refactor: remove gene_index step

docs/python_api/datasets/target_index.md

@@ -0,0 +1,9 @@
+---
+title: Target Index
+---
+
+::: gentropy.dataset.target_index.TargetIndex
+
+## Schema
+
+--8<-- "assets/schemas/target_index.md"

docs/python_api/steps/target_index.md

@@ -0,0 +1,5 @@
+---
+title: target_index
+---
+
+::: gentropy.target_index.TargetIndexStep

src/gentropy/biosample_index.py

@@ -27,7 +27,7 @@ def __init__(
             cell_ontology_input_path (str): Input cell ontology dataset path.
             uberon_input_path (str): Input uberon dataset path.
             efo_input_path (str): Input efo dataset path.
-            biosample_index_path (str): Output gene index dataset path.
+            biosample_index_path (str): Output biosample index dataset path.
         """
         cell_ontology_index = extract_ontology_from_json(
             cell_ontology_input_path, session.spark

src/gentropy/config.py

@@ -46,12 +46,12 @@ class ColocalisationConfig(StepConfig):
 
 
 @dataclass
-class GeneIndexConfig(StepConfig):
-    """Gene index step configuration."""
+class TargetIndexConfig(StepConfig):
+    """Target index step configuration."""
 
     target_path: str = MISSING
-    gene_index_path: str = MISSING
-    _target_: str = "gentropy.gene_index.GeneIndexStep"
+    target_index_path: str = MISSING
+    _target_: str = "gentropy.target_index.TargetIndexStep"
 
 
 @dataclass
@@ -305,7 +305,7 @@ class LocusToGeneFeatureMatrixConfig(StepConfig):
     variant_index_path: str | None = None
     colocalisation_path: str | None = None
     study_index_path: str | None = None
-    gene_index_path: str | None = None
+    target_index_path: str | None = None
     feature_matrix_path: str = MISSING
     features_list: list[str] = field(
         default_factory=lambda: [
@@ -694,7 +694,7 @@ def register_config() -> None:
     cs.store(group="step/session", name="base_session", node=SessionConfig)
     cs.store(group="step", name="colocalisation", node=ColocalisationConfig)
     cs.store(group="step", name="eqtl_catalogue", node=EqtlCatalogueConfig)
-    cs.store(group="step", name="gene_index", node=GeneIndexConfig)
+    cs.store(group="step", name="target_index", node=TargetIndexConfig)
     cs.store(group="step", name="biosample_index", node=BiosampleIndexConfig)
     cs.store(
         group="step",

src/gentropy/dataset/intervals.py

@@ -8,7 +8,7 @@
 from gentropy.common.Liftover import LiftOverSpark
 from gentropy.common.schemas import parse_spark_schema
 from gentropy.dataset.dataset import Dataset
-from gentropy.dataset.gene_index import GeneIndex
+from gentropy.dataset.target_index import TargetIndex
 
 if TYPE_CHECKING:
     from pyspark.sql import SparkSession
@@ -35,7 +35,7 @@ def from_source(
         spark: SparkSession,
         source_name: str,
         source_path: str,
-        gene_index: GeneIndex,
+        target_index: TargetIndex,
         lift: LiftOverSpark,
     ) -> Intervals:
         """Collect interval data for a particular source.
@@ -44,7 +44,7 @@ def from_source(
             spark (SparkSession): Spark session
             source_name (str): Name of the interval source
             source_path (str): Path to the interval source file
-            gene_index (GeneIndex): Gene index
+            target_index (TargetIndex): Target index
             lift (LiftOverSpark): LiftOverSpark instance to convert coordinats from hg37 to hg38
 
