def _add_evidence(
self,
assoc_id,
eco_id,
p_value,
percentage_change,
effect_size,
study_bnode
):
"""
:param assoc_id: assoc curie used to reify a
genotype to phenotype association, generated in _process_data()
:param eco_id: eco_id as curie, hardcoded in _process_data()
:param p_value: str, from self.files['all']
:param percentage_change: str, from self.files['all']
:param effect_size: str, from self.files['all']
:param study_bnode: str, from self.files['all']
:param phenotyping_center: str, from self.files['all']
:return: str, evidence_line_bnode as curie
"""
evidence_model = Evidence(self.graph, assoc_id)
provenance_model = Provenance(self.graph)
model = Model(self.graph)
# Add line of evidence
evidence_line_bnode = self.make_id(
"{0}{1}".format(assoc_id, study_bnode), '_')
evidence_model.add_supporting_evidence(evidence_line_bnode)
model.addIndividualToGraph(evidence_line_bnode, None, eco_id)
# Add supporting measurements to line of evidence
measurements = {}
if p_value is not None or p_value != "":
p_value_bnode = self.make_id(
"{0}{1}{2}".format(evidence_line_bnode, 'p_value', p_value), '_')
model.addIndividualToGraph(p_value_bnode, None, self.globaltt['p-value'])
try:
measurements[p_value_bnode] = float(p_value)
except ValueError:
measurements[p_value_bnode] = p_value
if percentage_change is not None and percentage_change != '':
fold_change_bnode = self.make_id(
"{0}{1}{2}".format(
evidence_line_bnode, 'percentage_change', percentage_change), '_')
model.addIndividualToGraph(
fold_change_bnode, None, self.resolve('percentage_change'))
measurements[fold_change_bnode] = percentage_change
if effect_size is not None or effect_size != "":
fold_change_bnode = self.make_id(
"{0}{1}{2}".format(
evidence_line_bnode, 'effect_size', effect_size), '_')
model.addIndividualToGraph(
fold_change_bnode, None, self.globaltt['effect size estimate'])
measurements[fold_change_bnode] = effect_size
evidence_model.add_supporting_data(evidence_line_bnode, measurements)
# Link evidence to provenance by connecting to study node
provenance_model.add_study_to_measurements(study_bnode, measurements.keys())
self.graph.addTriple(
evidence_line_bnode, self.globaltt['has_supporting_activity'],
study_bnode)
return evidence_line_bnode
def _add_evidence(
self,
assoc_id,
eco_id,
p_value,
percentage_change,
effect_size,
study_bnode
):
"""
:param assoc_id: assoc curie used to reify a
genotype to phenotype association, generated in _process_data()
:param eco_id: eco_id as curie, hardcoded in _process_data()
:param p_value: str, from self.files['all']
:param percentage_change: str, from self.files['all']
:param effect_size: str, from self.files['all']
:param study_bnode: str, from self.files['all']
:param phenotyping_center: str, from self.files['all']
:return: str, evidence_line_bnode as curie
"""
evidence_model = Evidence(self.graph, assoc_id)
provenance_model = Provenance(self.graph)
model = Model(self.graph)
# Add line of evidence
evidence_line_bnode = self.make_id(
"{0}{1}".format(assoc_id, study_bnode), '_')
evidence_model.add_supporting_evidence(evidence_line_bnode)
model.addIndividualToGraph(evidence_line_bnode, None, eco_id)
# Add supporting measurements to line of evidence
measurements = {}
if p_value is not None or p_value != "":
p_value_bnode = self.make_id(
"{0}{1}{2}".format(evidence_line_bnode, 'p_value', p_value), '_')
model.addIndividualToGraph(p_value_bnode, None, self.globaltt['p-value'])
try:
measurements[p_value_bnode] = float(p_value)
except ValueError:
measurements[p_value_bnode] = p_value
if percentage_change is not None and percentage_change != '':
fold_change_bnode = self.make_id(
"{0}{1}{2}".format(
evidence_line_bnode, 'percentage_change', percentage_change), '_')
model.addIndividualToGraph(
fold_change_bnode, None, self.resolve('percentage_change'))
measurements[fold_change_bnode] = percentage_change
if effect_size is not None or effect_size != "":
