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novels.py
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#!/usr/bin/env python
__author__ = "Maria J. Falaguera"
__date__ = "08 Nov 2024"
"""
novels.py: List novel target-disease associations in the Open Targets Platform.
"""
# sumbit job to gcloud machine
"""
gcloud dataproc jobs submit pyspark novels.py --cluster=cf-novels --project=open-targets-eu-dev --region="europe-west1"
"""
from pyspark.sql import functions as F
from pyspark.sql import SparkSession, Window, DataFrame
from functools import reduce
# Required data
year = 2023
prioritizedTherapeuticArea = [
"MONDO_0045024", # "cancer or benign tumor",
"EFO_0005741", # "infectious disease",
"OTAR_0000014", # "pregnancy or perinatal disease",
"EFO_0005932", # "animal disease",
"MONDO_0024458", # "disease of visual system",
"EFO_0000319", # "cardiovascular disease",
"EFO_0009605", # "pancreas disease",
"EFO_0010282", # "gastrointestinal disease",
"OTAR_0000017", # "reproductive system or breast disease",
"EFO_0010285", # "integumentary system disease",
"EFO_0001379", # "endocrine system disease",
"OTAR_0000010", # "respiratory or thoracic disease",
"EFO_0009690", # "urinary system disease",
"OTAR_0000006", # "musculoskeletal or connective tissue disease",
"MONDO_0021205", # "disease of ear",
"EFO_0000540", # "immune system disease",
"EFO_0005803", # "hematologic disease",
"EFO_0000618", # "nervous system disease",
"MONDO_0002025", # "psychiatric disorder",
"MONDO_0024297", # "nutritional or metabolic disease",
"OTAR_0000018", # "genetic, familial or congenital disease",
"OTAR_0000009", # "injury, poisoning or other complication",
"EFO_0000651", # "phenotype",
"EFO_0001444", # "measurement",
"GO_0008150", # "biological process"
]
excludeTherapeuticArea = [
"GO_0008150", # biological process
"EFO_0001444", # measurement
"EFO_0002571", # medical procedure
# "EFO_0000651", # phenotype
]
dataSources = [
{
"id": "ot_genetics_portal",
"sectionId": "otGenetics",
"label": "OT Genetics",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0, # needs to be a float
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#open-targets-genetics",
"category": "OTgen",
},
{
"id": "eva",
"sectionId": "eva",
"label": "ClinVar",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#clinvar",
"category": "OTgen",
},
{
"id": "gene_burden",
"sectionId": "geneBurden",
"label": "Gene Burden",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#gene-burden",
"category": "OTgen",
},
{
"id": "genomics_england",
"sectionId": "genomicsEngland",
"label": "GEL PanelApp",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#genomics-england-panelapp",
"category": "OTgen",
},
{
"id": "gene2phenotype",
"sectionId": "gene2Phenotype",
"label": "Gene2phenotype",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#gene2phenotype",
"category": "OTgen",
},
{
"id": "uniprot_literature",
"sectionId": "uniprotLiterature",
"label": "UniProt literature",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#uniprot-literature",
"category": "OTgen",
},
{
"id": "uniprot_variants",
"sectionId": "uniprotVariants",
"label": "UniProt curated variants",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#uniprot-variants",
"category": "OTgen",
},
{
"id": "orphanet",
"sectionId": "orphanet",
"label": "Orphanet",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#orphanet",
"category": "OTgen",
},
{
"id": "clingen",
"sectionId": "clinGen",
"label": "Clingen",
"aggregation": "Genetic association",
"aggregationId": "genetic_association",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#clingen",
"category": "OTgen",
},
{
"id": "cancer_gene_census",
"sectionId": "cancerGeneCensus",
"label": "Cancer Gene Census",
"aggregation": "Somatic mutations",
"aggregationId": "somatic_mutation",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#cancer-gene-census",
"category": "OTgen",
},
{
"id": "intogen",
"sectionId": "intOgen",
"label": "IntOGen",
"aggregation": "Somatic mutations",
"aggregationId": "somatic_mutation",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#intogen",
"category": "OTgen",
},
{
"id": "eva_somatic",
"sectionId": "evaSomatic",
"label": "ClinVar (somatic)",
"aggregation": "Somatic mutations",
"aggregationId": "somatic_mutation",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#clinvar-somatic",
"category": "OTgen",
},
{
"id": "cancer_biomarkers",
"sectionId": "cancerBiomarkers",
"label": "Cancer Biomarkers",
"aggregation": "Somatic mutations",
"aggregationId": "somatic_mutation",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#cancer-biomarkers",
"category": "OTgen",
},
{
