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import graph_tool.topology as gtt
def scores_to_results(
target,
result_size,
g,
seed_ids,
drug_ids,
scores,
ppi_dataset,
pdi_dataset,
filterPaths
):
r"""Transforms the scores to the required result format."""
node_name_attribute = "drugstone_id" # nodes in the input network which is created from RepoTrialDB have primaryDomainId as name attribute
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candidates = []
# if strain_or_drugs == "drugs":
if target == "drug":
candidates = [(node, scores[node]) for node in drug_ids if scores[node] > 0]
else:
candidates = [(node, scores[node]) for node in range(g.num_vertices()) if scores[node] > 0 and node not in set(seed_ids)]
best_candidates = [item[0] for item in sorted(candidates, key=lambda item: item[1], reverse=True)[:result_size]]
# Concatenate best result candidates with seeds and compute induced subgraph.
# since the result size filters out nodes, the result network is not complete anymore.
# Therefore, it is necessary to find the shortest paths to the found nodes in case intermediate nodes have been removed.
# This leads to more nodes in the result that given in the threshold, but the added intermediate nodes help to understand the output
# and are marked as intermediate nodes.
intermediate_nodes = set()
returned_edges = set()
returned_nodes = set(seed_ids) # return seed_ids in any case
# return only the path to a drug with the shortest distance
if filterPaths:
for candidate in best_candidates:
distances = gtt.shortest_distance(g, candidate, seed_ids)
closest_distance_mean = sum(distances) / len(distances)
for index, seed_id in enumerate(seed_ids):
if distances[index] > closest_distance_mean:
continue
vertices, edges = gtt.shortest_path(g, candidate, seed_id)
drug_in_path = False
for vertex in vertices:
if g.vertex_properties["type"][int(vertex)] == "Drug" and vertex != candidate:
drug_in_path = True
break
if drug_in_path:
continue
for vertex in vertices:
if int(vertex) not in returned_nodes:
# inserting intermediate node in order to make result comprehensive
intermediate_nodes.add(g.vertex_properties[node_name_attribute][int(vertex)])
returned_nodes.add(int(vertex))
for edge in edges:
if ((edge.source(), edge.target()) not in returned_edges) or ((edge.target(), edge.source()) not in returned_edges):
returned_edges.add((edge.source(), edge.target()))
else:
for candidate in best_candidates:
for index, seed_id in enumerate(seed_ids):
vertices, edges = gtt.shortest_path(g, candidate, seed_id)
drug_in_path = False
for vertex in vertices:
if g.vertex_properties["type"][int(vertex)] == "Drug" and vertex != candidate:
drug_in_path = True
break
if drug_in_path:
continue
for vertex in vertices:
if int(vertex) not in returned_nodes:
# inserting intermediate node in order to make result comprehensive
intermediate_nodes.add(g.vertex_properties[node_name_attribute][int(vertex)])
returned_nodes.add(int(vertex))
for edge in edges:
if ((edge.source(), edge.target()) not in returned_edges) or ((edge.target(), edge.source()) not in returned_edges):
returned_edges.add((edge.source(), edge.target()))
subgraph = {
"nodes": [g.vertex_properties[node_name_attribute][node] for node in returned_nodes],
"edges": [{"from": g.vertex_properties[node_name_attribute][source], "to": g.vertex_properties[node_name_attribute][target]} for source, target in returned_edges],
}
# Compute node attributes.
node_types = {g.vertex_properties[node_name_attribute][node]: g.vertex_properties["type"][node] for node in returned_nodes}
is_seed = {g.vertex_properties[node_name_attribute][node]: node in set(seed_ids) for node in returned_nodes}
returned_scores = {g.vertex_properties[node_name_attribute][node]: scores[node] for node in returned_nodes}
return {
"network": subgraph,
'intermediate_nodes': list(intermediate_nodes),
"node_attributes":
{
"node_types": node_types,
"is_seed": is_seed,
"scores": returned_scores
},
'gene_interaction_dataset': ppi_dataset,
'drug_interaction_dataset': pdi_dataset,
}