from app.funcs.query_processors import (
NetworkPlotSchemaInput,
TopicQueryProcessor,
)
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "mr"
EXPOSURE_DESC = "Exposure {Gwas}.".format(Gwas=data_table_node_link("Gwas"))
OUTCOME_DESC = "Outcome {Gwas}.".format(Gwas=data_table_node_link("Gwas"))
MR_DESC = """
Mendelian randomization {MR_EVE_MR}
evidence from exposure to outcome.
""".format(MR_EVE_MR=data_table_rel_link("MR_EVE_MR"))
table_col_configs = {
"exposure.id": NodeCol("Gwas", "id", EXPOSURE_DESC),
"exposure.trait": NodeCol("Gwas", "trait", EXPOSURE_DESC),
"outcome.id": NodeCol("Gwas", "id", OUTCOME_DESC),
"outcome.trait": NodeCol("Gwas", "trait", OUTCOME_DESC),
"mr.b": RelCol("MR_EVE_MR", "b", MR_DESC, rounding=True),
"mr.se": RelCol("MR_EVE_MR", "se", MR_DESC, rounding=True),
"mr.pval": RelCol("MR_EVE_MR", "pval", MR_DESC),
"mr.method": RelCol("MR_EVE_MR", "method", MR_DESC),
"mr.selection": RelCol("MR_EVE_MR", "selection", MR_DESC),
"mr.moescore": RelCol("MR_EVE_MR", "moescore", MR_DESC, rounding=True),
}
class MRQueryProcessor(TopicQueryProcessor):
from app.funcs.query_processors import (
NetworkPlotSchemaInput,
TopicQueryProcessor,
)
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "pathway"
GWAS_DESC = "{Gwas} trait of interests".format(
Gwas=data_table_node_link("Gwas"))
GWAS_TO_VARIANT_DESC = """
Identified association {GWAS_TO_VARIANT} between {Gwas} and {Variant}
""".format(
GWAS_TO_VARIANT=data_table_rel_link("GWAS_TO_VARIANT"),
Gwas=data_table_node_link("Gwas"),
Variant=data_table_node_link("Variant"),
)
VARIANT_DESC = "Associated {Variant}".format(
Variant=data_table_node_link("Variant"))
GENE_DESC = "Associated {Gene}".format(Gene=data_table_node_link("Gene"))
PROTEIN_DESC = "Associated {Protein}".format(
Protein=data_table_node_link("Protein"))
PATHWAY_DESC = "Biological {Pathway}".format(
Pathway=data_table_node_link("Pathway"))
table_col_configs = {
"gwas.id":
NodeCol("Gwas", "id", GWAS_DESC),
"gwas.trait":
NodeCol("Gwas", "trait", GWAS_DESC),
TopicQueryProcessor,
)
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "drugs_risk_factors"
api_endpoint = "drugs/risk-factors"
TRAIT_DESC = "Disease {Gwas} trait.".format(Gwas=data_table_node_link("Gwas"))
ASSOC_TRAIT_DESC = """
Risk factor {Gwas} trait that is identified to
have {MR_EVE_MR} evidence to the disease trait.
""".format(
Gwas=data_table_node_link("Gwas"),
MR_EVE_MR=data_table_rel_link("MR_EVE_MR"),
)
DRUG_DESC = """
{Drug} that is identified to be associated with the disease {Gwas} trait
via the path as shown in the network plot.
""".format(Gwas=data_table_node_link("Gwas"),
Drug=data_table_node_link("Drug"))
GENE_DESC = "Associated {Gene}".format(Gene=data_table_node_link("Gene"))
MR_DESC = """
Mendelian randomization {MR_EVE_MR} evidence
from the risk factor {Gwas} to the disease {Gwas}.
""".format(
MR_EVE_MR=data_table_rel_link("MR_EVE_MR"),
Gwas=data_table_node_link("Gwas"),
)
VARIANT_DESC = "Associated {Variant}".format(
from app.funcs.query_processors import (
NetworkPlotSchemaInput,
TopicQueryProcessor,
)
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "obs-cor"
TRAIT_DESC = "{Gwas} trait of interests.".format(
Gwas=data_table_node_link("Gwas"))
ASSOC_TRAIT_DESC = "Associated {Gwas} trait.".format(
Gwas=data_table_node_link("Gwas"))
OBS_COR_DESC = "Observational correlation {OBS_COR} between the two {Gwas} traits.".format(
Gwas=data_table_node_link("Gwas"), OBS_COR=data_table_rel_link("OBS_COR"))
table_col_configs = {
"trait.id": NodeCol("Gwas", "id", TRAIT_DESC),
"trait.trait": NodeCol("Gwas", "trait", TRAIT_DESC),
"obs_cor.cor": RelCol("OBS_COR", "cor", OBS_COR_DESC, rounding=True),
"assoc_trait.id": NodeCol("Gwas", "id", ASSOC_TRAIT_DESC),
"assoc_trait.trait": NodeCol("Gwas", "trait", ASSOC_TRAIT_DESC),
}
class ObsCorQueryProcessor(TopicQueryProcessor):
def __init__(self, params: Dict[str, Any]):
super().__init__(
master_name=master_name,
params=params,
table_col_configs=table_col_configs,
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "ontology_trait_disease"
api_endpoint = "ontology/gwas-efo-disease"
GWAS_DESC = "{Gwas} trait of interest.".format(
Gwas=data_table_node_link("Gwas")
)
GE_DESC = """
Mapping from {Gwas} to {Efo} via semantic similarity {GWAS_NLP_EFO}.
