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WF1Mod1.py
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WF1Mod1.py
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# solves the SME workflow #1: drug repurposing based on rare diseases
import os
import sys
import argparse
# PyCharm doesn't play well with relative imports + python console + terminal
try:
from code.reasoningtool import ReasoningUtilities as RU
except ImportError:
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import ReasoningUtilities as RU
import FormatOutput
import networkx as nx
try:
from QueryCOHD import QueryCOHD
except ImportError:
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
try:
from QueryCOHD import QueryCOHD
except ImportError:
sys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), 'kg-construction'))
from QueryCOHD import QueryCOHD
import NormGoogleDistance
NormGoogleDistance = NormGoogleDistance.NormGoogleDistance()
class SMEDrugRepurposingFisher:
def __init__(self):
None
@staticmethod
def answer(disease_id, use_json=False, num_show=20):
num_input_disease_symptoms = 50 # number of representative symptoms of the disease to keep
num_omim_keep = 25 # number of genetic conditions to keep
num_paths = 1 # number of paths to keep for each drug selected
# Initialize the response class
response = FormatOutput.FormatResponse(6)
response.response.table_column_names = ["disease name", "disease ID", "drug name", "drug ID", "confidence"]
# get the description of the disease
disease_description = RU.get_node_property(disease_id, 'name')
# Find symptoms of disease
# symptoms = RU.get_one_hop_target("disease", disease_id, "phenotypic_feature", "has_phenotype")
# symptoms_set = set(symptoms)
(symptoms_dict, symptoms) = RU.top_n_fisher_exact([disease_id], "disease", "phenotypic_feature", rel_type="has_phenotype", n=num_input_disease_symptoms)
symptoms_set = set(symptoms)
# check for an error
if not symptoms_set:
error_message = "I found no phenotypic_features for %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# Find diseases enriched for that phenotype
path_type = ["gene_mutations_contribute_to", "protein"]
(genetic_diseases_dict, genetic_diseases_selected) = RU.top_n_fisher_exact(symptoms, "phenotypic_feature",
"disease", rel_type="has_phenotype",
n=num_omim_keep, curie_prefix="OMIM",
on_path=path_type,
exclude=disease_id)
if not genetic_diseases_selected:
error_message = "I found no diseases connected to phenotypes of %s." % disease_description
if not use_json:
print(error_message)
return
else:
error_code = "NoPathsFound"
response = FormatOutput.FormatResponse(3)
response.add_error_message(error_code, error_message)
response.print()
return
# Next, find the most likely paths
# extract the relevant subgraph
path_type = ["disease", "has_phenotype", "phenotypic_feature", "has_phenotype", "disease"]
g = RU.get_subgraph_through_node_sets_known_relationships(path_type,
[[disease_id], symptoms, genetic_diseases_selected])
# decorate graph with fisher p-values
# get dict of id to nx nodes
nx_node_to_id = nx.get_node_attributes(g, "names")
nx_id_to_node = dict()
# reverse the dictionary
for node in nx_node_to_id.keys():
id = nx_node_to_id[node]
nx_id_to_node[id] = node
i = 0
for u, v, d in g.edges(data=True):
u_id = nx_node_to_id[u]
v_id = nx_node_to_id[v]
# decorate correct nodes
# input disease to symptoms, decorated by symptom p-value
if (u_id in symptoms_set and v_id == disease_id) or (v_id in symptoms_set and u_id == disease_id):
try:
d["p_value"] = symptoms_dict[v_id]
except:
d["p_value"] = symptoms_dict[u_id]
continue
# symptom to disease, decorated by disease p-value
if (u_id in symptoms_set and v_id in genetic_diseases_dict) or (
v_id in symptoms_set and u_id in genetic_diseases_dict):
try:
d["p_value"] = genetic_diseases_dict[v_id]
except:
d["p_value"] = genetic_diseases_dict[u_id]
continue
# decorate with COHD data
RU.weight_disease_phenotype_by_cohd(g, max_phenotype_oxo_dist=2,
default_value=1) # automatically pulls it out to top-level property
