class GeneInteractions(object):
def __init__(self):
self.blw = BioLinkWrapper()
self.meta = {
'input_type': {
'complexity': 'set',
'id_type': 'HGNC',
'data_type': 'gene',
},
'output_type': {
'complexity': 'set',
'id_type': 'HGNC',
'data_type': 'gene',
},
'source': 'Monarch Biolink',
'predicate': ['blm:interacts with']
}
def metadata(self):
print("""Mod1E Interaction Network metadata:""")
pprint(self.meta)
@staticmethod
def load_gene_set(input_gene_set):
annotated_gene_set = []
for gene in input_gene_set.get_input_curie_set():
annotated_gene_set.append({
'input_id': gene['hit_id'],
'sim_input_curie': gene['hit_id'],
'input_symbol': gene['hit_symbol']
})
return annotated_gene_set
def get_interactions(self, annotated_gene_set):
results = []
for gene in annotated_gene_set:
interactions = self.blw.gene_interactions(gene_curie=gene['sim_input_curie'])
for assoc in interactions['associations']:
interaction = \
self.blw.parse_association(
input_id=gene['sim_input_curie'],
input_label=gene['input_symbol'],
association=assoc
)
results.append({
'input_id': interaction['input_id'],
'input_symbol': interaction['input_symbol'],
'hit_symbol': interaction['hit_symbol'],
'hit_id': interaction['hit_id'],
'score': 0,
})
return results
class GeneInteractions(object):
def __init__(self):
self.blw = BioLinkWrapper()
self.gene_set = []
self.input_object = ''
self.meta = {
'input_type': {
'complexity': 'set',
'id_type': 'HGNC',
'data_type': 'gene',
},
'output_type': {
'complexity': 'set',
'id_type': 'HGNC',
'data_type': 'gene',
},
'source': 'Monarch Biolink',
'predicate': ['blm:interacts with']
}
def load_input_object(self, input_object):
self.input_object = input_object
def load_gene_set(self):
for gene in self.input_object['input']:
self.gene_set.append({
'input_id': gene['hit_id'],
'sim_input_curie': gene['hit_id'],
'input_symbol': gene['hit_symbol']
})
def get_interactions(self):
results = []
for gene in self.gene_set:
interactions = self.blw.gene_interactions(
gene_curie=gene['sim_input_curie'])
for assoc in interactions['associations']:
interaction = self.blw.parse_association(
input_id=gene['sim_input_curie'],
input_label=gene['input_symbol'],
association=assoc)
results.append({
'input_id': interaction['input_id'],
'input_symbol': interaction['input_symbol'],
'hit_symbol': interaction['hit_symbol'],
'hit_id': interaction['hit_id'],
'score': 0,
})
return results
class GeneInteractions(object):
def __init__(self):
self.blw = BioLinkWrapper(Config().get_biolink_api_endpoint())
self.meta = {
'input_type': {
'complexity': 'set',
'id_type': 'HGNC',
'data_type': 'gene',
},
'output_type': {
'complexity': 'set',
'id_type': 'HGNC',
'data_type': 'gene',
},
'source': 'Monarch Biolink',
'predicate': ['blm:interacts with']
}
def metadata(self):
print("""Mod1E Interaction Network metadata:""")
pprint(self.meta)
@staticmethod
# RMB: July 5, 2019 - gene_records is a Pandas DataFrame
def load_gene_set(gene_records):
annotated_gene_set = []
for gene in gene_records.to_dict(orient='records'):
annotated_gene_set.append({
'input_id': gene['hit_id'],
'sim_input_curie': gene['hit_id'],
'input_symbol': gene['hit_symbol']
})
return annotated_gene_set
def get_interactions(self, input_gene_set, threshold):
annotated_input_gene_set = self.load_gene_set(input_gene_set)
lower_bound = int(threshold)
results = []
for gene in annotated_input_gene_set:
interactions = self.blw.gene_interactions(gene_curie=gene['sim_input_curie'])
for assoc in interactions['associations']:
interaction = \
self.blw.parse_association(
input_id=gene['sim_input_curie'],
input_label=gene['input_symbol'],
association=assoc
)
results.append({
'input_id': interaction['input_id'],
'input_symbol': interaction['input_symbol'],
'hit_symbol': interaction['hit_symbol'],
'hit_id': interaction['hit_id'],
'score': 0,
})
# Process the results
results = pd.DataFrame().from_records(results)
counts = results['hit_symbol'].value_counts().rename_axis('unique_values').to_frame('counts').reset_index()
high_counts = counts[counts['counts'] > lower_bound]['unique_values'].tolist()
results = pd.DataFrame(results[results['hit_symbol'].isin(high_counts)])
return results