Used with lineage assignment @@ -292,6 +291,10 @@ def constructNetwork(rlist, qlist, assignments, within_label, weights_type (str) Specifies the type of weight to be annotated on the graph - core, accessory or Euclidean distance + sparse (bool) + Whether input is a sparse distance matrix + sparse_input (numpy.array) + Sparse distance matrix from lineage fit Returns: G (graph) @@ -309,7 +312,11 @@ def constructNetwork(rlist, qlist, assignments, within_label, # identify edges if edge_list: - if weights is not None: + if sparse: + connections = [] + for (ref, query), weight in sparse_input: + connections.append((ref, query, weight)) + elif weights is not None: connections = [] for weight, (ref, query) in zip(weights, assignments): connections.append((ref, query, weight)) @@ -731,6 +738,7 @@ def generate_minimum_spanning_tree(G, names): tree_string (str) Newick representation of the minimum spanning tree """ + sys.stderr.write("Starting calculation of minimum-spanning tree\n") # Define sequences names for tree taxon_namespace = dendropy.TaxonNamespace(names) # Test if weighted network @@ -750,7 +758,10 @@ def generate_minimum_spanning_tree(G, names): # Identify edges tree_edges = {v:[] for v in tree_nodes.keys()} tree_edge_lengths = {v:[] for v in tree_nodes.keys()} - network_edge_weights = list(mst_network.ep["weight"]) + if weighted_network: + network_edge_weights = list(mst_network.ep["weight"]) + else: + network_edge_weights = [1.0]*len(mst_network.get_edges()) for i,edge in enumerate(mst_network.get_edges()): # Connectivity tree_edges[edge[0]].append(edge[1]) @@ -779,6 +790,7 @@ def generate_minimum_spanning_tree(G, names): parent_node_indices.append(child_node_index) i = i + 1 # Return tree as string + sys.stderr.write("Completed calculation of minimum-spanning tree\n") tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) return tree_string diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 4e67e1bf..6f22a076 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -8,6 +8,7 @@ # additional import numpy as np import dendropy +import scipy.sparse # required from v2.1.1 onwards (no mash support) import pp_sketchlib @@ -63,6 +64,10 @@ def get_options(): 'are from ref-query') iGroup.add_argument('--distances', help='Prefix of input pickle of pre-calculated distances') + iGroup.add_argument('--sparse-distances', + help='File (with a name *_rank*_fit.npz) storing a sparse' + ' distance matrix', + default=None) iGroup.add_argument('--include-files', help='File with list of sequences to include in visualisation. ' 'Default is to use all sequences in database.', @@ -148,6 +153,7 @@ def get_options(): def generate_visualisations(query_db, ref_db, distances, + sparse_distMat, threads, output, gpu_dist, @@ -325,19 +331,28 @@ def generate_visualisations(query_db, if not overwrite: existing_tree = check_tree_exists(output, "MST") if existing_tree is None: - if qr_distMat is not None: + G = None + if sparse_distMat is not None: + combined_dists = scipy.sparse.load_npz(sparse_distMat) + print('Sparse distmat: ' + str(combined_dists)) + G = constructNetwork(combined_seq, + combined_seq, + [0]*combined_dists.shape[0], + 0, + edge_list=False, + sparse = True, + sparse_input = combined_dists) + elif qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) else: combined_dists = rr_distMat - G = constructNetwork( - combined_seq, - combined_seq, - [0]*combined_dists.shape[0], - 0, - edge_list=False, - weights=combined_dists, - weights_type='core' - ) + G = constructNetwork(combined_seq, + combined_seq, + [0]*combined_dists.shape[0], + 0, + edge_list=False, + weights=combined_dists, + weights_type='core') mst_tree = generate_minimum_spanning_tree(G, combined_seq) else: mst_tree = existing_tree @@ -409,6 +424,7 @@ def main(): generate_visualisations(args.query_db, args.ref_db, args.distances, + args.sparse_distances, args.threads, args.output, args.gpu_dist, From c075a2de49d590d922b82e9bc42af49b2d1db21d Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Thu, 7 Jan 2021 15:23:27 +0000 Subject: [PATCH 05/73] Add weights to networks from sparse matrices --- PopPUNK/network.py | 2 +- PopPUNK/visualise.py | 5 ++--- 2 files changed, 3 insertions(+), 4 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index fa0651d1..70719ef2 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -347,7 +347,7 @@ def constructNetwork(rlist, qlist, assignments, within_label, G = gt.Graph(directed = False) G.add_vertex(len(vertex_labels)) - if weights is not None: + if weights is not None or sparse: eweight = G.new_ep("float") G.add_edge_list(connections, eprops = [eweight]) G.edge_properties["weight"] = eweight diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 6f22a076..b34c7ec6 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -334,14 +334,13 @@ def generate_visualisations(query_db, G = None if sparse_distMat is not None: combined_dists = scipy.sparse.load_npz(sparse_distMat) - print('Sparse distmat: ' + str(combined_dists)) G = constructNetwork(combined_seq, combined_seq, [0]*combined_dists.shape[0], 0, edge_list=False, - sparse = True, - sparse_input = combined_dists) + sparse=True, + sparse_input=combined_dists) elif qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) else: From 62e8d0c78cd2b01231073ec2b2395db4de94956f Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Thu, 7 Jan 2021 15:39:51 +0000 Subject: [PATCH 06/73] Construct network from sparse matrix --- PopPUNK/network.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 70719ef2..599a18f6 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -311,10 +311,10 @@ def constructNetwork(rlist, qlist, assignments, within_label, vertex_labels.append(qlist) # identify edges - if edge_list: + if edge_list or sparse: if sparse: connections = [] - for (ref, query), weight in sparse_input: + for ref,query,weight in zip(sparse_input.row, sparse_input.col, sparse_input.data): connections.append((ref, query, weight)) elif weights is not None: connections = [] From 41cd01947a48bcb76ccb66dee87471955f4d1133 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 8 Jan 2021 08:40:16 +0000 Subject: [PATCH 07/73] Prevent undefined variable being returned in ListDistInts --- PopPUNK/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/utils.py b/PopPUNK/utils.py index da1219f0..aec9b05e 100644 --- a/PopPUNK/utils.py +++ b/PopPUNK/utils.py @@ -196,7 +196,7 @@ def listDistInts(refSeqs, querySeqs, self=True): for j in range(num_ref): yield(j, i) - return comparisons + return comparisons def qcDistMat(distMat, refList, queryList, a_max): From 9dc6700e3ec8ce73904d15391dde5daa265287ed Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 8 Jan 2021 08:41:41 +0000 Subject: [PATCH 08/73] Separate dense and sparse network processing --- PopPUNK/visualise.py | 33 +++++++++++++++++++++++---------- 1 file changed, 23 insertions(+), 10 deletions(-) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index b34c7ec6..95e54e3e 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -39,6 +39,7 @@ from .utils import update_distance_matrices from .utils import readIsolateTypeFromCsv from .utils import joinClusterDicts +from .utils import listDistInts #******************************# #* *# @@ -341,17 +342,29 @@ def generate_visualisations(query_db, edge_list=False, sparse=True, sparse_input=combined_dists) - elif qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix - combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) else: - combined_dists = rr_distMat - G = constructNetwork(combined_seq, - combined_seq, - [0]*combined_dists.shape[0], - 0, - edge_list=False, - weights=combined_dists, - weights_type='core') + if qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix + combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) + else: + combined_dists = rr_distMat + #### + sys.stderr.write("Started constructing complete graph") + G = constructNetwork(combined_seq, + combined_seq, + [0]*combined_dists.shape[0], + 0, + edge_list=False, + weights=combined_dists, + weights_type='core') + sys.stderr.write("Finished constructing complete graph") + #### Alternative approach - slower on a small network +# import graph_tool.all as gt + # G = gt.complete_graph(len(combined_seq)) + # eprop_dist = G.new_edge_property("double") + # for row_idx, (ref, query) in enumerate(listDistInts(combined_seq, + # combined_seq, + # self = True)): + # eprop_dist[G.edge(ref,query)] = combined_dists[row_idx, 0] mst_tree = generate_minimum_spanning_tree(G, combined_seq) else: mst_tree = existing_tree From 5f9fb8664fa4dd2647be058a08bca555e187e281 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 8 Jan 2021 08:49:37 +0000 Subject: [PATCH 09/73] Edit progress messages --- PopPUNK/models.py | 3 ++- PopPUNK/visualise.py | 20 ++++++++++---------- 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/PopPUNK/models.py b/PopPUNK/models.py index 8b8283c9..cc7e1095 100644 --- a/PopPUNK/models.py +++ b/PopPUNK/models.py @@ -90,12 +90,13 @@ def loadClusterFit(pkl_file, npz_file, outPrefix = "", max_samples = 100000): sys.stderr.write("Loading previously refined model\n") load_obj = RefineFit(outPrefix) elif fit_type == "lineage": - sys.stderr.write("Loading previously lineage cluster model\n") + sys.stderr.write("Loading lineage cluster model\n") load_obj = LineageFit(outPrefix, fit_object[0]) else: raise RuntimeError("Undefined model type: " + str(fit_type)) load_obj.load(fit_data, fit_object) + sys.stderr.write("Completed model loading") return load_obj class ClusterFit: diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 95e54e3e..ac6d39e5 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -347,16 +347,16 @@ def generate_visualisations(query_db, combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) else: combined_dists = rr_distMat - #### - sys.stderr.write("Started constructing complete graph") - G = constructNetwork(combined_seq, - combined_seq, - [0]*combined_dists.shape[0], - 0, - edge_list=False, - weights=combined_dists, - weights_type='core') - sys.stderr.write("Finished constructing complete graph") + #### + sys.stderr.write("Started constructing complete graph") + G = constructNetwork(combined_seq, + combined_seq, + [0]*combined_dists.shape[0], + 0, + edge_list=False, + weights=combined_dists, + weights_type='core') + sys.stderr.write("Finished constructing complete graph") #### Alternative approach - slower on a small network # import graph_tool.all as gt # G = gt.complete_graph(len(combined_seq)) From 58f17cdae58468b3c1277fec98042afe186a3766 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 8 Jan 2021 08:57:50 +0000 Subject: [PATCH 10/73] Add extra progress messages --- PopPUNK/models.py | 2 +- PopPUNK/visualise.py | 6 ++++-- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/PopPUNK/models.py b/PopPUNK/models.py index cc7e1095..422b13f1 100644 --- a/PopPUNK/models.py +++ b/PopPUNK/models.py @@ -96,7 +96,7 @@ def loadClusterFit(pkl_file, npz_file, outPrefix = "", max_samples = 100000): raise RuntimeError("Undefined model type: " + str(fit_type)) load_obj.load(fit_data, fit_object) - sys.stderr.write("Completed model loading") + sys.stderr.write("Completed model loading\n") return load_obj class ClusterFit: diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index ac6d39e5..1912c1c8 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -343,12 +343,14 @@ def generate_visualisations(query_db, sparse=True, sparse_input=combined_dists) else: + sys.stderr.write("Started distance matrix processing\n") if qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) else: combined_dists = rr_distMat + sys.stderr.write("Completed distance matrix processing\n") #### - sys.stderr.write("Started constructing complete graph") + sys.stderr.write("Started constructing complete graph\n") G = constructNetwork(combined_seq, combined_seq, [0]*combined_dists.shape[0], @@ -356,7 +358,7 @@ def generate_visualisations(query_db, edge_list=False, weights=combined_dists, weights_type='core') - sys.stderr.write("Finished constructing complete graph") + sys.stderr.write("Finished constructing complete graph\n") #### Alternative approach - slower on a small network # import graph_tool.all as gt # G = gt.complete_graph(len(combined_seq)) From fa5a2d17dcbda1db49d77143ca0fcb2810b2b13a Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 8 Jan 2021 15:32:05 +0000 Subject: [PATCH 11/73] Combine sparse and full distance matrices --- PopPUNK/network.py | 40 +++++++++++++++++++++++++++++++++++++++- PopPUNK/visualise.py | 9 +++++++-- 2 files changed, 46 insertions(+), 3 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 599a18f6..c531bba1 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -725,7 +725,7 @@ def printExternalClusters(newClusters, extClusterFile, outPrefix, columns = ["sample"] + list(extClusters.keys()), index = False) -def generate_minimum_spanning_tree(G, names): +def generate_minimum_spanning_tree(G, names, distMat): """Generate a minimum spanning tree from a network Args: @@ -751,6 +751,44 @@ def generate_minimum_spanning_tree(G, names): else: mst_edge_prop_map = gt.min_spanning_tree(G) mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) + # Find seed nodes as those with greatest outdegree in each component + seed_vertices = set() + component_assignments, component_frequencies = gt.label_components(mst_network) + for component_index in range(len(component_frequencies)): + component_members = component_assignments.a == component_index + component = gt.GraphView(mst_network, vfilt = component_members) + component_vertices = component.get_vertices() + out_degrees = component.get_out_degrees(component_vertices) + print('Out degrees: ' + str(out_degrees) + '\nClass: ' + str(type(out_degrees))) + seed_vertex = list(component_vertices[np.where(out_degrees == np.amax(out_degrees))]) + print('SEEDS: ' + str(seed_vertex) + ' TYPE: ' + str(type(seed_vertex))) + seed_vertices.add(seed_vertex[0]) # Can only add one otherwise not MST + print('Seed: ' + str(seed_vertex)) + # If multiple components, calculate distances between seed nodes + if len(component_frequencies) > 1: + # Extract distances + connections = [] + for row_idx, (ref, query) in enumerate(listDistInts(names, names, self = True)): + if ref in seed_vertices and query in seed_vertices: + dist = distMat[row_idx, 0] + edge_tuple = (ref, query, dist) + connections.append(edge_tuple) + # Construct graph + seed_G = gt.Graph(directed = False) + seed_G.add_vertex(len(seed_vertex)) + if weighted_network: + eweight = seed_G.new_ep("float") + seed_G.add_edge_list(connections, eprops = [eweight]) + seed_G.edge_properties["weight"] = eweight + seed_mst_edge_prop_map = gt.min_spanning_tree(seed_G, weights = seed_G.ep["weight"]) + seed_mst_network = gt.GraphView(seed_G, efilt = seed_mst_edge_prop_map) + # Insert seed MST into original MST - may be possible to use graph_union with include=True & intersection + deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) +# print("Deep: " + str(deep_edges)) + mst_network.add_edge_list(deep_edges) + else: + sys.stderr.write('I do not currently know what to do in this case\n') + # Initialise tree tree = dendropy.Tree(taxon_namespace=taxon_namespace) # Create nodes diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 1912c1c8..38c7b530 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -334,7 +334,10 @@ def generate_visualisations(query_db, if existing_tree is None: G = None if sparse_distMat is not None: + sys.stderr.write("Started sparse distance matrix processing\n") combined_dists = scipy.sparse.load_npz(sparse_distMat) + sys.stderr.write("Completed sparse distance matrix processing\n") + sys.stderr.write("Started constructing partial graph\n") G = constructNetwork(combined_seq, combined_seq, [0]*combined_dists.shape[0], @@ -342,6 +345,8 @@ def generate_visualisations(query_db, edge_list=False, sparse=True, sparse_input=combined_dists) + + sys.stderr.write("Completed constructing partial graph\n") else: sys.stderr.write("Started distance matrix processing\n") if qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix @@ -358,7 +363,7 @@ def generate_visualisations(query_db, edge_list=False, weights=combined_dists, weights_type='core') - sys.stderr.write("Finished constructing complete graph\n") + sys.stderr.write("Completed constructing complete graph\n") #### Alternative approach - slower on a small network # import graph_tool.all as gt # G = gt.complete_graph(len(combined_seq)) @@ -367,7 +372,7 @@ def generate_visualisations(query_db, # combined_seq, # self = True)): # eprop_dist[G.edge(ref,query)] = combined_dists[row_idx, 0] - mst_tree = generate_minimum_spanning_tree(G, combined_seq) + mst_tree = generate_minimum_spanning_tree(G, combined_seq, rr_distMat) else: mst_tree = existing_tree From b4e9b36408148fef93e11987eb9d78d748bdbbc1 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 8 Jan 2021 16:54:02 +0000 Subject: [PATCH 12/73] Reformat MST output --- PopPUNK/network.