def overridden_mock_result_list(
result_list_name="overridden_mock_result_list",
association=GeneToDiseaseAssociation.class_name,
domain=ConceptSpace(
category=Disease.class_name,
id_prefixes=['MONDO']
),
relationship='related_to',
range=ConceptSpace(
category=Gene.class_name,
id_prefixes=['HGNC']
)
):
rl = ResultList(
result_list_name=result_list_name,
source='ncats',
association=association,
domain=domain,
relationship=relationship,
range=range
)
rl.attributes.append(_a)
rl.concepts.append(mock_concept())
rl.concepts.append(mock_concept(identifier=mock_identifier_2()))
rl.results.append(_r)
return rl
def __init__(self, input_genes):
super(
GeneToGeneDepMapBiclusters, self
).__init__(module=BiclusterByGene(
bicluster_url=
'https://smartbag-crispridepmap.ncats.io/biclusters_DepMap_gene_to_cellline_v1_gene/',
bicluster_bicluster_url=
'https://smartbag-crispridepmap.ncats.io/biclusters_DepMap_gene_to_cellline_v1_bicluster/',
target_prefix=''),
metadata=ModuleMetaData(
name="Mod9B - Gene-to-Gene Bicluster",
source='DepMap Biclustering',
association=GeneToGeneAssociation,
domain=ConceptSpace(Gene, ['NCBI']),
relationship='related_to',
range=ConceptSpace(Gene, ['NCBI'])))
input_gene_set = self.get_simple_input_identifier_list(input_genes)
asyncio.run(self.module.gene_to_gene_biclusters_async(input_gene_set))
sorted_list_of_output_genes = self.module.gene_to_gene_bicluster_summarize(
input_gene_set)
self.results = pd.DataFrame.from_records(sorted_list_of_output_genes)
def __init__(self, input_genes):
super(
GeneToGeneBiclusters, self
).__init__(module=BiclusterByGene(
bicluster_url=
'https://bicluster.renci.org/RNAseqDB_bicluster_gene_to_tissue_v3_gene/',
bicluster_bicluster_url=
'https://bicluster.renci.org/RNAseqDB_bicluster_gene_to_tissue_v3_bicluster/',
target_prefix='NCBI'),
metadata=ModuleMetaData(
name="Mod9B - Gene-to-Gene Bicluster",
source='RNAseqDB Biclustering',
association=GeneToGeneAssociation,
domain=ConceptSpace(Gene, ['ENSEMBL']),
relationship='related_to',
range=ConceptSpace(Gene, ['ENSEMBL']),
))
input_gene_set = self.get_simple_input_identifier_list(input_genes)
asyncio.run(self.module.gene_to_gene_biclusters_async(input_gene_set))
sorted_list_of_output_genes = self.module.gene_to_gene_bicluster_summarize(
input_gene_set)
self.results = pd.DataFrame.from_records(sorted_list_of_output_genes)
def __init__(self, input_genes, action='InteractionActions', rows=50):
super(ChemicalGeneInteractionSet,
self).__init__(module=ChemicalGeneInteractions(),
metadata=ModuleMetaData(
name="Module 1B: Chemical Gene Interaction",
source='Chemical Toxicology Database (CTD)',
association=ChemicalToGeneAssociation,
domain=ConceptSpace(Gene, ['HGNC']),
relationship='interacts_with',
range=ConceptSpace(ChemicalSubstance,
['ChemicalID'])))
input_gene_set = self.get_input_data_frame(input_genes)
self.results = self.module.get_gene_chemical_interactions(
input_gene_set, action, rows)
def __init__(self, input_genes, threshold):
super(FunctionallySimilarGenes,
self).__init__(module=FunctionalSimilarity('human'),
metadata=ModuleMetaData(
name="Mod1A Functional Similarity",
source='Monarch Biolink',
association=FunctionalAssociation,
domain=ConceptSpace(Gene, ['HGNC']),
relationship='related_to',
range=ConceptSpace(Gene, ['HGNC']),
))
input_gene_data_frame = self.get_input_data_frame(input_genes)
self.results = self.module.compute_similarity(input_gene_data_frame,
threshold)
def __init__(self, input_genes, threshold):
super(PhenotypicallySimilarGenes, self).__init__(
module=PhenotypeSimilarity('human'),
metadata=ModuleMetaData(
name="Mod1B1 Phenotype Similarity",
source='Monarch Biolink',
association=GeneToPhenotypicFeatureAssociation,
domain=ConceptSpace(Gene, ['HGNC']),
relationship='has_phenotype',
range=ConceptSpace(Gene, ['HGNC']),
)
)
input_gene_data_frame = self.get_input_data_frame(input_genes)
self.results = self.module.compute_similarity(input_gene_data_frame, threshold)
def __init__(self, input_genes, threshold=0):
super(GeneInteractionSet,
self).__init__(module=GeneInteractions(),