"id": "chembl",
"sectionId": "chembl",
"label": "ChEMBL",
"aggregation": "Known drug",
"aggregationId": "known_drug",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#chembl",
"category": "OTchembl",
},
{
"id": "phase=IV",
"sectionId": "phase=IV",
"label": "phase=IV",
"aggregation": "phase=IV",
"aggregationId": "phase=IV",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#chembl",
"category": "OTphase=IV",
},
{
"id": "bioactivity",
"sectionId": "bioactivity",
"label": "Bioactivity",
"aggregation": "bioactivity",
"aggregationId": "bioactivity",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#chembl",
"category": "bioactivity",
},
{
"id": "phase<IV",
"sectionId": "phase<IV",
"label": "phase<IV",
"aggregation": "phase<IV",
"aggregationId": "phase<IV",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#chembl",
"category": "OTphase<IV",
},
{
"id": "crispr_screen",
"sectionId": "crispr_screen",
"label": "CRISPR Screens",
"aggregation": "Affected pathway",
"aggregationId": "affected_pathway",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#project-score",
"category": "OTgen",
},
{
"id": "crispr",
"sectionId": "crispr",
"label": "Project Score",
"aggregation": "Affected pathway",
"aggregationId": "affected_pathway",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#project-score",
"category": "OTgen",
},
{
"id": "slapenrich",
"sectionId": "slapEnrich",
"label": "SLAPenrich",
"aggregation": "Affected pathway",
"aggregationId": "affected_pathway",
"weight": 0.5,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#slapenrich",
"category": "OTpath",
},
{
"id": "progeny",
"sectionId": "progeny",
"label": "PROGENy",
"aggregation": "Affected pathway",
"aggregationId": "affected_pathway",
"weight": 0.5,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#slapenrich",
"category": "OTpath",
},
{
"id": "reactome",
"sectionId": "reactome",
"label": "Reactome",
"aggregation": "Affected pathway",
"aggregationId": "affected_pathway",
"weight": 1.0,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#reactome",
"category": "OTpath",
},
{
"id": "sysbio",
"sectionId": "sysBio",
"label": "Gene signatures",
"aggregation": "Affected pathway",
"aggregationId": "affected_pathway",
"weight": 0.5,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#gene-signatures",
"category": "OTpath",
},
{
"id": "europepmc",
"sectionId": "europePmc",
"label": "Europe PMC",
"aggregation": "Literature",
"aggregationId": "literature",
"weight": 0.2,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#europe-pmc",
"category": "OTlit",
},
{
"id": "expression_atlas",
"sectionId": "expression",
"label": "Expression Atlas",
"aggregation": "RNA expression",
"aggregationId": "rna_expression",
"weight": 0.2,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#expression-atlas",
"category": "OTrna",
},
{
"id": "impc",
"sectionId": "impc",
"label": "IMPC",
"aggregation": "Animal model",
"aggregationId": "animal_model",
"weight": 0.2,
"isPrivate": False,
"docsLink": "https://platform-docs.opentargets.org/evidence#impc",
"category": "OTgen",
},
# {
# "id": "ot_crispr",
# "sectionId": "oOTgen",
# "label": "OT CRISPR",
# "aggregation": "Partner-only",
# "aggregationId": "partner_only",
# "weight": 0.5,
# "isPrivate": True,
# "docsLink": "https://partner-platform.opentargets.org/projects",
# },
# {
# "id": "encore",
# "sectionId": "encore",
# "label": "ENCORE",
# "aggregation": "Partner-only",
# "aggregationId": "partner_only",
# "weight": 0.5,
# "isPrivate": True,
# "docsLink": "https://partner-platform.opentargets.org/projects",
# },
# {
# "id": "ot_crispr_validation",
# "sectionId": "validationlab",
# "label": "OT Validation",
# "aggregation": "Partner-only",
# "aggregationId": "partner_only",
# "weight": 0.5,
# "isPrivate": True,
# "docsLink": "https://partner-platform.opentargets.org/projects",
# },
]
prioritisationColumns = [
{
"id": "maxClinicalTrialPhase",
"label": "Target in clinic",
"aggregation": "aggregations.precedence",
"sectionId": "knownDrugs",
"description": "Target is in clinical trials for any indication",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#target-in-clinic",
},
{
"id": "isInMembrane",
"label": "Membrane protein",
"aggregation": "aggregations.tractability",
"sectionId": "subcellularLocation",
"description": "Target is annotated to be located in the cell membrane",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#membrane-protein",
},
{
"id": "isSecreted",
"label": "Secreted protein",
"aggregation": "aggregations.tractability",
"sectionId": "subcellularLocation",
"description": "Target is annotated to be secreted",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#secreted-protein",