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index c531bba1..9f765dd3 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -788,7 +788,9 @@ def generate_minimum_spanning_tree(G, names, distMat): mst_network.add_edge_list(deep_edges) else: sys.stderr.write('I do not currently know what to do in this case\n') - + # Print graph + pos = gt.sfdp_layout(mst_network) + gt.graph_draw(mst_network, pos, output="mst_network.pdf") # Initialise tree tree = dendropy.Tree(taxon_namespace=taxon_namespace) # Create nodes @@ -827,6 +829,12 @@ def generate_minimum_spanning_tree(G, names, distMat): added_node.add(child_node_index) parent_node_indices.append(child_node_index) i = i + 1 + # Add zero length branches for internal nodes in MST + for node in tree.preorder_node_iter(): + if not node.is_leaf(): + new_child = dendropy.Node(taxon=node.taxon,edge_length=0.0) + node.taxon = None + node.add_child(new_child) # Return tree as string sys.stderr.write("Completed calculation of minimum-spanning tree\n") tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) From d0d920cdf38df0a77fc0484516d93e9b4bc1d0c0 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Sun, 10 Jan 2021 07:15:32 +0000 Subject: [PATCH 13/73] Add debug message --- PopPUNK/network.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 9f765dd3..1be95567 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -784,6 +784,9 @@ def generate_minimum_spanning_tree(G, names, distMat): seed_mst_network = gt.GraphView(seed_G, efilt = seed_mst_edge_prop_map) # Insert seed MST into original MST - may be possible to use graph_union with include=True & intersection deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) + # debug + for row in range(deep_edges.shape[0]): + print("Linking " + str(names[deep_edges[row,0]) + " with " + str(names[deep_edges[row,1]) + " edge " + str(deep_edges[row,2)) # print("Deep: " + str(deep_edges)) mst_network.add_edge_list(deep_edges) else: From 0f2e0a54213025b5801e6e178a12ded2527e1890 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Sun, 10 Jan 2021 07:30:12 +0000 Subject: [PATCH 14/73] Update debug message --- PopPUNK/network.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 1be95567..2a247884 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -786,7 +786,12 @@ def generate_minimum_spanning_tree(G, names, distMat): deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) # debug for row in range(deep_edges.shape[0]): - print("Linking " + str(names[deep_edges[row,0]) + " with " + str(names[deep_edges[row,1]) + " edge " + str(deep_edges[row,2)) + print("Row: " + str(row) + " deep: " + str(deep_edges[row,0])) + source_index = int(deep_edges[row,0]) + target_index = int(deep_edges[row,1]) + print("Linking " + names[source_index] + " with " + names[target_index] + " edge " + str(deep_edges[row,2])) + pos = gt.sfdp_layout(seed_mst_network) + gt.graph_draw(seed_mst_network, pos, output="seed_mst_network.pdf") # print("Deep: " + str(deep_edges)) mst_network.add_edge_list(deep_edges) else: From 18d7eea8bd83b7abb77ad4f9948af8b62551b689 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Sun, 10 Jan 2021 21:27:43 +0000 Subject: [PATCH 15/73] Save intermediate MST graph --- PopPUNK/network.py | 1 + 1 file changed, 1 insertion(+) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 2a247884..447e2b42 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -751,6 +751,7 @@ def generate_minimum_spanning_tree(G, names, distMat): else: mst_edge_prop_map = gt.min_spanning_tree(G) mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) + mst_network.save("original_mst.graphml",fmt="graphml") # Find seed nodes as those with greatest outdegree in each component seed_vertices = set() component_assignments, component_frequencies = gt.label_components(mst_network) From 5867f074d439f04e241f6c24331fd69c76653057 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 06:46:06 +0000 Subject: [PATCH 16/73] Temporarily remove intermediate MST graph --- PopPUNK/network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 447e2b42..fb64004c 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -751,7 +751,7 @@ def generate_minimum_spanning_tree(G, names, distMat): else: mst_edge_prop_map = gt.min_spanning_tree(G) mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) - mst_network.save("original_mst.graphml",fmt="graphml") +# mst_network.save("original_mst.graphml",fmt="graphml") # Find seed nodes as those with greatest outdegree in each component seed_vertices = set() component_assignments, component_frequencies = gt.label_components(mst_network) From 3e48fc8c9f69b6dba45da15fe37684646e80dd16 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 09:21:49 +0000 Subject: [PATCH 17/73] Clean up debug messages before merge with master --- PopPUNK/network.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index fb64004c..2ecf25f6 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -751,7 +751,6 @@ def generate_minimum_spanning_tree(G, names, distMat): else: mst_edge_prop_map = gt.min_spanning_tree(G) mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) -# mst_network.save("original_mst.graphml",fmt="graphml") # Find seed nodes as those with greatest outdegree in each component seed_vertices = set() component_assignments, component_frequencies = gt.label_components(mst_network) @@ -760,11 +759,8 @@ def generate_minimum_spanning_tree(G, names, distMat): component = gt.GraphView(mst_network, vfilt = component_members) component_vertices = component.get_vertices() out_degrees = component.get_out_degrees(component_vertices) - print('Out degrees: ' + str(out_degrees) + '\nClass: ' + str(type(out_degrees))) seed_vertex = list(component_vertices[np.where(out_degrees == np.amax(out_degrees))]) - print('SEEDS: ' + str(seed_vertex) + ' TYPE: ' + str(type(seed_vertex))) seed_vertices.add(seed_vertex[0]) # Can only add one otherwise not MST - print('Seed: ' + str(seed_vertex)) # If multiple components, calculate distances between seed nodes if len(component_frequencies) > 1: # Extract distances @@ -785,21 +781,9 @@ def generate_minimum_spanning_tree(G, names, distMat): seed_mst_network = gt.GraphView(seed_G, efilt = seed_mst_edge_prop_map) # Insert seed MST into original MST - may be possible to use graph_union with include=True & intersection deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) - # debug - for row in range(deep_edges.shape[0]): - print("Row: " + str(row) + " deep: " + str(deep_edges[row,0])) - source_index = int(deep_edges[row,0]) - target_index = int(deep_edges[row,1]) - print("Linking " + names[source_index] + " with " + names[target_index] + " edge " + str(deep_edges[row,2])) - pos = gt.sfdp_layout(seed_mst_network) - gt.graph_draw(seed_mst_network, pos, output="seed_mst_network.pdf") -# print("Deep: " + str(deep_edges)) mst_network.add_edge_list(deep_edges) else: sys.stderr.write('I do not currently know what to do in this case\n') - # Print graph - pos = gt.sfdp_layout(mst_network) - gt.graph_draw(mst_network, pos, output="mst_network.pdf") # Initialise tree tree = dendropy.Tree(taxon_namespace=taxon_namespace) # Create nodes From e8d87657abe289387a3d41ecc0bbab80e61be4f6 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 10:19:17 +0000 Subject: [PATCH 18/73] Remove conflict message --- PopPUNK/plot.py | 1 - 1 file changed, 1 deletion(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 1ed88746..20ac6fb5 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -829,7 +829,6 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) return tree_string -<<<<<<< HEAD def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, rapidnj, queryList = None, overwrite = False, threads = 1): """Generate files for Phandango From 046b067632eac913acd664c5eafde57476a874a6 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 10:41:01 +0000 Subject: [PATCH 19/73] Make function calls consistent --- PopPUNK/plot.py | 12 ++++-------- PopPUNK/visualise.py | 9 +++------ 2 files changed, 7 insertions(+), 14 deletions(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 20ac6fb5..5ebcf6d7 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -680,7 +680,7 @@ def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, threads = 1): return tree def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, perplexity, - outPrefix, epiCsv, queryList = None, overwrite = False, threads = 1): + outPrefix, epiCsv, queryList = None, overwrite = False): """Generate files for microreact Output a neighbour joining tree (.nwk) from core distances, a plot of t-SNE clustering @@ -710,8 +710,6 @@ def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, p (default = None) overwrite (bool) Overwrite existing output if present (default = False) - threads (int) - Number of threads to use with rapidnj """ # Avoid recursive import from .tsne import generate_tsne @@ -829,8 +827,8 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) return tree_string -def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, rapidnj, - queryList = None, overwrite = False, threads = 1): +def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, + queryList = None, overwrite = False): """Generate files for Phandango Write a neighbour joining tree (.tree) from core distances @@ -873,7 +871,7 @@ def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, sys.stderr.write("Need an NJ tree for a Phandango output") def outputsForGrapetree(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, - queryList = None, overwrite = False, threads = 1): + queryList = None, overwrite = False): """Generate files for Grapetree Write a neighbour joining tree (.nwk) from core distances @@ -901,8 +899,6 @@ def outputsForGrapetree(combined_list, clustering, nj_tree, mst_tree, outPrefix, in the CSV. (default = None) overwrite (bool) Overwrite existing output if present (default = False). - threads (int) - Number of threads to use with rapidnj """ # generate sequence labels seqLabels = isolateNameToLabel(combined_list) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 9f55bdc0..c616c91f 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -403,8 +403,7 @@ def generate_visualisations(query_db, output, info_csv, queryList = qlist, - overwrite = overwrite, - threads = threads) + overwrite = overwrite) if phandango: sys.stderr.write("Writing phandango output\n") @@ -415,8 +414,7 @@ def generate_visualisations(query_db, output, info_csv, queryList = qlist, - overwrite = overwrite, - threads = threads) + overwrite = overwrite) if grapetree: sys.stderr.write("Writing grapetree output\n") @@ -427,8 +425,7 @@ def generate_visualisations(query_db, output, info_csv, queryList = qlist, - overwrite = overwrite, - threads = threads) + overwrite = overwrite) if cytoscape: sys.stderr.write("Writing cytoscape output\n") From fd86339eb2f70dcc31e16ee531f463fddf530700 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 21:05:16 +0000 Subject: [PATCH 20/73] Fix whitespace in network.py Co-authored-by: John Lees --- PopPUNK/network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 2ecf25f6..bf0f7a83 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -314,7 +314,7 @@ def constructNetwork(rlist, qlist, assignments, within_label, if edge_list or sparse: if sparse: connections = [] - for ref,query,weight in zip(sparse_input.row, sparse_input.col, sparse_input.data): + for ref, query, weight in zip(sparse_input.row, sparse_input.col, sparse_input.data): connections.append((ref, query, weight)) elif weights is not None: connections = [] From 58cbaee5d2b5dd003ac139e842a084ea65d84570 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 21:07:32 +0000 Subject: [PATCH 21/73] Fix whitespace in network.py Co-authored-by: John Lees --- PopPUNK/network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index bf0f7a83..b35c43dc 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -795,7 +795,7 @@ def generate_minimum_spanning_tree(G, names, distMat): network_edge_weights = list(mst_network.ep["weight"]) else: network_edge_weights = [1.0]*len(mst_network.get_edges()) - for i,edge in enumerate(mst_network.get_edges()): + for i, edge in enumerate(mst_network.get_edges()): # Connectivity tree_edges[edge[0]].append(edge[1]) tree_edges[edge[1]].append(edge[0]) From 7ed6cef29f7f32e628d69cb38d9c1b683f32011f Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 21:26:27 +0000 Subject: [PATCH 22/73] Extend permitted range of ranks --- PopPUNK/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/__main__.py b/PopPUNK/__main__.py index c927ac52..1bb47db9 100644 --- a/PopPUNK/__main__.py +++ b/PopPUNK/__main__.py @@ -238,7 +238,7 @@ def main(): # check if working with lineages if args.fit_model == 'lineage': rank_list = sorted([int(x) for x in args.ranks.split(',')]) - if min(rank_list) == 0 or max(rank_list) > 100: + if floor(min(rank_list)) == 0 or max(rank_list) > 1000: sys.stderr.write('Ranks should be small non-zero integers for sensible results\n') sys.exit(1) From ddcd4b767f93ea4559046ec89da97962802e981e Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 21:29:08 +0000 Subject: [PATCH 23/73] Replace floor with int --- PopPUNK/__main__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/__main__.py b/PopPUNK/__main__.py index 1bb47db9..9c91971c 100644 --- a/PopPUNK/__main__.py +++ b/PopPUNK/__main__.py @@ -238,7 +238,7 @@ def main(): # check if working with lineages if args.fit_model == 'lineage': rank_list = sorted([int(x) for x in args.ranks.split(',')]) - if floor(min(rank_list)) == 0 or max(rank_list) > 1000: + if int(min(rank_list)) == 0 or max(rank_list) > 1000: sys.stderr.write('Ranks should be small non-zero integers for sensible results\n') sys.exit(1) From 9bc521f90684ecc5ccc7ca41355ef230e1604f50 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 21:34:31 +0000 Subject: [PATCH 24/73] Add explanation of phylogency construction code --- PopPUNK/network.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index b35c43dc..b7ebfc59 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -796,10 +796,12 @@ def generate_minimum_spanning_tree(G, names, distMat): else: network_edge_weights = [1.0]*len(mst_network.get_edges()) for i, edge in enumerate(mst_network.get_edges()): - # Connectivity + # Connectivity - add both directions as unrooted tree is not directional - + # do not know which will be leaf node tree_edges[edge[0]].append(edge[1]) tree_edges[edge[1]].append(edge[0]) - # Length + # Lengths added in the same order as the corresponding children to enable + # branches to be matched to child nodes tree_edge_lengths[edge[0]].append(network_edge_weights[i]) tree_edge_lengths[edge[1]].append(network_edge_weights[i]) # Identify seed node as that with most links From ed21378247a3701a7e52a3c48b01b0c31bc5e568 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:01:23 +0000 Subject: [PATCH 25/73] Edit whitespace in network.py Co-authored-by: John Lees --- PopPUNK/network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index b7ebfc59..8ceaefed 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -817,7 +817,7 @@ def generate_minimum_spanning_tree(G, names, distMat): added_node = set(parent_node_indices) i = 0 while i < len(parent_node_indices): - for x,child_node_index in enumerate(tree_edges[parent_node_indices[i]]): + for x, child_node_index in enumerate(tree_edges[parent_node_indices[i]]): if child_node_index not in added_node: tree_nodes[parent_node_indices[i]].add_child(tree_nodes[child_node_index]) tree_nodes[child_node_index].edge_length = tree_edge_lengths[parent_node_indices[i]][x] From fb477cab381ee33890f688581f5a670c5dc97c6f Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:02:00 +0000 Subject: [PATCH 26/73] Command reformat in network.py Co-authored-by: John Lees --- PopPUNK/network.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 8ceaefed..87b4326f 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -832,6 +832,8 @@ def generate_minimum_spanning_tree(G, names, distMat): node.add_child(new_child) # Return tree as string sys.stderr.write("Completed calculation of minimum-spanning tree\n") - tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) + tree_string = tree.as_string(schema="newick", + suppress_rooting=True, + unquoted_underscores=True) return tree_string From ecdc25172486fb66e4ccd1ea9270ebe1318f1314 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:02:26 +0000 Subject: [PATCH 27/73] Edit whitespace in network.py Co-authored-by: John Lees --- PopPUNK/network.