""".format(
Gwas=data_table_node_link("Gwas"),
Efo=data_table_node_link("Efo"),
GWAS_NLP_EFO=data_table_rel_link("GWAS_NLP_EFO"),
)
EFO_DESC = "Experimental Factor Ontology {Efo}".format(
Efo=data_table_node_link("Efo")
)
DISEASE_DESC = "{Disease} term".format(Disease=data_table_node_link("Disease"))
table_col_configs = {
"gwas.id": NodeCol("Gwas", "id", GWAS_DESC),
"gwas.trait": NodeCol("Gwas", "trait", GWAS_DESC),
"ge.score": RelCol("GWAS_NLP_EFO", "score", GE_DESC),
"efo.type": NodeCol("Efo", "type", EFO_DESC),
"efo.value": NodeCol("Efo", "value", EFO_DESC),
"efo.id": NodeCol("Efo", "id", EFO_DESC),
"disease.id": NodeCol("Disease", "id", DISEASE_DESC),
"disease.label": NodeCol("Disease", "label", DISEASE_DESC),
}
TopicQueryProcessor,
)
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "literature_trait"
api_endpoint = "literature/gwas"
GWAS_DESC = "{Gwas} trait of interest.".format(
Gwas=data_table_node_link("Gwas")
)
GS_DESC = "Association between {Gwas} and {LiteratureTriple} via {GWAS_TO_LITERATURE_TRIPLE}.".format(
Gwas=data_table_node_link("Gwas"),
LiteratureTriple=data_table_node_link("LiteratureTriple"),
GWAS_TO_LITERATURE_TRIPLE=data_table_rel_link("GWAS_TO_LITERATURE_TRIPLE"),
)
TRIPLE_DESC = """
The SemMedDB knowledge triplet {LiteratureTriple} that links the {Gwas} trait
with a SemMedDB term {LiteratureTerm}.
""".format(
LiteratureTriple=data_table_node_link("LiteratureTriple"),
Gwas=data_table_node_link("Gwas"),
LiteratureTerm=data_table_node_link("LiteratureTerm"),
)
LIT_DESC = """
{Literature} evidence regarding the {Gwas} trait as a PubMed article.
""".format(
Literature=data_table_node_link("Literature"),
Gwas=data_table_node_link("Gwas"),
)
NetworkPlotSchemaInput,
TopicQueryProcessor,
)
from app.utils.data_table import NodeCol, RelCol
from app.utils.url_helpers import data_table_node_link, data_table_rel_link
from .graph import edge_schemas, node_schemas
master_name = "prs"
TRAIT_DESC = "The {gwas} trait of interests.".format(
gwas=data_table_node_link("Gwas"))
ASSOC_TRAIT_DESC = "The associated {gwas} trait.".format(
gwas=data_table_node_link("Gwas"))
PRS_DESC = "Pre-computed polygenic risk scores {prs} associations.".format(
prs=data_table_rel_link("PRS"))
table_col_configs = {
"trait.id": NodeCol("Gwas", "id", TRAIT_DESC),
"trait.trait": NodeCol("Gwas", "trait", TRAIT_DESC),
"assoc_trait.id": NodeCol("Gwas", "id", ASSOC_TRAIT_DESC),
"assoc_trait.trait": NodeCol("Gwas", "trait", ASSOC_TRAIT_DESC),
"prs.beta": RelCol("PRS", "beta", PRS_DESC, rounding=True),
"prs.se": RelCol("PRS", "se", PRS_DESC, rounding=True),
"prs.p": RelCol("PRS", "p", PRS_DESC),
"prs.r2": RelCol("PRS", "r2", PRS_DESC, rounding=True),
"prs.nsnps": RelCol("PRS", "nsnps", PRS_DESC),
"prs.n": RelCol("PRS", "n", PRS_DESC),
"prs.model": RelCol("PRS", "model", PRS_DESC),
}