},
{
"id": "hasLigand",
"label": "Ligand binder",
"aggregation": "aggregations.tractability",
"sectionId": "tractability",
"description": "Target binds a specific ligand",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#ligand-binder",
},
{
"id": "hasSmallMoleculeBinder",
"label": "Small molecule binder",
"aggregation": "aggregations.tractability",
"sectionId": "tractability",
"description": "Target binds a small molecule",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#small-molecule-binder",
},
{
"id": "hasPocket",
"label": "Predicted pockets",
"aggregation": "aggregations.tractability",
"sectionId": "tractability",
"description": "Target has predicted pockets",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#predicted-pockets",
},
{
"id": "mouseOrthologMaxIdentityPercentage",
"label": "Mouse ortholog identity",
"aggregation": "aggregations.doability",
"sectionId": "compGenomics",
"description": "Mouse ortholog maximum identity percentage",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#mouse-ortholog-identity",
},
{
"id": "hasHighQualityChemicalProbes",
"label": "Chemical probes",
"aggregation": "aggregations.doability",
"sectionId": "chemicalProbes",
"description": "Availability of high quality chemical probes for the target",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#chemical-probes",
},
{
"id": "mouseKOScore",
"label": "Mouse models",
"aggregation": "aggregations.safety",
"sectionId": "mousePhenotypes",
"description": "Availability of mouse knockout models for the target",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#mouse-models",
},
{
"id": "geneticConstraint",
"label": "Genetic constraint",
"aggregation": "aggregations.safety",
"sectionId": "geneticConstraint",
"description": "Relative genetic constraint in natural populations derived from GnomAD",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#genetic-constraint",
},
{
"id": "geneEssentiality",
"label": "Gene essentiality",
"aggregation": "aggregations.safety",
"sectionId": "depMapEssentiality",
"description": "Gene is defined as core essential by the DepMap portal",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#gene-essentiality",
},
{
"id": "hasSafetyEvent",
"label": "Known adverse events",
"aggregation": "aggregations.safety",
"sectionId": "safety",
"description": "Target associated with a curated adverse event",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#known-adverse-events",
},
{
"id": "isCancerDriverGene",
"label": "Cancer driver gene",
"aggregation": "aggregations.safety",
"sectionId": "cancerHallmarks",
"description": "Target is classified as an Oncogene and/or Tumor Suppressor Gene",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#cancer-driver-gene",
},
{
"id": "paralogMaxIdentityPercentage",
"label": "Paralogues",
"aggregation": "aggregations.safety",
"sectionId": "compGenomics",
"description": "Paralog maximum identity percentage",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#paralogues",
},
{
"id": "tissueSpecificity",
"label": "Tissue specificity",
"aggregation": "aggregations.safety",
"sectionId": "expressions",
"description": "HPA category types of elevated expression across tissues for the target",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#tissue-specificity",
},
{
"id": "tissueDistribution",
"label": "Tissue distribution",
"aggregation": "aggregations.safety",
"sectionId": "expressions",
"description": "HPA category types of detectable expression across tissues for the target",
"docsLink": "https://platform-docs.opentargets.org/target-prioritisation#tissue-distribution",
},
]
examples = [
("ENSG00000237380", "EFO_0003060"),
# ("ENSG00000149506", "EFO_0008560"), # bg-
# ("ENSG00000211891", "MONDO_0005271"), # bg-
# ("ENSG00000104687", "EFO_0000519"), # bg-
# ("ENSG00000139687", "EFO_0000702"), # bg-
# ("ENSG00000141736", "EFO_0003060"), # bg-
("ENSG00000214842", "EFO_0000508"),
("ENSG00000205038", "EFO_0008560"),
("ENSG00000204544", "EFO_0000519"),
("ENSG00000185842", "EFO_0010642"),
("ENSG00000184956", "EFO_0000702"),
("ENSG00000183578", "EFO_0000181"),
("ENSG00000180483", "EFO_0000508"),
("ENSG00000177238", "MONDO_0007254"),
("ENSG00000165972", "EFO_0004248"),
("ENSG00000164675", "EFO_0004248"),
("ENSG00000162779", "EFO_0000279"),
("ENSG00000154007", "EFO_0000279"),
("ENSG00000146453", "EFO_0000279"),
("ENSG00000145569", "MONDO_0005271"),
("ENSG00000121211", "MONDO_0007254"),
("ENSG00000089723", "EFO_0000707"),
("ENSG00000064225", "EFO_0003060"),
]
spark = SparkSession.builder.getOrCreate()
def getDatasourceToName(output="list"):
"""
Returns list of data sources names/labels.