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 87b4326f..89bf2141 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -827,7 +827,7 @@ def generate_minimum_spanning_tree(G, names, distMat): # Add zero length branches for internal nodes in MST for node in tree.preorder_node_iter(): if not node.is_leaf(): - new_child = dendropy.Node(taxon=node.taxon,edge_length=0.0) + new_child = dendropy.Node(taxon=node.taxon, edge_length=0.0) node.taxon = None node.add_child(new_child) # Return tree as string From 48bbcf8c5026a5862a0ee14cb7926d2f939e2cf5 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:05:02 +0000 Subject: [PATCH 28/73] Remove unused code from plot.py Co-authored-by: John Lees --- PopPUNK/plot.py | 1 - 1 file changed, 1 deletion(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 5ebcf6d7..8531aaf7 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -747,7 +747,6 @@ def write_tree(tree, prefix, suffix, overwrite): overwrite (bool) Whether to overwrite existing files """ - fn = prefix + '/' + prefix tree_filename = prefix + "/" + os.path.basename(prefix) + suffix if overwrite or not os.path.isfile(tree_filename): with open(tree_filename,'w') as tree_file: From 42b66b39266c3e8899affb6fbe9966fe59fa17de Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:07:45 +0000 Subject: [PATCH 29/73] Edit whitespace in network.py Co-authored-by: John Lees --- PopPUNK/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 8531aaf7..6f3f3b33 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -749,7 +749,7 @@ def write_tree(tree, prefix, suffix, overwrite): """ tree_filename = prefix + "/" + os.path.basename(prefix) + suffix if overwrite or not os.path.isfile(tree_filename): - with open(tree_filename,'w') as tree_file: + with open(tree_filename, 'w') as tree_file: tree_file.write(tree) def check_tree_exists(prefix, type): From 682acc41d59b82281ad360a00446577237438519 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:08:39 +0000 Subject: [PATCH 30/73] Reformat command in plot.py Co-authored-by: John Lees --- PopPUNK/plot.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 6f3f3b33..35a671c6 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -771,7 +771,9 @@ def check_tree_exists(prefix, type): tree_fn = tree_prefix + suffix if os.path.isfile(tree_fn): tree = dendropy.Tree.get(path=tree_fn, schema="newick") - tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) + tree_string = tree.as_string(schema="newick", + suppress_rooting=True, + unquoted_underscores=True) sys.stderr.write("Reading existing tree from " + tree_fn + "\n") break From 7f9a1cf97ce259fbaf3017a1816d6c9fbcd370ea Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:11:59 +0000 Subject: [PATCH 31/73] Remove unused variables from plot.py Co-authored-by: John Lees --- PopPUNK/plot.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 35a671c6..ecbed467 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -817,8 +817,6 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): tree = pdm.nj_tree() # Midpoint root tree and write outout - suppress_rooting=True, - unquoted_underscores=True tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False) # remove file as it can be large From caa5af86f2f1f2c65ca4164546bd7049f1f6305f Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:12:47 +0000 Subject: [PATCH 32/73] Reformat command in plot.py Co-authored-by: John Lees --- PopPUNK/plot.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index ecbed467..c66841f9 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -823,7 +823,9 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): os.remove(core_dist_file) # return Newick string - tree_string = tree.as_string(schema="newick",suppress_rooting=True,unquoted_underscores=True) + tree_string = tree.as_string(schema="newick", + suppress_rooting=True, + unquoted_underscores=True) return tree_string def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, From 2ee963ebcfec633f46c65dd712edb10170cb0b9c Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:19:13 +0000 Subject: [PATCH 33/73] Change name of check_tree_exists function --- PopPUNK/plot.py | 2 +- PopPUNK/visualise.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 5ebcf6d7..fb386975 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -753,7 +753,7 @@ def write_tree(tree, prefix, suffix, overwrite): with open(tree_filename,'w') as tree_file: tree_file.write(tree) -def check_tree_exists(prefix, type): +def load_tree(prefix, type): """Checks for existing trees from previous runs. Args: diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index c616c91f..6b1c947d 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -26,7 +26,7 @@ from .plot import outputsForGrapetree from .plot import writeClusterCsv from .plot import generate_nj_tree -from .plot import check_tree_exists +from .plot import load_tree from .prune_db import prune_distance_matrix From 4e63aca8530bb4872818882b8bf5b19b0b6be633 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:34:26 +0000 Subject: [PATCH 34/73] Fix write_tree docstring --- PopPUNK/plot.py | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index b2364965..2398f914 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -733,9 +733,7 @@ def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, p generate_tsne(seqLabels, accMat, perplexity, outPrefix, overwrite) def write_tree(tree, prefix, suffix, overwrite): - """Generate phylogeny using dendropy or RapidNJ - - Writes a neighbour joining tree (.nwk) from core distances. + """Prints a Newick-formatted string to an output file Args: tree (str) From ae9548ecefce17839df9ec6de01e5d76f6c2db2a Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:36:05 +0000 Subject: [PATCH 35/73] Add error message to write_tree --- PopPUNK/plot.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 2398f914..612b2474 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -749,6 +749,8 @@ def write_tree(tree, prefix, suffix, overwrite): if overwrite or not os.path.isfile(tree_filename): with open(tree_filename, 'w') as tree_file: tree_file.write(tree) + else: + sys.stderr.write("Unable to write phylogeny to " + tree_filename + "\n") def load_tree(prefix, type): """Checks for existing trees from previous runs. From 76e27bde5898cd623b6acc78f2d2c35388e872b6 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:37:39 +0000 Subject: [PATCH 36/73] Remove unused MST code --- PopPUNK/visualise.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 6b1c947d..50be2efa 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -364,14 +364,6 @@ def generate_visualisations(query_db, weights=combined_dists, weights_type='core') sys.stderr.write("Completed constructing complete graph\n") - #### Alternative approach - slower on a small network -# import graph_tool.all as gt - # G = gt.complete_graph(len(combined_seq)) - # eprop_dist = G.new_edge_property("double") - # for row_idx, (ref, query) in enumerate(listDistInts(combined_seq, - # combined_seq, - # self = True)): - # eprop_dist[G.edge(ref,query)] = combined_dists[row_idx, 0] mst_tree = generate_minimum_spanning_tree(G, combined_seq, rr_distMat) else: mst_tree = existing_tree From 7e5aa73b3a61daf59046dee5250f9ceab7f8bb31 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Mon, 11 Jan 2021 22:43:33 +0000 Subject: [PATCH 37/73] Change variable name in network.py --- PopPUNK/network.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 89bf2141..2f47e934 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -814,14 +814,14 @@ def generate_minimum_spanning_tree(G, names, distMat): tree.seed_node = tree_nodes[seed_node_index] # Generate links of tree parent_node_indices = [seed_node_index] - added_node = set(parent_node_indices) + added_nodes = set(parent_node_indices) i = 0 while i < len(parent_node_indices): for x, child_node_index in enumerate(tree_edges[parent_node_indices[i]]): - if child_node_index not in added_node: + if child_node_index not in added_nodes: tree_nodes[parent_node_indices[i]].add_child(tree_nodes[child_node_index]) tree_nodes[child_node_index].edge_length = tree_edge_lengths[parent_node_indices[i]][x] - added_node.add(child_node_index) + added_nodes.add(child_node_index) parent_node_indices.append(child_node_index) i = i + 1 # Add zero length branches for internal nodes in MST From 1b3b5bcaa36c8c7cc750f486243dde07e2d0cc1b Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Tue, 12 Jan 2021 06:18:44 +0000 Subject: [PATCH 38/73] Validate network distance type --- PopPUNK/network.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 2f47e934..0d21b622 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -289,8 +289,8 @@ def constructNetwork(rlist, qlist, assignments, within_label, If passed, the core,accessory distances for each assignment, which will be annotated as an edge attribute weights_type (str) - Specifies the type of weight to be annotated on the graph - core, accessory - or Euclidean distance + Specifies the type of weight to be annotated on the graph - options are core, + accessory or euclidean distance sparse (bool) Whether input is a sparse distance matrix sparse_input (numpy.array) @@ -310,6 +310,13 @@ def constructNetwork(rlist, qlist, assignments, within_label, self_comparison = False vertex_labels.append(qlist) + # Check weights type is valid + accepted_weights_types = ["core", "accessory", "euclidean"] + if weights_type not in accepted_weights_types: + sys.stderr.write("Unable to calculate distance type " + str(weights_type) + "; " + "accepted types are " + str(accepted_weights_types) + "\n") + sys.exit(1) + # identify edges if edge_list or sparse: if sparse: From 8fec15c27825be543f356289c5204362ea728f53 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 15 Jan 2021 16:17:20 +0000 Subject: [PATCH 39/73] Propagate tree loading function name change --- PopPUNK/visualise.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 50be2efa..a841fbd4 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -330,7 +330,7 @@ def generate_visualisations(query_db, if mst: existing_tree = None if not overwrite: - existing_tree = check_tree_exists(output, "MST") + existing_tree = load_tree(output, "MST") if existing_tree is None: G = None if sparse_distMat is not None: @@ -373,7 +373,7 @@ def generate_visualisations(query_db, if (microreact or phandango or grapetree) and not no_nj: existing_tree = None if not overwrite: - existing_tree = check_tree_exists(output, "NJ") + existing_tree = load_tree(output, "NJ") if existing_tree is None: nj_tree = generate_nj_tree(core_distMat, combined_seq, From a5f931a0281b1b0f001837ccbe56fe5fe5e27b0c Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 15 Jan 2021 16:37:55 +0000 Subject: [PATCH 40/73] Change tree output options --- PopPUNK/visualise.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index a841fbd4..9236ec70 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -119,7 +119,8 @@ def get_options(): faGroup.add_argument('--grapetree', help='Generate phylogeny and CSV for grapetree visualisation', default=False, action='store_true') faGroup.add_argument('--mst', help='Calculate a minimum spanning tree', default=False, action='store_true') faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') - faGroup.add_argument('--no-nj', help='Do no calculate an NJ tree', default=False, action='store_true') + faGroup.add_argument('--tree', help='Do no calculate an NJ tree', type=str, default='nj', + choices=['nj', 'mst', 'both']) faGroup.add_argument('--perplexity', type=float, default = 20.0, help='Perplexity used to calculate t-SNE projection (with --microreact) [default=20.0]') @@ -172,8 +173,7 @@ def generate_visualisations(query_db, previous_query_clustering, info_csv, rapidnj, - no_nj, - mst, + tree, overwrite, core_only, accessory_only, @@ -327,7 +327,7 @@ def generate_visualisations(query_db, # Generate MST mst_tree = None - if mst: + if tree == 'mst' or tree == 'both': existing_tree = None if not overwrite: existing_tree = load_tree(output, "MST") @@ -370,7 +370,7 @@ def generate_visualisations(query_db, # Generate NJ tree nj_tree = None - if (microreact or phandango or grapetree) and not no_nj: + if tree == 'nj' or tree == 'both': existing_tree = None if not overwrite: existing_tree = load_tree(output, "NJ") @@ -454,8 +454,7 @@ def main(): args.previous_query_clustering, args.info_csv, args.rapidnj, - args.no_nj, - args.mst, + args.tree, args.overwrite, args.core_only, args.accessory_only, From 8e50745192cf4cfe392c089ba3d3dba14c29fc70 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 15 Jan 2021 16:49:48 +0000 Subject: [PATCH 41/73] Separate phylogenetic methods into trees.py --- PopPUNK/plot.py | 153 +--------------------------------------- PopPUNK/trees.py | 161 +++++++++++++++++++++++++++++++++++++++++++ PopPUNK/visualise.py | 4 +- 3 files changed, 165 insertions(+), 153 deletions(-) create mode 100644 PopPUNK/trees.py diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 612b2474..60da8244 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -26,6 +26,8 @@ from sklearn.neighbors.kde import KernelDensity import dendropy +from .trees import write_tree + from .utils import isolateNameToLabel def plot_scatter(X, out_prefix, title, kde = True): @@ -624,61 +626,6 @@ def writeClusterCsv(outfile, nodeNames, nodeLabels, clustering, col + "(length of " + this_col_items + ")\n") sys.exit(1) -def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, threads = 1): - """Use rapidNJ for more rapid tree building - - Creates a phylip of core distances, system call to rapidnj executable, loads tree as - dendropy object (cleaning quotes in node names), removes temporary files. - - Args: - rapidnj (str) - Location of rapidnj executable - refList (list) - Names of sequences in coreMat (same order) - coreMat (numpy.array) - NxN core distance matrix produced in :func:`~outputsForMicroreact` - outPrefix (str) - Prefix for all generated output files, which will be placed in `outPrefix` subdirectory - threads (int) - Number of threads to use - - Returns: - tree (str) - Newick-formatted NJ tree from core distances - """ - # generate phylip matrix - phylip_name = outPrefix + "/" + os.path.basename(outPrefix) + "_core_distances.phylip" - with open(phylip_name, 'w') as pFile: - pFile.write(str(len(refList))+"\n") - for coreDist, ref in zip(coreMat, refList): - pFile.write(ref) - pFile.write(' '+' '.join(map(str, coreDist))) - pFile.write("\n") - - # construct tree - tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + "_core_NJ.nwk" - rapidnj_cmd = rapidnj + " " + phylip_name + " -n -i pd -o t -x " + tree_filename + ".raw -c " + str(threads) - try: - # run command - subprocess.run(rapidnj_cmd, shell=True, check=True) - - # remove quotation marks for microreact - with open(tree_filename + ".raw", 'r') as f, open(tree_filename, 'w') as fo: - for line in f: - fo.write(line.replace("'", '')) - # tidy unnecessary files - os.remove(tree_filename+".raw") - os.remove(phylip_name) - - # record errors - except subprocess.CalledProcessError as e: - sys.stderr.write("Could not run command " + rapidnj_cmd + "; returned code: " + str(e.returncode) + "\n") - sys.exit(1) - - # read tree and return - tree = dendropy.Tree.get(path=tree_filename, schema="newick") - return tree - def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, perplexity, outPrefix, epiCsv, queryList = None, overwrite = False): """Generate files for microreact @@ -732,102 +679,6 @@ def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, p # write the phylogeny .nwk; t-SNE network .dot; clusters + data .csv generate_tsne(seqLabels, accMat, perplexity, outPrefix, overwrite) -def write_tree(tree, prefix, suffix, overwrite): - """Prints a Newick-formatted string to an output file - - Args: - tree (str) - Newick-formatted string representation of tree - prefix (str) - Prefix for output files - suffix (str) - Suffix for output files - overwrite (bool) - Whether to overwrite existing files - """ - tree_filename = prefix + "/" + os.path.basename(prefix) + suffix - if overwrite or not os.path.isfile(tree_filename): - with open(tree_filename, 'w') as tree_file: - tree_file.write(tree) - else: - sys.stderr.write("Unable to write phylogeny to " + tree_filename + "\n") - -def load_tree(prefix, type): - """Checks for existing trees from previous runs. - - Args: - prefix (str) - Output prefix used for search - type (str) - Type of tree (NJ or MST) - - Returns: - tree_string (str) - Newick-formatted string of NJ tree - """ - tree_string = None - tree_prefix = prefix + "/" + os.path.basename(prefix) - for suffix in ["_core_" + type + ".tree","_core_" + type + ".nwk"]: - tree_fn = tree_prefix + suffix - if os.path.isfile(tree_fn): - tree = dendropy.Tree.get(path=tree_fn, schema="newick") - tree_string = tree.as_string(schema="newick", - suppress_rooting=True, - unquoted_underscores=True) - sys.stderr.write("Reading existing tree from " + tree_fn + "\n") - break - - return tree_string - -def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): - """Generate phylogeny using dendropy or RapidNJ - - Writes a neighbour joining tree (.nwk) from core distances. - - Args: - coreMat (numpy.array) - n x n array of core distances for n samples. - seqLabels (list) - Processed names of sequences being