fold_change_bnode = self.make_id(
"{0}{1}{2}".format(
evidence_line_bnode, 'effect_size', effect_size), '_')
model.addIndividualToGraph(
fold_change_bnode, None, self.globaltt['effect size estimate'])
measurements[fold_change_bnode] = effect_size
evidence_model.add_supporting_data(evidence_line_bnode, measurements)
# Link evidence to provenance by connecting to study node
provenance_model.add_study_to_measurements(study_bnode, measurements.keys())
self.graph.addTriple(
evidence_line_bnode, self.globaltt['has_evidence_item_output_from'], study_bnode)
return evidence_line_bnode
def _add_outcome_evidence(self, association, outcome):
"""
:param association: str association curie
:param outcome: dict (json)
:return: None
"""
evidence = Evidence(self.graph, association)
source = {
'curie': "DOI:10.5061/dryad.8q0s4/1",
'label': "Data from: A curated and standardized adverse "
"drug event resource to accelerate drug safety research",
'type': self.globaltt['data set']
}
reference = {
'curie': "PMID:27193236",
'label': None,
'type': self.globaltt['publication']
}
evidence_curie = self.make_id("{0}{1}{2}".format(
association, outcome['id'], self.name))
evidence_type = self.globaltt['clinical study evidence']
evidence.add_supporting_evidence(evidence_curie, evidence_type)
evidence.add_supporting_publication(evidence_curie, reference['curie'],
reference['label'],
reference['type'])
evidence.add_source(evidence_curie, source['curie'], source['label'],
source['type'])
count_bnode = self.make_id("{0}{1}{2}".format(evidence_curie,
outcome['case_count'],
self.name),
prefix="_")
pr_ratio_bnode = self.make_id("{0}{1}{2}{3}".format(
evidence_curie, outcome['prr'], self.name, 'prr'),
prefix="_")
odds_ratio_bnode = self.make_id("{0}{1}{2}{3}".format(
evidence_curie, outcome['ror'], self.name, 'ror'),
prefix="_")
evidence.add_data_individual(count_bnode,
ind_type=self.globaltt['count'])
evidence.add_data_individual(
pr_ratio_bnode,
ind_type=self.globaltt['proportional_reporting_ratio'])
evidence.add_data_individual(odds_ratio_bnode,
ind_type=self.globaltt['odds_ratio'])
value_map = {
count_bnode: outcome['case_count'],
pr_ratio_bnode: outcome['prr'],
odds_ratio_bnode: outcome['ror']
}
evidence.add_supporting_data(evidence_curie, value_map)
return
def _add_outcome_evidence(self, association, outcome):
"""
:param association: str association curie
:param outcome: dict (json)
:return: None
"""
evidence = Evidence(self.graph, association)
source = {
'curie': "DOI:10.5061/dryad.8q0s4/1",
'label': "Data from: A curated and standardized adverse "
"drug event resource to accelerate drug safety research",
'type': self.globaltt['data set']
}
reference = {
'curie': "PMID:27193236",
'label': None,
'type': self.globaltt['publication']
}
evidence_curie = self.make_id("{0}{1}{2}".format(
association, outcome['id'], self.name
))
evidence_type = self.globaltt['clinical study evidence']
evidence.add_supporting_evidence(evidence_curie, evidence_type)
evidence.add_supporting_publication(
evidence_curie, reference['curie'], reference['label'],
reference['type'])
evidence.add_source(
evidence_curie, source['curie'], source['label'], source['type'])
count_bnode = self.make_id(
"{0}{1}{2}".format(evidence_curie,
outcome['case_count'], self.name), prefix="_")
pr_ratio_bnode = self.make_id(
"{0}{1}{2}{3}".format(evidence_curie, outcome['prr'], self.name, 'prr'),
prefix="_")
odds_ratio_bnode = self.make_id(
"{0}{1}{2}{3}".format(evidence_curie, outcome['ror'], self.name, 'ror'),
prefix="_")
evidence.add_data_individual(
count_bnode, ind_type=self.globaltt['count'])
evidence.add_data_individual(
pr_ratio_bnode,
ind_type=self.globaltt['proportional_reporting_ratio'])
evidence.add_data_individual(
odds_ratio_bnode, ind_type=self.globaltt['odds_ratio'])
value_map = {
count_bnode: outcome['case_count'],
pr_ratio_bnode: outcome['prr'],
odds_ratio_bnode: outcome['ror']
}
evidence.add_supporting_data(evidence_curie, value_map)
return