"""
id2label_dict = {
datasource["id"]: datasource["label"] for datasource in dataSources
}
if output == "dict":
return id2label_dict
elif output == "list":
id2label_list = [[idx, id2label_dict[idx]] for idx in id2label_dict]
return id2label_list
def getPrioritisationToAggregation(output="list"):
id2label_dict = {col["id"]: col["aggregation"] for col in prioritisationColumns}
if output == "dict":
return id2label_dict
elif output == "list":
id2label_list = [[idx, id2label_dict[idx]] for idx in id2label_dict]
return id2label_list
def getDatatypeForDatasource(output="list"):
"""
Returns list of data sources names/labels.
"""
source2type_dict = {
datasource["id"]: datasource["aggregationId"] for datasource in dataSources
}
if output == "dict":
return source2type_dict
elif output == "list":
source2type_list = [
[source, source2type_dict[source]] for source in source2type_dict
]
return source2type_list
# Paths
ot_platform_version = "23.06"
data_path = "gs://open-targets-data-releases/{}/output/etl/parquet/".format(
ot_platform_version
)
diseases_file = data_path + "diseases"
targets_file = data_path + "targets"
drugs_file = data_path + "searchDrug"
targetsPrioritisation_file = (
"gs://open-targets-data-releases/24.06/output/etl/parquet/targetPrioritisation"
)
results_path = "gs://ot-team/cfalaguera/novelty/{}/".format(ot_platform_version)
associationByDatasourceIndirectOverYears_file = (
results_path + "associationByDatasourceIndirectOverYears"
)
associationByDatasourceDirectOverYears_file = (
results_path + "associationByDatasourceDirectOverYears"
)
associationByOverallIndirectOverYears_file = (
results_path + "associationByOverallIndirectOverYears"
)
associationByOverallDirectOverYears_file = (
results_path + "associationByOverallDirectOverYears"
)
evidenceIndirect_file = results_path + "evidenceIndirectDated"
evidenceDirect_file = results_path + "evidenceDirectDated"
spark = SparkSession.builder.getOrCreate()
def getTherapeuticAreaForDisease():
"""
Map diseases to their prioritized therapeutic area.
Returns:
pyspark dataframe with the following columns:
- diseaseId
- therapeuticArea
- therapeuticAreaName
"""
# get TAs ranked by priority
therapeuticAreas = spark.createDataFrame(
data=[
[therapeuticArea, ranking]
for ranking, therapeuticArea in enumerate(
# otPlatform.prioritizedTherapeuticArea
prioritizedTherapeuticArea
)
],
schema=["therapeuticArea", "ranking"],
)
# select top TA for each disease
partition = Window.partitionBy("diseaseId").orderBy(F.col("ranking"))
diseases = spark.read.parquet(diseases_file).persist()
diseases = (
# get diseases mapped to multiple TAs
diseases.select(
F.col("id").alias("diseaseId"),
F.explode_outer("therapeuticAreas").alias("therapeuticArea"),
)
# add TA ranking
.join(therapeuticAreas, "therapeuticArea", "left")
# fill with 1000 those TA not included in the ranking (to avoid losing them)
.fillna(1000, subset=["ranking"])
# select top ranking TA for each disease
.withColumn("row", F.row_number().over(partition))
.filter(F.col("row") == 1)
.drop("row", "ranking")
# add TA name
.join(
diseases.select(
F.col("id").alias("therapeuticArea"),
F.col("name").alias("therapeuticAreaName"),
),
"therapeuticArea",
"left",
)
)
return diseases
# for cutoff in range(1, 10):
for cutoff in [4]:
cutoff = cutoff / 10
# for year in range(2000, 2023 + 1):
for year in range(2023, 2023 + 1):
pairs = (
spark.read.parquet(associationByDatasourceDirectOverYears_file)
.withColumn(
"lastNovelty",
F.lag("novelty").over(
Window.partitionBy("targetId", "diseaseId", "datasourceId").orderBy(
"year"
)
),
)