analysed. - outPrefix (str) - Output prefix for core distances file - rapidnj (str) - A string with the location of the rapidnj executable for tree-building. If None, will - use dendropy by default - threads (int) - Number of threads to use with rapidnj - - Returns: - tree_string (str) - Newick-formatted string of NJ tree - """ - # Save distances to file - core_dist_file = outPrefix + "/" + os.path.basename(outPrefix) + "_core_dists.csv" - np.savetxt(core_dist_file, coreMat, delimiter=",", header = ",".join(seqLabels), comments="") - - # calculate phylogeny - sys.stderr.write("Building phylogeny\n") - if rapidnj is not None: - tree = buildRapidNJ(rapidnj, seqLabels, coreMat, outPrefix, threads = threads) - else: - pdm = dendropy.PhylogeneticDistanceMatrix.from_csv(src=open(core_dist_file), - delimiter=",", - is_first_row_column_names=True, - is_first_column_row_names=False) - tree = pdm.nj_tree() - - # Midpoint root tree and write outout - tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False) - - # remove file as it can be large - os.remove(core_dist_file) - - # return Newick string - tree_string = tree.as_string(schema="newick", - suppress_rooting=True, - unquoted_underscores=True) - return tree_string - def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, queryList = None, overwrite = False): """Generate files for Phandango diff --git a/PopPUNK/trees.py b/PopPUNK/trees.py new file mode 100644 index 00000000..c3bbbc83 --- /dev/null +++ b/PopPUNK/trees.py @@ -0,0 +1,161 @@ +# vim: set fileencoding= : +# Copyright 2018-2020 John Lees and Nick Croucher + +'''Functions for construction and processing of trees''' + +import sys +import os +import subprocess +import numpy as np +import dendropy + +def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, threads = 1): + """Use rapidNJ for more rapid tree building + + Creates a phylip of core distances, system call to rapidnj executable, loads tree as + dendropy object (cleaning quotes in node names), removes temporary files. + + Args: + rapidnj (str) + Location of rapidnj executable + refList (list) + Names of sequences in coreMat (same order) + coreMat (numpy.array) + NxN core distance matrix produced in :func:`~outputsForMicroreact` + outPrefix (str) + Prefix for all generated output files, which will be placed in `outPrefix` subdirectory + threads (int) + Number of threads to use + + Returns: + tree (str) + Newick-formatted NJ tree from core distances + """ + # generate phylip matrix + phylip_name = outPrefix + "/" + os.path.basename(outPrefix) + "_core_distances.phylip" + with open(phylip_name, 'w') as pFile: + pFile.write(str(len(refList))+"\n") + for coreDist, ref in zip(coreMat, refList): + pFile.write(ref) + pFile.write(' '+' '.join(map(str, coreDist))) + pFile.write("\n") + + # construct tree + tree_filename = outPrefix + "/" + os.path.basename(outPrefix) + "_core_NJ.nwk" + rapidnj_cmd = rapidnj + " " + phylip_name + " -n -i pd -o t -x " + tree_filename + ".raw -c " + str(threads) + try: + # run command + subprocess.run(rapidnj_cmd, shell=True, check=True) + + # remove quotation marks for microreact + with open(tree_filename + ".raw", 'r') as f, open(tree_filename, 'w') as fo: + for line in f: + fo.write(line.replace("'", '')) + # tidy unnecessary files + os.remove(tree_filename+".raw") + os.remove(phylip_name) + + # record errors + except subprocess.CalledProcessError as e: + sys.stderr.write("Could not run command " + rapidnj_cmd + "; returned code: " + str(e.returncode) + "\n") + sys.exit(1) + + # read tree and return + tree = dendropy.Tree.get(path=tree_filename, schema="newick") + return tree + +def write_tree(tree, prefix, suffix, overwrite): + """Prints a Newick-formatted string to an output file + + Args: + tree (str) + Newick-formatted string representation of tree + prefix (str) + Prefix for output files + suffix (str) + Suffix for output files + overwrite (bool) + Whether to overwrite existing files + """ + tree_filename = prefix + "/" + os.path.basename(prefix) + suffix + if overwrite or not os.path.isfile(tree_filename): + with open(tree_filename, 'w') as tree_file: + tree_file.write(tree) + else: + sys.stderr.write("Unable to write phylogeny to " + tree_filename + "\n") + +def load_tree(prefix, type): + """Checks for existing trees from previous runs. + + Args: + prefix (str) + Output prefix used for search + type (str) + Type of tree (NJ or MST) + + Returns: + tree_string (str) + Newick-formatted string of NJ tree + """ + tree_string = None + tree_prefix = prefix + "/" + os.path.basename(prefix) + for suffix in ["_core_" + type + ".tree","_core_" + type + ".nwk"]: + tree_fn = tree_prefix + suffix + if os.path.isfile(tree_fn): + tree = dendropy.Tree.get(path=tree_fn, schema="newick") + tree_string = tree.as_string(schema="newick", + suppress_rooting=True, + unquoted_underscores=True) + sys.stderr.write("Reading existing tree from " + tree_fn + "\n") + break + + return tree_string + +def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): + """Generate phylogeny using dendropy or RapidNJ + + Writes a neighbour joining tree (.nwk) from core distances. + + Args: + coreMat (numpy.array) + n x n array of core distances for n samples. + seqLabels (list) + Processed names of sequences being analysed. + outPrefix (str) + Output prefix for core distances file + rapidnj (str) + A string with the location of the rapidnj executable for tree-building. If None, will + use dendropy by default + threads (int) + Number of threads to use with rapidnj + + Returns: + tree_string (str) + Newick-formatted string of NJ tree + """ + # Save distances to file + core_dist_file = outPrefix + "/" + os.path.basename(outPrefix) + "_core_dists.csv" + np.savetxt(core_dist_file, coreMat, delimiter=",", header = ",".join(seqLabels), comments="") + + # calculate phylogeny + sys.stderr.write("Building phylogeny\n") + if rapidnj is not None: + tree = buildRapidNJ(rapidnj, seqLabels, coreMat, outPrefix, threads = threads) + else: + pdm = dendropy.PhylogeneticDistanceMatrix.from_csv(src=open(core_dist_file), + delimiter=",", + is_first_row_column_names=True, + is_first_column_row_names=False) + tree = pdm.nj_tree() + + # Midpoint root tree and write outout + tree.reroot_at_midpoint(update_bipartitions=True, suppress_unifurcations=False) + + # remove file as it can be large + os.remove(core_dist_file) + + # return Newick string + tree_string = tree.as_string(schema="newick", + suppress_rooting=True, + unquoted_underscores=True) + return tree_string diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 9236ec70..ca140cf0 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -25,8 +25,6 @@ from .plot import outputsForPhandango from .plot import outputsForGrapetree from .plot import writeClusterCsv -from .plot import generate_nj_tree -from .plot import load_tree from .prune_db import prune_distance_matrix @@ -34,6 +32,8 @@ from .sketchlib import getKmersFromReferenceDatabase from .sketchlib import addRandom +from .trees import load_tree, generate_nj_tree + from .utils import readPickle from .utils import setGtThreads from .utils import update_distance_matrices From ce72382f0642c458e50b149d315b57f1c6d12705 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 15 Jan 2021 16:52:33 +0000 Subject: [PATCH 42/73] Change file path for loading tree --- PopPUNK/trees.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/trees.py b/PopPUNK/trees.py index c3bbbc83..55ebcea6 100644 --- a/PopPUNK/trees.py +++ b/PopPUNK/trees.py @@ -98,7 +98,7 @@ def load_tree(prefix, type): Newick-formatted string of NJ tree """ tree_string = None - tree_prefix = prefix + "/" + os.path.basename(prefix) + tree_prefix = os.path.join(prefix,os.path.basename(prefix)) for suffix in ["_core_" + type + ".tree","_core_" + type + ".nwk"]: tree_fn = tree_prefix + suffix if os.path.isfile(tree_fn): From 25db4e1cc731d713ff135cc972b1813202ee67d7 Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 15 Jan 2021 16:55:59 +0000 Subject: [PATCH 43/73] Remove unnecessary dendropy imports --- PopPUNK/plot.py | 1 - PopPUNK/visualise.py | 1 - 2 files changed, 2 deletions(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 60da8244..a8b953e0 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -24,7 +24,6 @@ from sklearn.neighbors import KernelDensity except ImportError: from sklearn.neighbors.kde import KernelDensity -import dendropy from .trees import write_tree diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index ca140cf0..dca41837 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -7,7 +7,6 @@ import sys # additional import numpy as np -import dendropy import scipy.sparse # required from v2.1.1 onwards (no mash support) From 24e57918753d3e65106244f3ad562d1ba4b1f04c Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Fri, 15 Jan 2021 17:21:55 +0000 Subject: [PATCH 44/73] Allow core or accessory distances to be used for MST --- PopPUNK/network.py | 17 ++++++++++++++--- PopPUNK/trees.py | 4 ++-- PopPUNK/visualise.py | 16 ++++++++++------ 3 files changed, 26 insertions(+), 11 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 0d21b622..de789695 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -732,7 +732,7 @@ def printExternalClusters(newClusters, extClusterFile, outPrefix, columns = ["sample"] + list(extClusters.keys()), index = False) -def generate_minimum_spanning_tree(G, names, distMat): +def generate_minimum_spanning_tree(G, names, distMat, distType): """Generate a minimum spanning tree from a network Args: @@ -740,12 +740,23 @@ def generate_minimum_spanning_tree(G, names, distMat): Graph tool network names (list) List of sequence names - + distMat (numpy array or scipy sparse matrix) + Distance matrix + distType (str) + Type of distance used to construct tree: core or accessory Returns: tree_string (str) Newick representation of the minimum spanning tree """ sys.stderr.write("Starting calculation of minimum-spanning tree\n") + # Define index of distances used to construct MST + if distType == 'core': + dist_index = 0 + elif distType == 'accessory': + dist_index = 1 + else: + sys.stderr.write('Unable to use distance type ' + str(distType) + '\n') + sys.exit(1) # Define sequences names for tree taxon_namespace = dendropy.TaxonNamespace(names) # Test if weighted network @@ -774,7 +785,7 @@ def generate_minimum_spanning_tree(G, names, distMat): connections = [] for row_idx, (ref, query) in enumerate(listDistInts(names, names, self = True)): if ref in seed_vertices and query in seed_vertices: - dist = distMat[row_idx, 0] + dist = distMat[row_idx, dist_index] edge_tuple = (ref, query, dist) connections.append(edge_tuple) # Construct graph diff --git a/PopPUNK/trees.py b/PopPUNK/trees.py index 55ebcea6..bf78e620 100644 --- a/PopPUNK/trees.py +++ b/PopPUNK/trees.py @@ -84,7 +84,7 @@ def write_tree(tree, prefix, suffix, overwrite): else: sys.stderr.write("Unable to write phylogeny to " + tree_filename + "\n") -def load_tree(prefix, type): +def load_tree(prefix, type, distances = 'core'): """Checks for existing trees from previous runs. Args: @@ -99,7 +99,7 @@ def load_tree(prefix, type): """ tree_string = None tree_prefix = os.path.join(prefix,os.path.basename(prefix)) - for suffix in ["_core_" + type + ".tree","_core_" + type + ".nwk"]: + for suffix in ['_' + distances + '_' + type + ".tree",'_' + distances + '_' + type + ".nwk"]: tree_fn = tree_prefix + suffix if os.path.isfile(tree_fn): tree = dendropy.Tree.get(path=tree_fn, schema="newick") diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index dca41837..58d15a3f 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -116,10 +116,12 @@ def get_options(): faGroup.add_argument('--cytoscape', help='Generate network output files for Cytoscape', default=False, action='store_true') faGroup.add_argument('--phandango', help='Generate phylogeny and TSV for Phandango visualisation', default=False, action='store_true') faGroup.add_argument('--grapetree', help='Generate phylogeny and CSV for grapetree visualisation', default=False, action='store_true') - faGroup.add_argument('--mst', help='Calculate a minimum spanning tree', default=False, action='store_true') - faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') - faGroup.add_argument('--tree', help='Do no calculate an NJ tree', type=str, default='nj', + faGroup.add_argument('--tree', help='Type of tree to calculate (nj, mst or both)', type=str, default='nj', choices=['nj', 'mst', 'both']) + faGroup.add_argument('--mst-distances', help='Distances used to calculate a minimum spanning tree', type=str, + default='core', choice=['core','accessory']) + faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') + faGroup.add_argument('--perplexity', type=float, default = 20.0, help='Perplexity used to calculate t-SNE projection (with --microreact) [default=20.0]') @@ -173,6 +175,7 @@ def generate_visualisations(query_db, info_csv, rapidnj, tree, + mst_distances, overwrite, core_only, accessory_only, @@ -329,7 +332,7 @@ def generate_visualisations(query_db, if tree == 'mst' or tree == 'both': existing_tree = None if not overwrite: - existing_tree = load_tree(output, "MST") + existing_tree = load_tree(output, "MST", distances=mst_distances) if existing_tree is None: G = None if sparse_distMat is not None: @@ -361,9 +364,9 @@ def generate_visualisations(query_db, 0, edge_list=False, weights=combined_dists, - weights_type='core') + weights_type=mst_distances) sys.stderr.write("Completed constructing complete graph\n") - mst_tree = generate_minimum_spanning_tree(G, combined_seq, rr_distMat) + mst_tree = generate_minimum_spanning_tree(G, combined_seq, rr_distMat, mst_distances) else: mst_tree = existing_tree @@ -454,6 +457,7 @@ def main(): args.info_csv, args.rapidnj, args.tree, + args.mst_distances, args.overwrite, args.core_only, args.accessory_only, From 9a82fe0e21a763764f7c530f0c0bc7409a14728f Mon Sep 17 00:00:00 2001 From: nickjcroucher Date: Sat, 16 Jan 2021 07:22:42 +0000 Subject: [PATCH 45/73] Fix typo in argument parsing --- PopPUNK/visualise.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 58d15a3f..746e5435 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -119,7 +119,7 @@ def get_options(): faGroup.add_argument('--tree', help='Type of tree to calculate (nj, mst or both)', type=str, default='nj', choices=['nj', 'mst', 'both']) faGroup.add_argument('--mst-distances', help='Distances used to calculate a minimum spanning tree', type=str, - default='core', choice=['core','accessory']) + default='core', choices=['core','accessory']) faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') faGroup.add_argument('--perplexity', From 465c4663d5ed689a89d95f4827fc3a5f498ab586 Mon Sep 17 00:00:00 2001 From: John Lees Date: Wed, 27 Jan 2021 12:21:13 +0000 Subject: [PATCH 46/73] Pin dendropy version --- environment.