metadata=ModuleMetaData(
name="Module 1E - Gene Interaction",
source='Monarch Biolink',
association=GeneToGeneAssociation,
domain=ConceptSpace(Gene, ['HGNC']),
relationship='interacts_with',
range=ConceptSpace(Gene, ['HGNC']),
))
input_gene_data_frame = self.get_input_data_frame(input_genes)
# TODO: add schema check
self.results = self.module.get_interactions(input_gene_data_frame,
threshold)
def __init__(self, input_genes):
super(GeneToTissueBiclusters, self).__init__(
module=BiclusterByGeneToTissue(),
metadata=ModuleMetaData(
name="Mod9A - Gene-to-Tissue Bicluster",
source='RNAseqDB Biclustering',
association=GeneToExpressionSiteAssociation,
domain=ConceptSpace(Gene, ['ENSEMBL']),
relationship='related_to',
range=ConceptSpace(AnatomicalEntity,
['MONDO', 'DOID', 'UBERON']),
))
input_gene_set = self.get_simple_input_identifier_list(input_genes)
most_common_tissues = asyncio.run(
self.module.gene_to_tissue_biclusters_async(input_gene_set))
self.results = pd.DataFrame.from_records(most_common_tissues)
def __init__(self, input_tissues):
super(TissueToTissueBicluster, self).__init__(
module=BiclusterByTissueToTissue(),
metadata=ModuleMetaData(
name="Mod9A - Tissue-to-Tissue Bicluster",
source='RNAseqDB Biclustering',
association=AnatomicalEntityToAnatomicalEntityAssociation,
domain=ConceptSpace(AnatomicalEntity, ['UBERON']),
relationship='related_to',
range=ConceptSpace(AnatomicalEntity, ['UBERON']),
))
input_tissue_ids = self.get_simple_input_identifier_list(input_tissues)
most_common_tissues = asyncio.run(
self.module.tissue_to_tissue_biclusters_async(input_tissue_ids))
self.results = pd.DataFrame.from_records(most_common_tissues,
columns=["hit_id", "score"])
def __init__(self, input_phenotypes):
super(PhenotypeToDiseaseBiclusters, self).__init__(
module=BiclusterByPhenotypeToDisease(),
metadata=ModuleMetaData(
name="Mod9A - Phenotype-to-Disease Bicluster",
source='RNAseqDB Biclustering',
association=DiseaseToPhenotypicFeatureAssociation,
domain=ConceptSpace(PhenotypicFeature, ['HP']),
relationship='has_phenotype',
range=ConceptSpace(Disease, ['MONDO'])))
input_phenotype_ids: List[str] = self.get_simple_input_identifier_list(
input_phenotypes)
most_common_diseases = asyncio.run(
self.module.phenotype_to_disease_biclusters_async(
input_phenotype_ids))
self.results = pd.DataFrame.from_records(most_common_diseases,
columns=["hit_id", "score"])
def __init__(self, disease_id, disease_name='', query_biolink=True):
super(DiseaseAssociatedGeneSet,
self).__init__(module=LookUp(),
metadata=ModuleMetaData(
name="Mod2.0 - Disease Associated Genes",
source='Monarch Biolink',
association=GeneToDiseaseAssociation,
domain=ConceptSpace(Disease, ['MONDO']),
relationship='gene_associated_with_condition',
range=ConceptSpace(Gene, ['HGNC']),
))
# get genes associated with disease from Biolink
self.results = self.module.disease_geneset_lookup(
disease_id, disease_name, query_biolink)
if not self.results.empty:
self.disease_associated_genes = self.results[[
'hit_id', 'hit_symbol'
]].to_dict(orient='records')
else:
self.disease_associated_genes = []
source='ncats',
association=association,
domain=domain,
relationship=relationship,
range=range)
rl.attributes.append(_a)
rl.concepts.append(mock_concept())
rl.concepts.append(mock_concept(identifier=mock_identifier_2()))
rl.results.append(_r)
return rl
_json_test_file = "result_list_test.json"
_mock_uberon_concept_space = ConceptSpace(category=Disease,
id_prefixes=['UBERON'])
_mock_upheno_concept_space = ConceptSpace(category=PhenotypicFeature,
id_prefixes=['UPHENO'])
_mock_hgnc_concept_space = ConceptSpace(category=Gene, id_prefixes=['HGNC'])
_mock_mondo_concept_space = ConceptSpace(category=Disease,
id_prefixes=['MONDO'])
class TestResultList(TestCase):
def test_default_result_list_to_json(self):
rl = default_mock_result_list()
print("\n\nDefault ResultList JSON output: \n", rl.to_json())
def mock_concept_space():
cs = ConceptSpace(category=mock_concept_space_category,
id_prefixes=[mock_concept_space_id_prefixes])
return cs