.filter(
(F.col("novelty") > 0)
& (F.col("novelty") >= cutoff)
& (F.col("year") == year)
& (F.col("novelty") > F.col("lastNovelty"))
)
.join(
getTherapeuticAreaForDisease().select(
"diseaseId", "therapeuticAreaName", "therapeuticArea"
),
"diseaseId",
"left",
)
# filter only indications
.filter(
~F.col("therapeuticAreaName").isin(
[
"measurement",
"biological process",
"medical procedure",
"phenotype",
"injury, poisoning or other complication",
"animal disease",
]
)
)
# filter very generic indications
.filter(F.col("therapeuticArea") != F.col("diseaseId"))
.filter(
~F.col("diseaseId").isin(
["EFO_0000508", "EFO_0010642", "MONDO_0004992"]
)
)
# filter noisy areas
.filter(
F.col("therapeuticArea") != "OTAR_0000017"
) # has many spermatic dysfunction related targets coming from IMPC
# add novelty data type
.join(
spark.createDataFrame(
getDatatypeForDatasource(),
schema=["datasourceId", "datatypeId"],
),
"datasourceId",
"left",
)
.select(
"targetId",
"diseaseId",
"novelty",
"datatypeId",
"datasourceId",
"therapeuticAreaName",
)
# add disease info
.join(
spark.read.parquet(diseases_file).select(
F.col("id").alias("diseaseId"), F.col("name").alias("diseaseName")
),
"diseaseId",
"left",
)
# add target info
.join(
spark.read.parquet(targets_file).select(
F.col("id").alias("targetId"),
F.col("biotype").alias("targetBiotype"),
F.col("approvedSymbol").alias("targetSymbol"),
),
"targetId",
"left",
)
.distinct()
)
pairs.toPandas().to_csv(
"gs://ot-team/cfalaguera/novels/associationsWithNoveltyPeak2023.csv",
index=False,
header=True,
)
(
pairs.groupby("therapeuticAreaName")
.agg(
F.size(F.collect_set(F.concat_ws("-", "diseaseId", "targetId"))).alias(
"associations"
),
)
.withColumn("year", F.lit(year))
.write.parquet(
"gs://ot-team/cfalaguera/novels/associations/novelty={}/year={}".format(
cutoff, year
)
)
)
pairs.groupby("therapeuticAreaName", "targetSymbol").agg(
F.size(F.collect_set("diseaseId")).alias("nDisease"),
F.mean("novelty").alias("meanNovelty"),
F.max("novelty").alias("maxNovelty"),
F.concat_ws(",", F.collect_set("targetId")).alias("targetIds"),
F.concat_ws(",", F.collect_set("datasourceId")).alias("datasources"),
F.concat_ws(";", F.collect_set("diseaseName")).alias("diseaseNames"),
F.concat_ws(";", F.collect_set("diseaseId")).alias("diseaseIds"),
).write.parquet(
"gs://ot-team/cfalaguera/novels/novels/novelty={}/year={}".format(
cutoff, year
)
)
(
pairs.groupby("targetBiotype")
.agg(F.size(F.collect_set("targetId")).alias("targets"))
.write.parquet(
"gs://ot-team/cfalaguera/novels/targetsByBiotype/novelty={}/year={}".format(
cutoff, year
)
)
)
(
pairs.groupby("therapeuticAreaName", "datasourceId")
.agg(
F.size(F.collect_set("targetId")).alias("targets"),
F.size(F.collect_set(F.concat_ws("-", "diseaseId", "targetId"))).alias(
"associations"
),
)
.write.parquet(
"gs://ot-team/cfalaguera/novels/counts/novelty={}/year={}".format(
cutoff, year
)
)
)
(
pairs.withColumn(
"therapeuticAreaName",
F.when(
F.col("therapeuticAreaName").isin(
[
"cancer or benign tumor",
]
),
F.col("therapeuticAreaName"),
).otherwise(F.lit("other")),
)
.groupby("therapeuticAreaName")
.agg(
F.size(F.collect_set(F.concat_ws("-", "diseaseId", "targetId"))).alias(
"associations"
),
)
.withColumn("year", F.lit(year))
.write.parquet(
"gs://ot-team/cfalaguera/novels/associationsByArea/novelty={}/year={}".format(
cutoff, year
)
)
)
(
pairs.withColumn("year", F.lit("year"))
.groupby("year")
.agg(
F.size(F.collect_set("targetId")).alias("targets"),
)
.write.parquet(
"gs://ot-team/cfalaguera/novels/targets/novelty={}/year={}".format(
cutoff, year
)
)
)