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/environment.yml b/environment.yml index e9b7f3fd..46c38610 100644 --- a/environment.yml +++ b/environment.yml @@ -11,7 +11,7 @@ dependencies: - python-dateutil - pandas - scikit-learn - - dendropy + - dendropy >=4.4.0 - matplotlib - mash - hdbscan From fa85b60cbf5ce36186e5618c7f3d944c3560a5fe Mon Sep 17 00:00:00 2001 From: John Lees Date: Wed, 27 Jan 2021 12:40:33 +0000 Subject: [PATCH 47/73] Remove MST with unweighted graph --- PopPUNK/network.py | 58 +++++++++++++++++++++++++--------------------- 1 file changed, 32 insertions(+), 26 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index de789695..6405c39b 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -757,18 +757,21 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): else: sys.stderr.write('Unable to use distance type ' + str(distType) + '\n') sys.exit(1) + + # + # Create MST + # + # Define sequences names for tree taxon_namespace = dendropy.TaxonNamespace(names) - # Test if weighted network - weighted_network = False + # Test if weighted network and calculate minimum spanning tree if "weight" in G.edge_properties: - weighted_network = True - # Calculate minimum spanning tree - if weighted_network: mst_edge_prop_map = gt.min_spanning_tree(G, weights = G.ep["weight"]) + mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) else: - mst_edge_prop_map = gt.min_spanning_tree(G) - mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) + sys.stderr.write("generate_minimum_spanning_tree requires a weighted graph\n") + raise RuntimeError("MST passed unweighted graph") + # Find seed nodes as those with greatest outdegree in each component seed_vertices = set() component_assignments, component_frequencies = gt.label_components(mst_network) @@ -779,6 +782,7 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): out_degrees = component.get_out_degrees(component_vertices) seed_vertex = list(component_vertices[np.where(out_degrees == np.amax(out_degrees))]) seed_vertices.add(seed_vertex[0]) # Can only add one otherwise not MST + # If multiple components, calculate distances between seed nodes if len(component_frequencies) > 1: # Extract distances @@ -791,28 +795,27 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): # Construct graph seed_G = gt.Graph(directed = False) seed_G.add_vertex(len(seed_vertex)) - if weighted_network: - eweight = seed_G.new_ep("float") - seed_G.add_edge_list(connections, eprops = [eweight]) - seed_G.edge_properties["weight"] = eweight - seed_mst_edge_prop_map = gt.min_spanning_tree(seed_G, weights = seed_G.ep["weight"]) - seed_mst_network = gt.GraphView(seed_G, efilt = seed_mst_edge_prop_map) - # Insert seed MST into original MST - may be possible to use graph_union with include=True & intersection - deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) - mst_network.add_edge_list(deep_edges) - else: - sys.stderr.write('I do not currently know what to do in this case\n') - # Initialise tree + eweight = seed_G.new_ep("float") + seed_G.add_edge_list(connections, eprops = [eweight]) + seed_G.edge_properties["weight"] = eweight + seed_mst_edge_prop_map = gt.min_spanning_tree(seed_G, weights = seed_G.ep["weight"]) + seed_mst_network = gt.GraphView(seed_G, efilt = seed_mst_edge_prop_map) + # Insert seed MST into original MST - may be possible to use graph_union with include=True & intersection + deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) + mst_network.add_edge_list(deep_edges) + + # + # MST graph -> phylogeny + # + + # Initialise tree and create nodes tree = dendropy.Tree(taxon_namespace=taxon_namespace) - # Create nodes tree_nodes = {v:dendropy.Node(taxon=taxon_namespace[int(v)]) for v in mst_network.get_vertices()} + # Identify edges tree_edges = {v:[] for v in tree_nodes.keys()} tree_edge_lengths = {v:[] for v in tree_nodes.keys()} - if weighted_network: - network_edge_weights = list(mst_network.ep["weight"]) - else: - network_edge_weights = [1.0]*len(mst_network.get_edges()) + network_edge_weights = list(mst_network.ep["weight"]) for i, edge in enumerate(mst_network.get_edges()): # Connectivity - add both directions as unrooted tree is not directional - # do not know which will be leaf node @@ -822,6 +825,7 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): # branches to be matched to child nodes tree_edge_lengths[edge[0]].append(network_edge_weights[i]) tree_edge_lengths[edge[1]].append(network_edge_weights[i]) + # Identify seed node as that with most links max_links = 0 seed_node_index = None @@ -830,11 +834,12 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): max_links = len(tree_edges[vertex]) seed_node_index = vertex tree.seed_node = tree_nodes[seed_node_index] + # Generate links of tree parent_node_indices = [seed_node_index] added_nodes = set(parent_node_indices) i = 0 - while i < len(parent_node_indices): + while i < len(parent_node_indices): # NB loop end will increase for x, child_node_index in enumerate(tree_edges[parent_node_indices[i]]): if child_node_index not in added_nodes: tree_nodes[parent_node_indices[i]].add_child(tree_nodes[child_node_index]) @@ -842,16 +847,17 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): added_nodes.add(child_node_index) parent_node_indices.append(child_node_index) i = i + 1 + # Add zero length branches for internal nodes in MST for node in tree.preorder_node_iter(): if not node.is_leaf(): new_child = dendropy.Node(taxon=node.taxon, edge_length=0.0) node.taxon = None node.add_child(new_child) + # Return tree as string sys.stderr.write("Completed calculation of minimum-spanning tree\n") tree_string = tree.as_string(schema="newick", suppress_rooting=True, unquoted_underscores=True) return tree_string - From 71fda358e4c178619e9b6124cf98afa4ada4756e Mon Sep 17 00:00:00 2001 From: John Lees Date: Wed, 27 Jan 2021 17:08:23 +0000 Subject: [PATCH 48/73] Remove sparse matrix mst --- PopPUNK/__main__.py | 9 +-- PopPUNK/network.py | 127 ++++++++++--------------------------------- PopPUNK/plot.py | 22 +++++++- PopPUNK/trees.py | 76 +++++++++++++++++++++++++- PopPUNK/visualise.py | 59 +++++++------------- 5 files changed, 146 insertions(+), 147 deletions(-) diff --git a/PopPUNK/__main__.py b/PopPUNK/__main__.py index 9c91971c..cfdbd86e 100644 --- a/PopPUNK/__main__.py +++ b/PopPUNK/__main__.py @@ -238,14 +238,15 @@ def main(): # check if working with lineages if args.fit_model == 'lineage': rank_list = sorted([int(x) for x in args.ranks.split(',')]) - if int(min(rank_list)) == 0 or max(rank_list) > 1000: - sys.stderr.write('Ranks should be small non-zero integers for sensible results\n') - sys.exit(1) + if int(min(rank_list)) == 0: + sys.stderr.write("Ranks must be >= 1\n") + if max(rank_list) > 100: + sys.stderr.write("WARNING: Ranks should be small non-zero integers for sensible lineage results\n") # run according to mode sys.stderr.write("PopPUNK (POPulation Partitioning Using Nucleotide Kmers)\n") sys.stderr.write("\t(with backend: " + dbFuncs['backend'] + " v" + dbFuncs['backend_version'] + "\n") - sys.stderr.write('\t sketchlib: ' + checkSketchlibLibrary() + ')\n') + sys.stderr.write("\t sketchlib: " + checkSketchlibLibrary() + ")\n") # Check on parallelisation of graph-tools setGtThreads(args.threads) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 6405c39b..c6064252 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -31,6 +31,8 @@ from .utils import setupDBFuncs from .utils import isolateNameToLabel +accepted_weights_types = ["core", "accessory", "euclidean"] + def fetchNetwork(network_dir, model, refList, ref_graph = False, core_only = False, accessory_only = False): """Load the network based on input options @@ -291,8 +293,6 @@ def constructNetwork(rlist, qlist, assignments, within_label, weights_type (str) Specifies the type of weight to be annotated on the graph - options are core, accessory or euclidean distance - sparse (bool) - Whether input is a sparse distance matrix sparse_input (numpy.array) Sparse distance matrix from lineage fit @@ -311,24 +311,24 @@ def constructNetwork(rlist, qlist, assignments, within_label, vertex_labels.append(qlist) # Check weights type is valid - accepted_weights_types = ["core", "accessory", "euclidean"] if weights_type not in accepted_weights_types: sys.stderr.write("Unable to calculate distance type " + str(weights_type) + "; " - "accepted types are " + str(accepted_weights_types) + "\n") + "accepted types are " + str(accepted_weights_types) + "\n") sys.exit(1) + if edge_list and sparse_input: + raise RuntimeError("Cannot construct network from edge list and sparse matrix") # identify edges - if edge_list or sparse: - if sparse: - connections = [] - for ref, query, weight in zip(sparse_input.row, sparse_input.col, sparse_input.data): - connections.append((ref, query, weight)) - elif weights is not None: - connections = [] + connections = [] + if edge_list: + if weights != None: for weight, (ref, query) in zip(weights, assignments): connections.append((ref, query, weight)) else: connections = assignments + elif sparse_input != None: + for ref, query, weight in zip(sparse_input.row, sparse_input.col, sparse_input.data): + connections.append((ref, query, weight)) else: for row_idx, (assignment, (ref, query)) in enumerate(zip(assignments, listDistInts(rlist, qlist, @@ -341,10 +341,6 @@ def constructNetwork(rlist, qlist, assignments, within_label, dist = weights[row_idx, 0] elif weights_type == 'accessory': dist = weights[row_idx, 1] - else: - sys.stderr.write('Unable to parse network weight type ' + weights_type + '; ' - 'options are core, accessory or euclidean') - sys.exit(1) edge_tuple = (ref, query, dist) else: edge_tuple = (ref, query) @@ -732,38 +728,22 @@ def printExternalClusters(newClusters, extClusterFile, outPrefix, columns = ["sample"] + list(extClusters.keys()), index = False) -def generate_minimum_spanning_tree(G, names, distMat, distType): +def generate_minimum_spanning_tree(G): """Generate a minimum spanning tree from a network Args: G (network) Graph tool network - names (list) - List of sequence names - distMat (numpy array or scipy sparse matrix) - Distance matrix - distType (str) - Type of distance used to construct tree: core or accessory + Returns: - tree_string (str) - Newick representation of the minimum spanning tree + mst_network (str) + Minimum spanning tree (as graph-tool graph) """ - sys.stderr.write("Starting calculation of minimum-spanning tree\n") - # Define index of distances used to construct MST - if distType == 'core': - dist_index = 0 - elif distType == 'accessory': - dist_index = 1 - else: - sys.stderr.write('Unable to use distance type ' + str(distType) + '\n') - sys.exit(1) - # # Create MST # + sys.stderr.write("Starting calculation of minimum-spanning tree\n") - # Define sequences names for tree - taxon_namespace = dendropy.TaxonNamespace(names) # Test if weighted network and calculate minimum spanning tree if "weight" in G.edge_properties: mst_edge_prop_map = gt.min_spanning_tree(G, weights = G.ep["weight"]) @@ -787,11 +767,19 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): if len(component_frequencies) > 1: # Extract distances connections = [] - for row_idx, (ref, query) in enumerate(listDistInts(names, names, self = True)): - if ref in seed_vertices and query in seed_vertices: - dist = distMat[row_idx, dist_index] - edge_tuple = (ref, query, dist) - connections.append(edge_tuple) + max_weight = np.max(G.edge_properties["weight"].a) + for ref in seed_vertices: + seed_edges = G.get_all_edges(ref, ["weight"]) + found = False # Not all edges may be in graph + for seed_edge in seed_edges: + if seed_edge[1] in seed_vertices: + found = True + connections.append((seed_edge)) + if found == False: + for query in seed_vertices: + if query != ref: + connections.append((ref, query, max_weight)) + # Construct graph seed_G = gt.Graph(directed = False) seed_G.add_vertex(len(seed_vertex)) @@ -804,60 +792,5 @@ def generate_minimum_spanning_tree(G, names, distMat, distType): deep_edges = seed_mst_network.get_edges([seed_mst_network.ep["weight"]]) mst_network.add_edge_list(deep_edges) - # - # MST graph -> phylogeny - # - - # Initialise tree and create nodes - tree = dendropy.Tree(taxon_namespace=taxon_namespace) - tree_nodes = {v:dendropy.Node(taxon=taxon_namespace[int(v)]) for v in mst_network.get_vertices()} - - # Identify edges - tree_edges = {v:[] for v in tree_nodes.keys()} - tree_edge_lengths = {v:[] for v in tree_nodes.keys()} - network_edge_weights = list(mst_network.ep["weight"]) - for i, edge in enumerate(mst_network.get_edges()): - # Connectivity - add both directions as unrooted tree is not directional - - # do not know which will be leaf node - tree_edges[edge[0]].append(edge[1]) - tree_edges[edge[1]].append(edge[0]) - # Lengths added in the same order as the corresponding children to enable - # branches to be matched to child nodes - tree_edge_lengths[edge[0]].append(network_edge_weights[i]) - tree_edge_lengths[edge[1]].append(network_edge_weights[i]) - - # Identify seed node as that with most links - max_links = 0 - seed_node_index = None - for vertex in tree_edges.keys(): - if len(tree_edges[vertex]) > max_links: - max_links = len(tree_edges[vertex]) - seed_node_index = vertex - tree.seed_node = tree_nodes[seed_node_index] - - # Generate links of tree - parent_node_indices = [seed_node_index] - added_nodes = set(parent_node_indices) - i = 0 - while i < len(parent_node_indices): # NB loop end will increase - for x, child_node_index in enumerate(tree_edges[parent_node_indices[i]]): - if child_node_index not in added_nodes: - tree_nodes[parent_node_indices[i]].add_child(tree_nodes[child_node_index]) - tree_nodes[child_node_index].edge_length = tree_edge_lengths[parent_node_indices[i]][x] - added_nodes.add(child_node_index) - parent_node_indices.append(child_node_index) - i = i + 1 - - # Add zero length branches for internal nodes in MST - for node in tree.preorder_node_iter(): - if not node.is_leaf(): - new_child = dendropy.Node(taxon=node.taxon, edge_length=0.0) - node.taxon = None - node.add_child(new_child) - - # Return tree as string sys.stderr.write("Completed calculation of minimum-spanning tree\n") - tree_string = tree.as_string(schema="newick", - suppress_rooting=True, - unquoted_underscores=True) - return tree_string + return mst_network diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index a8b953e0..9cdfac16 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -25,7 +25,7 @@ except ImportError: from sklearn.neighbors.kde import KernelDensity -from .trees import write_tree +from .trees import write_tree, mst_to_phylogeny from .utils import isolateNameToLabel @@ -404,7 +404,21 @@ def distHistogram(dists, rank, outPrefix): "_rank_" + str(rank) + "_histogram.png") plt.close() -def outputsForCytoscape(G, clustering, outPrefix, epiCsv, queryList = None, suffix = None, writeCsv = True, viz_subset = None): +def drawMST(mst, outPrefix): + pos = gt.sfdp_layout(mst, eweight = mst.edge_properties["weight"]) + deg = g.degree_property_map("in") + deg.a = 4 * (sqrt(deg.a) * 0.5 + 0.4) + ebet = gt.betweenness(g)[1] + ebet.a /= ebet.a.max() / 10. + eorder = ebet.copy() + eorder.a *= -1 + graph_file_name = outPrefix + "/" + os.path.basename(outPrefix) + "_mst_plot.png" + gt.graph_draw(mst, pos=pos, vertex_size=deg, vertex_fill_color=deg, vorder=deg, + edge_color=ebet, eorder=eorder, edge_pen_width=ebet, + output=graph_file_name, output_szie=(1500, 1500)) + +def outputsForCytoscape(G, clustering, outPrefix, epiCsv, queryList = None, + suffix = None, writeCsv = True, viz_subset = None): """Write outputs for cytoscape. A graphml of the network, and CSV with metadata Args: @@ -673,7 +687,9 @@ def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, p # write MST if mst_tree is not None: - write_tree(mst_tree, outPrefix, "_core_MST.nwk", overwrite) + drawMST(mst_tree, outPrefix) + mst_as_tree = mst_to_phylogeny(mst_tree, seqLabels) + write_tree(mst_as_tree, outPrefix, "_core_MST.nwk", overwrite) # write the phylogeny .nwk; t-SNE network .dot; clusters + data .csv generate_tsne(seqLabels, accMat, perplexity, outPrefix, overwrite) diff --git a/PopPUNK/trees.py b/PopPUNK/trees.py index bf78e620..8f4b9672 100644 --- a/PopPUNK/trees.py +++ b/PopPUNK/trees.py @@ -92,7 +92,7 @@ def load_tree(prefix, type, distances = 'core'): Output prefix used for search type (str) Type of tree (NJ or MST) - + Returns: tree_string (str) Newick-formatted string of NJ tree @@ -128,7 +128,7 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): use dendropy by default threads (int) Number of threads to use with rapidnj - + Returns: tree_string (str) Newick-formatted string of NJ tree @@ -153,9 +153,79 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): # remove file as it can be large os.remove(core_dist_file) - + # return Newick string tree_string = tree.as_string(schema="newick", suppress_rooting=True, unquoted_underscores=True) return tree_string + +def mst_to_phylogeny(mst_network, names): + """Convert a MST graph to a phylogeny + + Args: + mst_network (network) + Minimum spanning tree from + :func:`~PopPUNK.network.generate_minimum_spanning_tree` + + Returns: + mst_network (str) + Minimum spanning tree (as graph-tool graph) + """ + # + # MST graph -> phylogeny + # + # Define sequences names for tree + taxon_namespace = dendropy.TaxonNamespace(names) + # Initialise tree and create nodes + tree = dendropy.Tree(taxon_namespace=taxon_namespace) + tree_nodes = {v:dendropy.Node(taxon=taxon_namespace[int(v)]) for v in mst_network.get_vertices()} + + # Identify edges + tree_edges = {v:[] for v in tree_nodes.keys()} + tree_edge_lengths = {v:[] for v in tree_nodes.keys()} + network_edge_weights = list(mst_network.ep["weight"]) + for i, edge in enumerate(mst_network.get_edges()): + # Connectivity - add both directions as unrooted tree is not directional - + # do not know which will be leaf node + tree_edges[edge[0]].append(edge[1]) + tree_edges[edge[1]].append(edge[0]) + # Lengths added in the same order as the corresponding children to enable + # branches to be matched to child nodes + tree_edge_lengths[edge[0]].append(network_edge_weights[i]) + tree_edge_lengths[edge[1]].append(network_edge_weights[i]) + + # Identify seed node as that with most links + max_links = 0 + seed_node_index = None + for vertex in tree_edges.keys(): + if len(tree_edges[vertex]) > max_links: + max_links = len(tree_edges[vertex]) + seed_node_index = vertex + tree.seed_node = tree_nodes[seed_node_index] + + # Generate links of tree + parent_node_indices = [seed_node_index] + added_nodes = set(parent_node_indices) + i = 0 + while i < len(parent_node_indices): # NB loop end will increase + for x, child_node_index in enumerate(tree_edges[parent_node_indices[i]]): + if child_node_index not in added_nodes: + tree_nodes[parent_node_indices[i]].add_child(tree_nodes[child_node_index]) + tree_nodes[child_node_index].edge_length = tree_edge_lengths[parent_node_indices[i]][x] + added_nodes.add(child_node_index) + parent_node_indices.append(child_node_index) + i = i + 1 + + # Add zero length branches for internal nodes in MST + for node in tree.preorder_node_iter(): + if not node.is_leaf(): + new_child = dendropy.Node(taxon=node.taxon, edge_length=0.0) + node.taxon = None + node.add_child(new_child) + + # Return tree as string + tree_string = tree.as_string(schema="newick", + suppress_rooting=True, + unquoted_underscores=True) + return tree_string \ No newline at end of file diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 746e5435..cb56e71b 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -17,7 +17,10 @@ from .models import loadClusterFit -from .network import constructNetwork, fetchNetwork, generate_minimum_spanning_tree +from .network import constructNetwork +from .network import fetchNetwork +from .network import generate_minimum_spanning_tree +from .network import accepted_weights_types from .plot import outputsForMicroreact from .plot import outputsForCytoscape @@ -64,10 +67,6 @@ def get_options(): 'are from ref-query') iGroup.add_argument('--distances', help='Prefix of input pickle of pre-calculated distances') - iGroup.add_argument('--sparse-distances', - help='File (with a name *_rank*_fit.npz) storing a sparse' - ' distance matrix', - default=None) iGroup.add_argument('--include-files', help='File with list of sequences to include in visualisation. ' 'Default is to use all sequences in database.', @@ -119,7 +118,7 @@ def get_options(): faGroup.add_argument('--tree', help='Type of tree to calculate (nj, mst or both)', type=str, default='nj', choices=['nj', 'mst', 'both']) faGroup.add_argument('--mst-distances', help='Distances used to calculate a minimum spanning tree', type=str, - default='core', choices=['core','accessory']) + default='core', choices=accepted_weights_types) faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') faGroup.add_argument('--perplexity', @@ -197,7 +196,6 @@ def generate_visualisations(query_db, sys.stderr.write("Cannot create output directory\n") sys.exit(1) - # Load original distances if distances is None: if query_db is None: distances = os.path.basename(ref_db) + "/" + ref_db + ".dists" @@ -272,6 +270,7 @@ def generate_visualisations(query_db, acc_distMat = acc_distMat[np.ix_(row_slice, row_slice)] else: viz_subset = None + # Either use strain definitions, lineage assignments or external clustering isolateClustering = {} # Use external clustering if specified @@ -334,39 +333,19 @@ def generate_visualisations(query_db, if not overwrite: existing_tree = load_tree(output, "MST", distances=mst_distances) if existing_tree is None: - G = None - if sparse_distMat is not None: - sys.stderr.write("Started sparse distance matrix processing\n") - combined_dists = scipy.sparse.load_npz(sparse_distMat) - sys.stderr.write("Completed sparse distance matrix processing\n") - sys.stderr.write("Started constructing partial graph\n") - G = constructNetwork(combined_seq, - combined_seq, - [0]*combined_dists.shape[0], - 0, - edge_list=False, - sparse=True, - sparse_input=combined_dists) - - sys.stderr.write("Completed constructing partial graph\n") - else: - sys.stderr.write("Started distance matrix processing\n") - if qr_distMat is not None: # not sure how to concatenate QR/QQ matrices to sparse matrix - combined_dists = np.concatenate((rr_distMat, qr_distMat, qq_distMat), axis = 0) - else: - combined_dists = rr_distMat - sys.stderr.write("Completed distance matrix processing\n") - #### - sys.stderr.write("Started constructing complete graph\n") - G = constructNetwork(combined_seq, - combined_seq, - [0]*combined_dists.shape[0], - 0, - edge_list=False, - weights=combined_dists, - weights_type=mst_distances) - sys.stderr.write("Completed constructing complete graph\n") - mst_tree = generate_minimum_spanning_tree(G, combined_seq, rr_distMat, mst_distances) + complete_distMat = \ + np.hstack((pp_sketchlib.squareToLong(core_distMat, threads).reshape(-1, 1), + pp_sketchlib.squareToLong(acc_distMat, threads).reshape(-1, 1))) + # Dense network may be slow + sys.stderr.write("Generating MST from dense distances (may be slow)\n") + G = constructNetwork(combined_seq, + combined_seq, + np.zeros(complete_distMat.shape[0]), + 0, + edge_list=False, + weights=complete_distMat, + weights_type=mst_distances) + mst_tree = generate_minimum_spanning_tree(G) else: mst_tree = existing_tree From 9ae1519c45006bbab1fc2c3b2dac017b3393e15d Mon Sep 17 00:00:00 2001 From: John Lees Date: Wed, 27 Jan 2021 17:09:11 +0000 Subject: [PATCH 49/73] Unneeded arg --- PopPUNK/visualise.py | 1 - 1 file changed, 1 deletion(-) diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index cb56e71b..2401e553 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -342,7 +342,6 @@ def generate_visualisations(query_db, combined_seq, np.zeros(complete_distMat.shape[0]), 0, - edge_list=False, weights=complete_distMat, weights_type=mst_distances) mst_tree = generate_minimum_spanning_tree(G) From a584c7c44225fefa1a47760ff79fc31f3d17c28d Mon Sep 17 00:00:00 2001 From: John Lees Date: Wed, 27 Jan 2021 18:17:41 +0000 Subject: [PATCH 50/73] Add script to make MSTs from sparse matrix input --- PopPUNK/network.py | 24 ++++++---- PopPUNK/plot.py | 6 ++- PopPUNK/sparse_mst.py | 108 ++++++++++++++++++++++++++++++++++++++++++ PopPUNK/visualise.py | 2 - poppunk_mst-runner.py | 10 ++++ setup.py | 1 + 6 files changed, 138 insertions(+), 13 deletions(-) create mode 100755 PopPUNK/sparse_mst.py create mode 100755 poppunk_mst-runner.py diff --git a/PopPUNK/network.py b/PopPUNK/network.py index c6064252..abd6618b 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -266,7 +266,7 @@ def writeReferences(refList, outPrefix): def constructNetwork(rlist, qlist, assignments, within_label, summarise = True, edge_list = False, weights = None, - weights_type = 'euclidean', sparse = False, sparse_input = None): + weights_type = 'euclidean', sparse_input = None): """Construct an unweighted, undirected network without self-loops. Nodes are samples and edges where samples are within the same cluster @@ -728,12 +728,15 @@ def printExternalClusters(newClusters, extClusterFile, outPrefix, columns = ["sample"] + list(extClusters.keys()), index = False) -def generate_minimum_spanning_tree(G): +def generate_minimum_spanning_tree(G, from_cugraph = False): """Generate a minimum spanning tree from a network Args: G (network) Graph tool network + from_cugraph (bool) + If a pre-calculated MST from cugraph + [default = False] Returns: mst_network (str) @@ -742,15 +745,18 @@ def generate_minimum_spanning_tree(G): # # Create MST # - sys.stderr.write("Starting calculation of minimum-spanning tree\n") + if not from_cugraph: + sys.stderr.write("Starting calculation of minimum-spanning tree\n") - # Test if weighted network and calculate minimum spanning tree - if "weight" in G.edge_properties: - mst_edge_prop_map = gt.min_spanning_tree(G, weights = G.ep["weight"]) - mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) + # Test if weighted network and calculate minimum spanning tree + if "weight" in G.edge_properties: + mst_edge_prop_map = gt.min_spanning_tree(G, weights = G.ep["weight"]) + mst_network = gt.GraphView(G, efilt = mst_edge_prop_map) + else: + sys.stderr.write("generate_minimum_spanning_tree requires a weighted graph\n") + raise RuntimeError("MST passed unweighted graph") else: - sys.stderr.write("generate_minimum_spanning_tree requires a weighted graph\n") - raise RuntimeError("MST passed unweighted graph") + mst_network = G # Find seed nodes as those with greatest outdegree in each component seed_vertices = set() diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 9cdfac16..3b8961d4 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -14,6 +14,7 @@ import matplotlib.pyplot as plt import matplotlib.lines as lines import itertools +from math import sqrt # for other outputs from shutil import copyfile import pandas as pd @@ -405,10 +406,11 @@ def distHistogram(dists, rank, outPrefix): plt.close() def drawMST(mst, outPrefix): + import graph_tool.all as gt pos = gt.sfdp_layout(mst, eweight = mst.edge_properties["weight"]) - deg = g.degree_property_map("in") + deg = mst.degree_property_map("in") deg.a = 4 * (sqrt(deg.a) * 0.5 + 0.4) - ebet = gt.betweenness(g)[1] + ebet = gt.betweenness(mst)[1] ebet.a /= ebet.a.max() / 10. eorder = ebet.copy() eorder.a *= -1 diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py new file mode 100755 index 00000000..a0c666b7 --- /dev/null +++ b/PopPUNK/sparse_mst.py @@ -0,0 +1,108 @@ +#!/usr/bin/env python +# vim: set fileencoding= : +# Copyright 2018-2021 John Lees and Nick Croucher + +# universal +import os +import sys + +import pickle +import pandas as pd +from scipy import sparse + +# import poppunk package +from .__init__ import __version__ + +from .network import constructNetwork, generate_minimum_spanning_tree +from .plot import drawMST +from .trees import mst_to_phylogeny, write_tree +from .utils import setGtThreads + +# command line parsing +def get_options(): + + import argparse + + parser = argparse.ArgumentParser(description='Create a minimum-spanning tree', + prog='poppunk_mst') + + # input options + iGroup = parser.add_argument_group('Input files') + iGroup.add_argument('--distances', required=True, help='Prefix of input pickle of pre-calculated distances (required)') + iGroup.add_argument('--rank-fit', required=True, help='Location of rank fit, a sparse matrix (*_rank*_fit.npz)') + + # output options + oGroup = parser.add_argument_group('Output options') + oGroup.add_argument('--output', required=True, help='Prefix for output files (required)') + + # processing + other = parser.add_argument_group('Other options') + other.add_argument('--threads', default=1, type=int, + help='Number of threads to use [default = 1]') + other.add_argument('--gpu-graph', default=False, action='store_true', + help='Use cugraph for the calculation') + + other.add_argument('--version', action='version', + version='%(prog)s '+__version__) + + return parser.parse_args() + +def main(): + + # Check input args ok + args = get_options() + + import graph_tool.all as gt + try: + import cugraph + except ImportError as e: + if args.gpu_graph: + sys.stderr.write("cugraph unavailable\n") + raise ImportError(e) + + # Check output path ok + if not os.path.isdir(args.output): + try: + os.makedirs(args.output) + except OSError: + sys.stderr.write("Cannot create output directory\n") + sys.exit(1) + setGtThreads(args.threads) + + # Read in sample names + with open(args.distances + ".pkl", 'rb') as pickle_file: + rlist, qlist, self = pickle.load(pickle_file) + if not self: + sys.stderr.write("This script must be run on a full all-v-all model\n") + sys.exit(1) + + # Create network with sparse dists + sys.stderr.write("Loading distances into graph\n") + sparse_mat = sparse.load_npz(args.rank_fit) + if args.gpu_graph: + G_df = pd.DataFrame.from_dict({'source': sparse_mat.row, + 'destination': sparse_mat.col, + 'weights': sparse_mat.data}) + M = sparse_mat.tocsr() + offsets = pd.Series(M.indptr) + indices = pd.Series(M.indices) + weights = pd.Series(M.data) + G = cugraph.from_adjlist(offsets, indices, weights) + sys.stderr.write("Calculating MST (GPU)\n") + G_mst = cugraph.tree.minimum_spanning_tree(G, weight='weights') + edge_df = G_mst.view_edge_list() + mst = constructNetwork(rlist, rlist, + (edge_df['src'], edge_df['dst']), + 0, edge_list=True, weights=edge_df['weight']) + else: + G = constructNetwork(rlist, rlist, None, 0, sparse_input = sparse_mat) + + sys.stderr.write("Calculating MST (CPU)\n") + mst = generate_minimum_spanning_tree(G, args.gpu_graph) + sys.stderr.write("Generating output\n") + drawMST(mst, args.output) + mst_as_tree = mst_to_phylogeny(mst, rlist) + write_tree(mst_as_tree, args.output, "_MST.nwk", overwrite = True) + + sys.exit(0) + diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 2401e553..8a371c8d 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -155,7 +155,6 @@ def get_options(): def generate_visualisations(query_db, ref_db, distances, - sparse_distMat, threads, output, gpu_dist, @@ -416,7 +415,6 @@ def main(): generate_visualisations(args.query_db, args.ref_db, args.distances, - args.sparse_distances, args.threads, args.output, args.gpu_dist, diff --git a/poppunk_mst-runner.py b/poppunk_mst-runner.py new file mode 100755 index 00000000..e186eccd --- /dev/null +++ b/poppunk_mst-runner.py @@ -0,0 +1,10 @@ +#!/usr/bin/env python +# vim: set fileencoding= : +# Copyright 2018-2021 John Lees and Nick Croucher + +"""Convenience wrapper for running poppunk_mst directly from source tree.""" + +from PopPUNK.sparse_mst import main + +if __name__ == '__main__': + main() diff --git a/setup.py b/setup.py index bbef0975..455167fc 100644 --- a/setup.py +++ b/setup.py @@ -57,6 +57,7 @@ def find_version(*file_paths): 'poppunk = PopPUNK.__main__:main', 'poppunk_assign = PopPUNK.assign:main', 'poppunk_visualise = PopPUNK.visualise:main', + 'poppunk_mst = PopPUNK.sparse_mst:main', 'poppunk_prune = PopPUNK.prune_db:main', 'poppunk_references = PopPUNK.reference_pick:main', 'poppunk_tsne = PopPUNK.tsne:main' From 686267dea95cf0f6bc207b69d695c5ebc2207ea3 Mon Sep 17 00:00:00 2001 From: John Lees Date: Thu, 28 Jan 2021 11:02:35 +0000 Subject: [PATCH 51/73] Note from issue --- PopPUNK/network.py | 1 + 1 file changed, 1 insertion(+) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index abd6618b..0930fa52 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -781,6 +781,7 @@ def generate_minimum_spanning_tree(G, from_cugraph = False): if seed_edge[1] in seed_vertices: found = True connections.append((seed_edge)) + # TODO: alternative would be to requery the DB (likely quick) if found == False: for query in seed_vertices: if query != ref: From 41d13a820fb484a00a0363c89d871a9391f6881f Mon Sep 17 00:00:00 2001 From: John Lees Date: Thu, 28 Jan 2021 16:36:10 +0000 Subject: [PATCH 52/73] Code to make MST plots --- PopPUNK/plot.py | 67 +++++++++++++++++++++++++++++-------------- PopPUNK/sparse_mst.py | 8 ++++-- PopPUNK/trees.py | 7 +++-- PopPUNK/visualise.py | 14 ++++++--- 4 files changed, 66 insertions(+), 30 deletions(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 3b8961d4..88ce1670 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -14,7 +14,6 @@ import matplotlib.pyplot as plt import matplotlib.lines as lines import itertools -from math import sqrt # for other outputs from shutil import copyfile import pandas as pd @@ -405,27 +404,45 @@ def distHistogram(dists, rank, outPrefix): "_rank_" + str(rank) + "_histogram.png") plt.close() -def drawMST(mst, outPrefix): +def drawMST(mst, outPrefix, isolate_clustering, overwrite): import graph_tool.all as gt - pos = gt.sfdp_layout(mst, eweight = mst.edge_properties["weight"]) - deg = mst.degree_property_map("in") - deg.a = 4 * (sqrt(deg.a) * 0.5 + 0.4) - ebet = gt.betweenness(mst)[1] - ebet.a /= ebet.a.max() / 10. - eorder = ebet.copy() - eorder.a *= -1 - graph_file_name = outPrefix + "/" + os.path.basename(outPrefix) + "_mst_plot.png" - gt.graph_draw(mst, pos=pos, vertex_size=deg, vertex_fill_color=deg, vorder=deg, - edge_color=ebet, eorder=eorder, edge_pen_width=ebet, - output=graph_file_name, output_szie=(1500, 1500)) - -def outputsForCytoscape(G, clustering, outPrefix, epiCsv, queryList = None, + graph1_file_name = outPrefix + "/" + os.path.basename(outPrefix) + "_mst_stress_plot.png" + graph2_file_name = outPrefix + "/" + os.path.basename(outPrefix) + "_mst_cluster_plot.png" + if overwrite or not os.path.isfile(graph1_file_name) or not os.path.isfile(graph2_file_name): + sys.stderr.write("Drawing MST\n") + pos = gt.sfdp_layout(mst) + if overwrite or not os.path.isfile(graph1_file_name): + deg = mst.degree_property_map("total") + deg.a = 4 * (np.sqrt(deg.a) * 0.5 + 0.4) + ebet = gt.betweenness(mst)[1] + ebet.a /= ebet.a.max() / 50. + eorder = ebet.copy() + eorder.a *= -1 + gt.graph_draw(mst, pos=pos, vertex_size=gt.prop_to_size(deg, mi=20, ma=100), + vertex_fill_color=deg, vorder=deg, + edge_color=ebet, eorder=eorder, edge_pen_width=ebet, + output=graph1_file_name, output_size=(3000, 3000)) + if overwrite or not os.path.isfile(graph2_file_name): + cluster_fill = {} + for cluster in set(isolate_clustering['Cluster'].values()): + cluster_fill[cluster] = list(np.random.rand(3)) + [0.9] + plot_color = mst.new_vertex_property('vector') + mst.vertex_properties['plot_color'] = plot_color + for v in mst.vertices(): + plot_color[v] = cluster_fill[isolate_clustering['Cluster'][mst.vp.id[v]]] + + gt.graph_draw(mst, pos=pos, vertex_fill_color=mst.vertex_properties['plot_color'], + output=graph2_file_name, output_size=(3000, 3000)) + +def outputsForCytoscape(G, G_mst, clustering, outPrefix, epiCsv, queryList = None, suffix = None, writeCsv = True, viz_subset = None): """Write outputs for cytoscape. A graphml of the network, and CSV with metadata Args: G (graph) The network to write from :func:`~PopPUNK.network.constructNetwork` + G_mst (graph) + The minimum spanning tree of G clustering (dict) Dictionary of cluster assignments (keys are nodeNames). outPrefix (str) @@ -468,6 +485,16 @@ def outputsForCytoscape(G, clustering, outPrefix, epiCsv, queryList = None, graph_file_name = os.path.basename(outPrefix) + "_" + suffix + "_cytoscape.graphml" G.save(outPrefix + "/" + graph_file_name, fmt = 'graphml') + if G_mst != None: + isolate_labels = isolateNameToLabel(G_mst.vp.id) + for n,v in enumerate(G_mst.vertices()): + G_mst.vp.id[v] = isolate_labels[n] + if suffix is None: + graph_file_name = os.path.basename(outPrefix) + "_cytoscape_mst.graphml" + else: + graph_file_name = os.path.basename(outPrefix) + "_" + suffix + "_cytoscape_mst.graphml" + G_mst.save(outPrefix + "/" + graph_file_name, fmt = 'graphml') + # Write CSV of metadata if writeCsv: seqLabels = isolateNameToLabel(isolate_names) @@ -683,18 +710,16 @@ def outputsForMicroreact(combined_list, clustering, nj_tree, mst_tree, accMat, p writeClusterCsv(outPrefix + "/" + os.path.basename(outPrefix) + "_microreact_clusters.csv", combined_list, combined_list, clustering, 'microreact', epiCsv, queryList) + # write the phylogeny .nwk; t-SNE network .dot; clusters + data .csv + generate_tsne(seqLabels, accMat, perplexity, outPrefix, overwrite) + # write NJ tree if nj_tree is not None: write_tree(nj_tree, outPrefix, "_core_NJ.nwk", overwrite) # write MST if mst_tree is not None: - drawMST(mst_tree, outPrefix) - mst_as_tree = mst_to_phylogeny(mst_tree, seqLabels) - write_tree(mst_as_tree, outPrefix, "_core_MST.nwk", overwrite) - - # write the phylogeny .nwk; t-SNE network .dot; clusters + data .csv - generate_tsne(seqLabels, accMat, perplexity, outPrefix, overwrite) + write_tree(mst_tree, outPrefix, "_MST.nwk", overwrite) def outputsForPhandango(combined_list, clustering, nj_tree, mst_tree, outPrefix, epiCsv, queryList = None, overwrite = False): diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index a0c666b7..ba6bdaed 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -34,6 +34,8 @@ def get_options(): # output options oGroup = parser.add_argument_group('Output options') oGroup.add_argument('--output', required=True, help='Prefix for output files (required)') + oGroup.add_argument('--no-plot', default=False, action='store_true', + help='Do not try and draw the MST') # processing other = parser.add_argument_group('Other options') @@ -93,16 +95,18 @@ def main(): edge_df = G_mst.view_edge_list() mst = constructNetwork(rlist, rlist, (edge_df['src'], edge_df['dst']), - 0, edge_list=True, weights=edge_df['weight']) + 0, edge_list=True, weights=edge_df['weight'], + summarise=False) else: G = constructNetwork(rlist, rlist, None, 0, sparse_input = sparse_mat) sys.stderr.write("Calculating MST (CPU)\n") mst = generate_minimum_spanning_tree(G, args.gpu_graph) sys.stderr.write("Generating output\n") - drawMST(mst, args.output) mst_as_tree = mst_to_phylogeny(mst, rlist) write_tree(mst_as_tree, args.output, "_MST.nwk", overwrite = True) + if not args.no_plot: + drawMST(mst, args.output, True) sys.exit(0) diff --git a/PopPUNK/trees.py b/PopPUNK/trees.py index 8f4b9672..6691153b 100644 --- a/PopPUNK/trees.py +++ b/PopPUNK/trees.py @@ -62,6 +62,7 @@ def buildRapidNJ(rapidnj, refList, coreMat, outPrefix, threads = 1): # read tree and return tree = dendropy.Tree.get(path=tree_filename, schema="newick") + os.remove(tree_filename) return tree def write_tree(tree, prefix, suffix, overwrite): @@ -102,11 +103,11 @@ def load_tree(prefix, type, distances = 'core'): for suffix in ['_' + distances + '_' + type + ".tree",'_' + distances + '_' + type + ".nwk"]: tree_fn = tree_prefix + suffix if os.path.isfile(tree_fn): + sys.stderr.write("Reading existing tree from " + tree_fn + "\n") tree = dendropy.Tree.get(path=tree_fn, schema="newick") tree_string = tree.as_string(schema="newick", suppress_rooting=True, unquoted_underscores=True) - sys.stderr.write("Reading existing tree from " + tree_fn + "\n") break return tree_string @@ -156,8 +157,8 @@ def generate_nj_tree(coreMat, seqLabels, outPrefix, rapidnj, threads): # return Newick string tree_string = tree.as_string(schema="newick", - suppress_rooting=True, - unquoted_underscores=True) + suppress_rooting=True, + unquoted_underscores=True) return tree_string def mst_to_phylogeny(mst_network, names): diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 8a371c8d..7365c462 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -22,6 +22,7 @@ from .network import generate_minimum_spanning_tree from .network import accepted_weights_types +from .plot import drawMST from .plot import outputsForMicroreact from .plot import outputsForCytoscape from .plot import outputsForPhandango @@ -34,8 +35,9 @@ from .sketchlib import getKmersFromReferenceDatabase from .sketchlib import addRandom -from .trees import load_tree, generate_nj_tree +from .trees import load_tree, generate_nj_tree, mst_to_phylogeny +from .utils import isolateNameToLabel from .utils import readPickle from .utils import setGtThreads from .utils import update_distance_matrices @@ -327,6 +329,7 @@ def generate_visualisations(query_db, # Generate MST mst_tree = None + mst_graph = None if tree == 'mst' or tree == 'both': existing_tree = None if not overwrite: @@ -342,8 +345,11 @@ def generate_visualisations(query_db, np.zeros(complete_distMat.shape[0]), 0, weights=complete_distMat, - weights_type=mst_distances) - mst_tree = generate_minimum_spanning_tree(G) + weights_type=mst_distances, + summarise=False) + mst_graph = generate_minimum_spanning_tree(G) + drawMST(mst_graph, output, isolateClustering, overwrite) + mst_tree = mst_to_phylogeny(mst_graph, isolateNameToLabel(combined_seq)) else: mst_tree = existing_tree @@ -401,7 +407,7 @@ def generate_visualisations(query_db, if cytoscape: sys.stderr.write("Writing cytoscape output\n") genomeNetwork, cluster_file = fetchNetwork(prev_clustering, model, rlist, False, core_only, accessory_only) - outputsForCytoscape(genomeNetwork, isolateClustering, output, info_csv, viz_subset = viz_subset) + outputsForCytoscape(genomeNetwork, mst_graph, isolateClustering, output, info_csv, viz_subset = viz_subset) if model.type == 'lineage': sys.stderr.write("Note: Only support for output of cytoscape graph at lowest rank\n") From 6a19d570762764d034fb8b22f49646571a67c4d7 Mon Sep 17 00:00:00 2001 From: John Lees Date: Thu, 28 Jan 2021 16:43:24 +0000 Subject: [PATCH 53/73] Add drawMST docstring --- PopPUNK/plot.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index 88ce1670..c2fa2506 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -405,6 +405,18 @@ def distHistogram(dists, rank, outPrefix): plt.close() def drawMST(mst, outPrefix, isolate_clustering, overwrite): + """Plot a layout of the minimum spanning tree + + Args: + mst (graph_tool.Graph) + A minimum spanning tree + outPrefix (str) + Output prefix for save files + isolate_clustering (dict) + Dictionary of ID: cluster, used for colouring vertices + overwrite (bool) + Overwrite existing output files + """ import graph_tool.all as gt graph1_file_name = outPrefix + "/" + os.path.basename(outPrefix) + "_mst_stress_plot.png" graph2_file_name = outPrefix + "/" + os.path.basename(outPrefix) + "_mst_cluster_plot.png" From 53c5eba7034a93eed4b8b089f91031819e498954 Mon Sep 17 00:00:00 2001 From: John Lees Date: Thu, 28 Jan 2021 17:11:06 +0000 Subject: [PATCH 54/73] Add cluster definitions to sparse_mst --- PopPUNK/network.py | 2 +- PopPUNK/sparse_mst.py | 21 +++++++++++++++++++-- 2 files changed, 20 insertions(+), 3 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 0930fa52..dd747f58 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -350,7 +350,7 @@ def constructNetwork(rlist, qlist, assignments, within_label, G = gt.Graph(directed = False) G.add_vertex(len(vertex_labels)) - if weights is not None or sparse: + if weights is not None or sparse_input != None: eweight = G.new_ep("float") G.add_edge_list(connections, eprops = [eweight]) G.edge_properties["weight"] = eweight diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index ba6bdaed..bf169a44 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -7,6 +7,7 @@ import sys import pickle +import re import pandas as pd from scipy import sparse @@ -16,7 +17,7 @@ from .network import constructNetwork, generate_minimum_spanning_tree from .plot import drawMST from .trees import mst_to_phylogeny, write_tree -from .utils import setGtThreads +from .utils import setGtThreads, readIsolateTypeFromCsv # command line parsing def get_options(): @@ -30,6 +31,7 @@ def get_options(): iGroup = parser.add_argument_group('Input files') iGroup.add_argument('--distances', required=True, help='Prefix of input pickle of pre-calculated distances (required)') iGroup.add_argument('--rank-fit', required=True, help='Location of rank fit, a sparse matrix (*_rank*_fit.npz)') + iGroup.add_argument('--previous-clustering', help='CSV file with cluster definitions') # output options oGroup = parser.add_argument_group('Output options') @@ -105,8 +107,23 @@ def main(): sys.stderr.write("Generating output\n") mst_as_tree = mst_to_phylogeny(mst, rlist) write_tree(mst_as_tree, args.output, "_MST.nwk", overwrite = True) + + # Make plots if not args.no_plot: - drawMST(mst, args.output, True) + if args.previous_clustering != None: + mode = "clusters" + if re.match(r"_lineages\.csv$", args.previous_clustering): + mode = "lineages" + isolateClustering = readIsolateTypeFromCsv(args.previous_clustering, + mode = mode, + return_dict = True) + else: + # Create dictionary with everything in the same cluster if none passed + isolateClustering = {'Cluster': {}} + for v in mst.vertices: + isolateClustering['Cluster'][mst.vp.id[v]] = '0' + + drawMST(mst, args.output, isolateClustering, True) sys.exit(0) From be21d6bc17fd54e346378ee13a5ebd6fa602eae5 Mon Sep 17 00:00:00 2001 From: John Lees Date: Thu, 28 Jan 2021 17:21:59 +0000 Subject: [PATCH 55/73] Remove summary from output --- PopPUNK/sparse_mst.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index bf169a44..395790e0 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -100,7 +100,8 @@ def main(): 0, edge_list=True, weights=edge_df['weight'], summarise=False) else: - G = constructNetwork(rlist, rlist, None, 0, sparse_input = sparse_mat) + G = constructNetwork(rlist, rlist, None, 0, + sparse_input=sparse_mat, summarise=False) sys.stderr.write("Calculating MST (CPU)\n") mst = generate_minimum_spanning_tree(G, args.gpu_graph) From c149dbf10acba4b0757263c795433ec1f670ed34 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 14:39:52 +0000 Subject: [PATCH 56/73] Read sketchlib version from module No longer exit on error --- PopPUNK/sketchlib.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/PopPUNK/sketchlib.py b/PopPUNK/sketchlib.py index dc0f905c..1ef1bb3e 100644 --- a/PopPUNK/sketchlib.py +++ b/PopPUNK/sketchlib.py @@ -42,12 +42,17 @@ def checkSketchlibVersion(): version (str) Version string """ - p = subprocess.Popen([sketchlib_exe + ' --version'], shell=True, stdout=subprocess.PIPE) - version = 0 - for line in iter(p.stdout.readline, ''): - if line != '': - version = line.rstrip().decode().split(" ")[1] - break + try: + version = pp_sketchlib.version + + # Older versions didn't export attributes + except AttributeError: + p = subprocess.Popen([sketchlib_exe + ' --version'], shell=True, stdout=subprocess.PIPE) + version = 0 + for line in iter(p.stdout.readline, ''): + if line != '': + version = line.rstrip().decode().split(" ")[1] + break sketchlib_version = [int(v) for v in version.split(".")] if sketchlib_version[0] < SKETCHLIB_MAJOR or \ @@ -57,7 +62,7 @@ def checkSketchlibVersion(): "v" + str(SKETCHLIB_MAJOR) + \ "." + str(SKETCHLIB_MINOR) + \ "." + str(SKETCHLIB_PATCH) + " or higher\n") - sys.exit(1) + sys.stderr.write("Continuing... but safety not guaranteed\n") return version From b1fc5f7b8b6d720011d2323a75f0628d1fead25a Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 15:18:20 +0000 Subject: [PATCH 57/73] Make stress plot point size smaller --- PopPUNK/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/plot.py b/PopPUNK/plot.py index c2fa2506..2f0f23dc 100644 --- a/PopPUNK/plot.py +++ b/PopPUNK/plot.py @@ -430,7 +430,7 @@ def drawMST(mst, outPrefix, isolate_clustering, overwrite): ebet.a /= ebet.a.max() / 50. eorder = ebet.copy() eorder.a *= -1 - gt.graph_draw(mst, pos=pos, vertex_size=gt.prop_to_size(deg, mi=20, ma=100), + gt.graph_draw(mst, pos=pos, vertex_size=gt.prop_to_size(deg, mi=20, ma=50), vertex_fill_color=deg, vorder=deg, edge_color=ebet, eorder=eorder, edge_pen_width=ebet, output=graph1_file_name, output_size=(3000, 3000)) From a92b1846cb1593a5f0ec4da6776d3588e82a48ad Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 15:18:42 +0000 Subject: [PATCH 58/73] Fix cugraph initialisation --- PopPUNK/sparse_mst.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index 395790e0..b40a56ab 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -84,14 +84,17 @@ def main(): sys.stderr.write("Loading distances into graph\n") sparse_mat = sparse.load_npz(args.rank_fit) if args.gpu_graph: - G_df = pd.DataFrame.from_dict({'source': sparse_mat.row, - 'destination': sparse_mat.col, - 'weights': sparse_mat.data}) + # Alternative + #G_df = pd.DataFrame.from_dict({'source': sparse_mat.row, + # 'destination': sparse_mat.col, + # 'weights': sparse_mat.data}) M = sparse_mat.tocsr() offsets = pd.Series(M.indptr) indices = pd.Series(M.indices) weights = pd.Series(M.data) - G = cugraph.from_adjlist(offsets, indices, weights) + + G = cugraph.Graph() + G = cugraph.from_cudf_adjlist(offsets, indices, weights) sys.stderr.write("Calculating MST (GPU)\n") G_mst = cugraph.tree.minimum_spanning_tree(G, weight='weights') edge_df = G_mst.view_edge_list() From 9fa20840efae36fd4f7cc7623a66e564d3b107d1 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 15:19:44 +0000 Subject: [PATCH 59/73] Correct adjlist function call --- PopPUNK/sparse_mst.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index b40a56ab..85cd46f0 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -94,7 +94,7 @@ def main(): weights = pd.Series(M.data) G = cugraph.Graph() - G = cugraph.from_cudf_adjlist(offsets, indices, weights) + G.from_cudf_adjlist(offsets, indices, weights) sys.stderr.write("Calculating MST (GPU)\n") G_mst = cugraph.tree.minimum_spanning_tree(G, weight='weights') edge_df = G_mst.view_edge_list() From b42a765144d51cbe630760d1d07d31206a886dc2 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 16:33:01 +0000 Subject: [PATCH 60/73] Use cudf explicitly --- PopPUNK/sparse_mst.py | 25 +++++++++++-------------- 1 file changed, 11 insertions(+), 14 deletions(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index 85cd46f0..65bfff36 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -59,9 +59,10 @@ def main(): import graph_tool.all as gt try: import cugraph + import cudf except ImportError as e: if args.gpu_graph: - sys.stderr.write("cugraph unavailable\n") + sys.stderr.write("cugraph and cudf unavailable\n") raise ImportError(e) # Check output path ok @@ -84,20 +85,16 @@ def main(): sys.stderr.write("Loading distances into graph\n") sparse_mat = sparse.load_npz(args.rank_fit) if args.gpu_graph: - # Alternative - #G_df = pd.DataFrame.from_dict({'source': sparse_mat.row, - # 'destination': sparse_mat.col, - # 'weights': sparse_mat.data}) - M = sparse_mat.tocsr() - offsets = pd.Series(M.indptr) - indices = pd.Series(M.indices) - weights = pd.Series(M.data) - + G_df = cudf.DataFrame({'source': sparse_mat.row, + 'destination': sparse_mat.col, + 'weights': sparse_mat.data}) G = cugraph.Graph() - G.from_cudf_adjlist(offsets, indices, weights) - sys.stderr.write("Calculating MST (GPU)\n") - G_mst = cugraph.tree.minimum_spanning_tree(G, weight='weights') + G.from_cudf_edgelist(G_df, edge_attr='weights', renumber=False) + + sys.stderr.write("Calculating MST (GPU part)\n") + G_mst = cugraph.minimum_spanning_tree(G, weight='weights') edge_df = G_mst.view_edge_list() + sys.stderr.write("Calculating MST (CPU part)\n") mst = constructNetwork(rlist, rlist, (edge_df['src'], edge_df['dst']), 0, edge_list=True, weights=edge_df['weight'], @@ -105,8 +102,8 @@ def main(): else: G = constructNetwork(rlist, rlist, None, 0, sparse_input=sparse_mat, summarise=False) + sys.stderr.write("Calculating MST (CPU)\n") - sys.stderr.write("Calculating MST (CPU)\n") mst = generate_minimum_spanning_tree(G, args.gpu_graph) sys.stderr.write("Generating output\n") mst_as_tree = mst_to_phylogeny(mst, rlist) From 77c22180e4e5b84cb06abcf717f51ce7b14950a4 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 16:40:14 +0000 Subject: [PATCH 61/73] Correct weight col name --- PopPUNK/sparse_mst.