# transform the graph properties so they all point the same direction
# will be finding shortest paths, so make 0=bad, 1=good transform to 0=good, 1=bad
RU.transform_graph_weight(g, "cohd_freq", default_value=0,
transformation=lambda x: 1 / float(x + .001) - 1 / (1 + .001))
# merge the graph properties (additively)
RU.merge_graph_properties(g, ["p_value", "cohd_freq"], "merged", operation=lambda x, y: x + y)
#RU.merge_graph_properties(g, ["p_value", "cohd_freq"], "merged", operation=lambda x, y: x + y)
graph_weight_tuples = []
for disease in genetic_diseases_selected:
#decorated_paths, decorated_path_edges, path_lengths = RU.get_top_shortest_paths(g, disease_id, disease,
# num_paths,
# property='merged')
decorated_paths, decorated_path_edges, path_lengths = RU.get_top_shortest_paths(g, disease_id, disease,
num_paths,
property='p_value')
for path_ind in range(num_paths):
g2 = nx.Graph()
path = decorated_paths[path_ind]
for node_prop in path:
node_uuid = node_prop['properties']['UUID']
g2.add_node(node_uuid, **node_prop)
path = decorated_path_edges[path_ind]
for edge_prop in path:
source_uuid = edge_prop['properties']['source_node_uuid']
target_uuid = edge_prop['properties']['target_node_uuid']
g2.add_edge(source_uuid, target_uuid, **edge_prop)
graph_weight_tuples.append((g2, path_lengths[path_ind], disease))
# sort by the path weight
graph_weight_tuples.sort(key=lambda x: x[1])
# print out the results
if not use_json:
for graph, weight, out_disease_id in graph_weight_tuples:
out_disease_description = RU.get_node_property(out_disease_id, "name", node_label="disease")
print("%s %f" % (out_disease_description, weight))
else:
response.response.table_column_names = ["input disease name", "input disease ID", "output disease name", "output disease ID", "path weight"]
for graph, weight, out_disease_id in graph_weight_tuples:
out_disease_description = RU.get_node_property(out_disease_id, "name", node_label="disease")
# Machine learning probability of "treats"
confidence = 1 - weight # for the p-values
# Google distance
#gd = NormGoogleDistance.get_ngd_for_all([out_disease_id, disease_id], [out_disease_description, disease_description])
# populate the graph
res = response.add_subgraph(graph.nodes(data=True), graph.edges(data=True),
"The monogenic condition %s is enriched for shared phenotypes with %s." % (
out_disease_description, disease_description), confidence,
return_result=True)
res.essence = "%s" % out_disease_description # populate with essence of question result
row_data = [] # initialize the row data
row_data.append("%s" % disease_description)
row_data.append("%s" % disease_id)
row_data.append("%s" % out_disease_description)
row_data.append("%s" % out_disease_id)
row_data.append("%f" % weight)
res.row_data = row_data
response.print()
@staticmethod
def describe():
output = "Answers questions of the form: 'What are some potential treatments for $disease?'" + "\n"
# TODO: subsample disease nodes
return output
def main():
parser = argparse.ArgumentParser(description="Answers questions of the form: 'What are some potential treatments for $disease?'",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-d', '--disease', type=str, help="disease curie ID", default="DOID:9352") #OMIM:603903
parser.add_argument('-j', '--json', action='store_true', help='Flag specifying that results should be printed in JSON format (to stdout)', default=False)
parser.add_argument('--describe', action='store_true', help='Print a description of the question to stdout and quit', default=False)
parser.add_argument('--num_show', type=int, help='Maximum number of results to return', default=20)
# Parse and check args
args = parser.parse_args()
disease_id = args.disease
use_json = args.json
describe_flag = args.describe
num_show = args.num_show
# Initialize the question class
Q = SMEDrugRepurposingFisher()
if describe_flag:
res = Q.describe()
print(res)
else:
Q.answer(disease_id, use_json=use_json, num_show=num_show)
if __name__ == "__main__":
main()