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index 65bfff36..a5e4fede 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -97,7 +97,7 @@ def main(): sys.stderr.write("Calculating MST (CPU part)\n") mst = constructNetwork(rlist, rlist, (edge_df['src'], edge_df['dst']), - 0, edge_list=True, weights=edge_df['weight'], + 0, edge_list=True, weights=edge_df['weights'], summarise=False) else: G = constructNetwork(rlist, rlist, None, 0, From 89fcae7e76e0b133c721f5bbcec24b5e05ea1183 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 16:52:19 +0000 Subject: [PATCH 62/73] Generate tuple list from df --- PopPUNK/sparse_mst.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index a5e4fede..adf3a532 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -95,8 +95,9 @@ def main(): G_mst = cugraph.minimum_spanning_tree(G, weight='weights') edge_df = G_mst.view_edge_list() sys.stderr.write("Calculating MST (CPU part)\n") + edge_tuple = [tuple(x) for x in edge_df[['src', 'dst']].values] mst = constructNetwork(rlist, rlist, - (edge_df['src'], edge_df['dst']), + edge_tuple, 0, edge_list=True, weights=edge_df['weights'], summarise=False) else: From 791eaf101853bf9958b35eca94b64c866a707790 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 16:54:32 +0000 Subject: [PATCH 63/73] Correct booleans --- PopPUNK/network.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index dd747f58..28d24989 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -321,12 +321,12 @@ def constructNetwork(rlist, qlist, assignments, within_label, # identify edges connections = [] if edge_list: - if weights != None: + if weights is not None: for weight, (ref, query) in zip(weights, assignments): connections.append((ref, query, weight)) else: connections = assignments - elif sparse_input != None: + elif sparse_input is not None: for ref, query, weight in zip(sparse_input.row, sparse_input.col, sparse_input.data): connections.append((ref, query, weight)) else: @@ -350,7 +350,7 @@ def constructNetwork(rlist, qlist, assignments, within_label, G = gt.Graph(directed = False) G.add_vertex(len(vertex_labels)) - if weights is not None or sparse_input != None: + if weights is not None or sparse_input is not None: eweight = G.new_ep("float") G.add_edge_list(connections, eprops = [eweight]) G.edge_properties["weight"] = eweight From 7733e1fe88ff17b4ca9a6f526cf957fc6d852b36 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 16:56:02 +0000 Subject: [PATCH 64/73] Copy values to host --- PopPUNK/sparse_mst.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index adf3a532..2e11beef 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -95,10 +95,11 @@ def main(): G_mst = cugraph.minimum_spanning_tree(G, weight='weights') edge_df = G_mst.view_edge_list() sys.stderr.write("Calculating MST (CPU part)\n") - edge_tuple = [tuple(x) for x in edge_df[['src', 'dst']].values] + edge_tuple = [tuple(x) for x in edge_df[['src', 'dst']].values_host] mst = constructNetwork(rlist, rlist, edge_tuple, - 0, edge_list=True, weights=edge_df['weights'], + 0, edge_list=True, + weights=edge_df['weights'].values_host, summarise=False) else: G = constructNetwork(rlist, rlist, None, 0, From 39ae907fa62240a46f2b7f3d090200feaef5cddc Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 17:05:58 +0000 Subject: [PATCH 65/73] Correct graph name passed forwards --- PopPUNK/sparse_mst.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index 2e11beef..5a9079b5 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -88,15 +88,15 @@ def main(): G_df = cudf.DataFrame({'source': sparse_mat.row, 'destination': sparse_mat.col, 'weights': sparse_mat.data}) - G = cugraph.Graph() - G.from_cudf_edgelist(G_df, edge_attr='weights', renumber=False) + G_cu = cugraph.Graph() + G_cu.from_cudf_edgelist(G_df, edge_attr='weights', renumber=False) sys.stderr.write("Calculating MST (GPU part)\n") - G_mst = cugraph.minimum_spanning_tree(G, weight='weights') + G_mst = cugraph.minimum_spanning_tree(G_cu, weight='weights') edge_df = G_mst.view_edge_list() sys.stderr.write("Calculating MST (CPU part)\n") - edge_tuple = [tuple(x) for x in edge_df[['src', 'dst']].values_host] - mst = constructNetwork(rlist, rlist, + edge_tuple = edge_df[['src', 'dst']].values + G = constructNetwork(rlist, rlist, edge_tuple, 0, edge_list=True, weights=edge_df['weights'].values_host, From b62d2aa1f0bae7142dffd7960c1cbc993a516fc9 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 17:10:02 +0000 Subject: [PATCH 66/73] Run MST modes in tests --- test/clean_test.py | 2 ++ test/run_test.py | 5 +++++ 2 files changed, 7 insertions(+) diff --git a/test/clean_test.py b/test/clean_test.py index d6786cae..0cb3030a 100755 --- a/test/clean_test.py +++ b/test/clean_test.py @@ -34,6 +34,8 @@ def deleteDir(dirname): "example_viz_query", "example_lineage_viz", "example_viz_query_lineages", + "example_mst", + "example_sparse_mst", "example_tsne", "example_prune", "example_refs" diff --git a/test/run_test.py b/test/run_test.py index 03c15aa1..33f55b97 100755 --- a/test/run_test.py +++ b/test/run_test.py @@ -61,6 +61,11 @@ subprocess.run("python ../poppunk_visualise-runner.py --ref-db example_db --previous-clustering example_lineages --model-dir example_lineages --output example_lineage_viz --microreact", shell=True, check=True) subprocess.run("python ../poppunk_visualise-runner.py --distances example_query/example_query.dists --ref-db example_db --model-dir example_lineages --query-db example_lineage_query --output example_viz_query_lineages --microreact", shell=True, check=True) +# MST +sys.stderr.write("Running MST\n") +subprocess.run("python ../poppunk_visualise-runner.py --ref-db example_db --output example_mst --microreact --mst", shell=True, check=True) +subprocess.run("python ../poppunk_mst-runner.py --distances example_db/example_db.dists --rank-fit example_lineages/example_lineages_rank5_fit.npz --output example_sparse_mst --no-plot", shell=True, check=True) + # t-sne sys.stderr.write("Running tsne viz\n") subprocess.run("python ../poppunk_tsne-runner.py --distances example_db/example_db.dists --output example_tsne --perplexity 5 --verbosity 1", shell=True, check=True) From a49beaaa66a254637746145c498b423e421c806a Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 17:37:03 +0000 Subject: [PATCH 67/73] Fix MST component joining --- PopPUNK/network.py | 4 ++-- test/clean_test.py | 1 + test/run_test.py | 4 ++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/PopPUNK/network.py b/PopPUNK/network.py index 28d24989..fd2d3951 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -773,9 +773,9 @@ def generate_minimum_spanning_tree(G, from_cugraph = False): if len(component_frequencies) > 1: # Extract distances connections = [] - max_weight = np.max(G.edge_properties["weight"].a) + max_weight = float(np.max(G.edge_properties["weight"].a)) for ref in seed_vertices: - seed_edges = G.get_all_edges(ref, ["weight"]) + seed_edges = G.get_all_edges(ref, [G.ep['weight']]) found = False # Not all edges may be in graph for seed_edge in seed_edges: if seed_edge[1] in seed_vertices: diff --git a/test/clean_test.py b/test/clean_test.py index 0cb3030a..ea3ae921 100755 --- a/test/clean_test.py +++ b/test/clean_test.py @@ -23,6 +23,7 @@ def deleteDir(dirname): "example_qc", "example_dbscan", "example_refine", + "example_threshold", "example_lineages", "example_use", "example_query", diff --git a/test/run_test.py b/test/run_test.py index 33f55b97..03ab8942 100755 --- a/test/run_test.py +++ b/test/run_test.py @@ -63,8 +63,8 @@ # MST sys.stderr.write("Running MST\n") -subprocess.run("python ../poppunk_visualise-runner.py --ref-db example_db --output example_mst --microreact --mst", shell=True, check=True) -subprocess.run("python ../poppunk_mst-runner.py --distances example_db/example_db.dists --rank-fit example_lineages/example_lineages_rank5_fit.npz --output example_sparse_mst --no-plot", shell=True, check=True) +subprocess.run("python ../poppunk_visualise-runner.py --ref-db example_db --output example_mst --microreact --tree mst", shell=True, check=True) +subprocess.run("python ../poppunk_mst-runner.py --distances example_db/example_db.dists --rank-fit example_lineages/example_lineages_rank5_fit.npz --previous-clustering example_dbscan/example_dbscan_clusters.csv --output example_sparse_mst --no-plot", shell=True, check=True) # t-sne sys.stderr.write("Running tsne viz\n") From 6185cb25a8beae9849a488ad5d6746564b7877ea Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 17:51:59 +0000 Subject: [PATCH 68/73] Save MST as graphml (in sparse_mst) --- PopPUNK/sparse_mst.py | 1 + 1 file changed, 1 insertion(+) diff --git a/PopPUNK/sparse_mst.py b/PopPUNK/sparse_mst.py index 5a9079b5..50a9e4b1 100755 --- a/PopPUNK/sparse_mst.py +++ b/PopPUNK/sparse_mst.py @@ -108,6 +108,7 @@ def main(): mst = generate_minimum_spanning_tree(G, args.gpu_graph) sys.stderr.write("Generating output\n") + mst.save(args.output + "/" + os.path.basename(args.output) + ".graphml", fmt="graphml") mst_as_tree = mst_to_phylogeny(mst, rlist) write_tree(mst_as_tree, args.output, "_MST.nwk", overwrite = True) From 482f8cc3b090bf8cbb4e0e290bbd7049b2b50421 Mon Sep 17 00:00:00 2001 From: John Lees Date: Fri, 29 Jan 2021 18:37:32 +0000 Subject: [PATCH 69/73] Begin MST docs --- docs/index.rst | 1 + docs/mst.rst | 36 ++++++++++++++++++++++++++++++++++++ 2 files changed, 37 insertions(+) create mode 100644 docs/mst.rst diff --git a/docs/index.rst b/docs/index.rst index a032d3dd..150cf911 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -40,6 +40,7 @@ by reading the :doc:`best_practises`. model_fitting.rst model_distribution.rst visualisation.rst + mst.rst subclustering.rst troubleshooting.rst options.rst diff --git a/docs/mst.rst b/docs/mst.rst new file mode 100644 index 00000000..4dd6bb88 --- /dev/null +++ b/docs/mst.rst @@ -0,0 +1,36 @@ +Minimum spanning trees +======================= + +**Contents**: + +.. contents:: + :local: + +Using the distances and a network, you can generate a minimum spanning tree. This +can be useful when a neigbour joining tree is difficult to produce, for example +if the dataset is very large, and in some cases has uses in tracing spread +(take care with this interpretation, direction is not usually obvious). + +There are three different ways to make MSTs, depending on how much data you have. +Roughly: + +- 'Small': Up to :math:`\sim 10^3` samples. +- 'Medium': Up to :math:`\sim 10^5` samples. +- 'Large': Over :math:`10^5` samples. + +In each mode, you can get as output: + +- A plot of the MST as a graph layout, optionally coloured by strain. +- A plot of the MST as a graph layout, highlighting edge betweenness and node degree. +- The graph as a graphml file, to view in :doc:`cytoscape`. +- The MST formatted as a newick file, to view in a tree viewer of your choice. + +With small data +--------------- +For a small dataset it's feasible to simply make an MST from your dense distance matrix + +With medium data +---------------- + +With big data +------------- \ No newline at end of file From f1cc5c2cf061f8863cca5e8b7b319649d9e9406e Mon Sep 17 00:00:00 2001 From: John Lees Date: Sat, 30 Jan 2021 13:06:17 +0000 Subject: [PATCH 70/73] Avoid graph tools import in visualise --- PopPUNK/__main__.py | 3 +++ PopPUNK/network.py | 4 +-- PopPUNK/visualise.py | 60 ++++++++++++++++++++++---------------------- 3 files changed, 35 insertions(+), 32 deletions(-) diff --git a/PopPUNK/__main__.py b/PopPUNK/__main__.py index cfdbd86e..4c59b145 100644 --- a/PopPUNK/__main__.py +++ b/PopPUNK/__main__.py @@ -16,6 +16,9 @@ # import poppunk package from .__init__ import __version__ +# globals +accepted_weights_types = ["core", "accessory", "euclidean"] + #******************************# #* *# #* Command line parsing *# diff --git a/PopPUNK/network.py b/PopPUNK/network.py index fd2d3951..3e993600 100644 --- a/PopPUNK/network.py +++ b/PopPUNK/network.py @@ -22,6 +22,8 @@ import graph_tool.all as gt import dendropy +from .__main__ import accepted_weights_types + from .sketchlib import addRandom from .utils import iterDistRows @@ -31,8 +33,6 @@ from .utils import setupDBFuncs from .utils import isolateNameToLabel -accepted_weights_types = ["core", "accessory", "euclidean"] - def fetchNetwork(network_dir, model, refList, ref_graph = False, core_only = False, accessory_only = False): """Load the network based on input options diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index 7365c462..fc94c8ab 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -15,36 +15,6 @@ # import poppunk package from .__init__ import __version__ -from .models import loadClusterFit - -from .network import constructNetwork -from .network import fetchNetwork -from .network import generate_minimum_spanning_tree -from .network import accepted_weights_types - -from .plot import drawMST -from .plot import outputsForMicroreact -from .plot import outputsForCytoscape -from .plot import outputsForPhandango -from .plot import outputsForGrapetree -from .plot import writeClusterCsv - -from .prune_db import prune_distance_matrix - -from .sketchlib import readDBParams -from .sketchlib import getKmersFromReferenceDatabase -from .sketchlib import addRandom - -from .trees import load_tree, generate_nj_tree, mst_to_phylogeny - -from .utils import isolateNameToLabel -from .utils import readPickle -from .utils import setGtThreads -from .utils import update_distance_matrices -from .utils import readIsolateTypeFromCsv -from .utils import joinClusterDicts -from .utils import listDistInts - #******************************# #* *# #* Command line parsing *# @@ -53,6 +23,7 @@ def get_options(): import argparse + from .__main__ import accepted_weights_types parser = argparse.ArgumentParser(description='Create visualisations from PopPUNK results', prog='poppunk_visualise') @@ -181,6 +152,35 @@ def generate_visualisations(query_db, accessory_only, web): + from .models import loadClusterFit + + from .network import constructNetwork + from .network import fetchNetwork + from .network import generate_minimum_spanning_tree + + from .plot import drawMST + from .plot import outputsForMicroreact + from .plot import outputsForCytoscape + from .plot import outputsForPhandango + from .plot import outputsForGrapetree + from .plot import writeClusterCsv + + from .prune_db import prune_distance_matrix + + from .sketchlib import readDBParams + from .sketchlib import getKmersFromReferenceDatabase + from .sketchlib import addRandom + + from .trees import load_tree, generate_nj_tree, mst_to_phylogeny + + from .utils import isolateNameToLabel + from .utils import readPickle + from .utils import setGtThreads + from .utils import update_distance_matrices + from .utils import readIsolateTypeFromCsv + from .utils import joinClusterDicts + from .utils import listDistInts + # Check on parallelisation of graph-tools setGtThreads(threads) From d8d1cc0de148392049ec29a5fbd106313a283a0c Mon Sep 17 00:00:00 2001 From: John Lees Date: Sat, 30 Jan 2021 14:27:39 +0000 Subject: [PATCH 71/73] Update docs --- PopPUNK/visualise.py | 4 +- docs/images/mst_medium_clusters.png | Bin 0 -> 1220938 bytes docs/images/mst_medium_stress.png | Bin 0 -> 929698 bytes docs/images/mst_small_clusters.png | Bin 0 -> 737248 bytes docs/images/mst_small_stress.png | Bin 0 -> 359550 bytes docs/installation.rst | 69 +++++++++++ docs/mst.rst | 172 ++++++++++++++++++++++++++-- 7 files changed, 235 insertions(+), 10 deletions(-) create mode 100644 docs/images/mst_medium_clusters.png create mode 100644 docs/images/mst_medium_stress.png create mode 100644 docs/images/mst_small_clusters.png create mode 100644 docs/images/mst_small_stress.png diff --git a/PopPUNK/visualise.py b/PopPUNK/visualise.py index fc94c8ab..d767a54d 100644 --- a/PopPUNK/visualise.py +++ b/PopPUNK/visualise.py @@ -88,9 +88,9 @@ def get_options(): faGroup.add_argument('--cytoscape', help='Generate network output files for Cytoscape', default=False, action='store_true') faGroup.add_argument('--phandango', help='Generate phylogeny and TSV for Phandango visualisation', default=False, action='store_true') faGroup.add_argument('--grapetree', help='Generate phylogeny and CSV for grapetree visualisation', default=False, action='store_true') - faGroup.add_argument('--tree', help='Type of tree to calculate (nj, mst or both)', type=str, default='nj', + faGroup.add_argument('--tree', help='Type of tree to calculate [default = nj]', type=str, default='nj', choices=['nj', 'mst', 'both']) - faGroup.add_argument('--mst-distances', help='Distances used to calculate a minimum spanning tree', type=str, + faGroup.add_argument('--mst-distances', help='Distances used to calculate a minimum spanning tree [default = core]', type=str, default='core', choices=accepted_weights_types) faGroup.add_argument('--rapidnj', help='Path to rapidNJ binary to build NJ tree for Microreact', default='rapidnj') diff --git a/docs/images/mst_medium_clusters.png b/docs/images/mst_medium_clusters.png new file mode 100644 index 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