         Returns:
@@ -70,4 +70,4 @@ def from_source(
 
         source_class = source_to_class[source_name]
         data = source_class.read(spark, source_path)  # type: ignore
-        return source_class.parse(data, gene_index, lift)  # type: ignore
+        return source_class.parse(data, target_index, lift)  # type: ignore

src/gentropy/dataset/l2g_features/colocalisation.py

@@ -9,11 +9,11 @@
 
 from gentropy.common.spark_helpers import convert_from_wide_to_long
 from gentropy.dataset.colocalisation import Colocalisation
-from gentropy.dataset.gene_index import GeneIndex
 from gentropy.dataset.l2g_features.l2g_feature import L2GFeature
 from gentropy.dataset.l2g_gold_standard import L2GGoldStandard
 from gentropy.dataset.study_index import StudyIndex
 from gentropy.dataset.study_locus import StudyLocus
+from gentropy.dataset.target_index import TargetIndex
 from gentropy.dataset.variant_index import VariantIndex
 
 if TYPE_CHECKING:
@@ -74,7 +74,7 @@ def extend_missing_colocalisation_to_neighbourhood_genes(
     feature_name: str,
     local_features: DataFrame,
     variant_index: VariantIndex,
-    gene_index: GeneIndex,
+    target_index: TargetIndex,
     study_locus: StudyLocus,
 ) -> DataFrame:
     """This function creates an artificial dataset of features that represents the missing colocalisation to the neighbourhood genes.
@@ -83,7 +83,7 @@ def extend_missing_colocalisation_to_neighbourhood_genes(
         feature_name (str): The name of the feature to extend
         local_features (DataFrame): The dataframe of features to extend
         variant_index (VariantIndex): Variant index containing all variant/gene relationships
-        gene_index (GeneIndex): Gene index to fetch the gene information
+        target_index (TargetIndex): Target index to fetch the gene information
         study_locus (StudyLocus): Study locus to traverse between colocalisation and variant index
 
     Returns:
@@ -94,7 +94,7 @@ def extend_missing_colocalisation_to_neighbourhood_genes(
             "variantId", f.explode("transcriptConsequences").alias("tc")
         )
         .select(f.col("tc.targetId").alias("geneId"), "variantId")
-        .join(gene_index.df.select("geneId", "biotype"), "geneId", "left")
+        .join(target_index.df.select("geneId", "biotype"), "geneId", "left")
         .filter(f.col("biotype") == "protein_coding")
         .drop("biotype")
         .distinct()
@@ -127,7 +127,7 @@ def common_neighbourhood_colocalisation_feature_logic(
     *,
     colocalisation: Colocalisation,
     study_index: StudyIndex,
-    gene_index: GeneIndex,
+    target_index: TargetIndex,
     study_locus: StudyLocus,
     variant_index: VariantIndex,
 ) -> DataFrame:
@@ -141,7 +141,7 @@ def common_neighbourhood_colocalisation_feature_logic(
         qtl_types (list[str] | str): The types of QTL to filter the data by
         colocalisation (Colocalisation): Dataset with the colocalisation results
         study_index (StudyIndex): Study index to fetch study type and gene
-        gene_index (GeneIndex): Gene index to add gene type
+        target_index (TargetIndex): Target index to add gene type
         study_locus (StudyLocus): Study locus to traverse between colocalisation and study index
         variant_index (VariantIndex): Variant index to annotate all overlapping genes
 
@@ -165,15 +165,15 @@ def common_neighbourhood_colocalisation_feature_logic(
             local_feature_name,
             local_max,
             variant_index,
-            gene_index,
+            target_index,
             study_locus,
         )
     )
     return (
         extended_local_max.join(
             # Compute average score in the vicinity (feature will be the same for any gene associated with a studyLocus)
             # (non protein coding genes in the vicinity are excluded see #3552)
-            gene_index.df.filter(f.col("biotype") == "protein_coding").select("geneId"),
+            target_index.df.filter(f.col("biotype") == "protein_coding").select("geneId"),
             "geneId",
             "inner",
         )
@@ -242,7 +242,7 @@ class EQtlColocClppMaximumNeighbourhoodFeature(L2GFeature):
     feature_dependency_type = [
         Colocalisation,
         StudyIndex,
-        GeneIndex,
+        TargetIndex,
         StudyLocus,
         VariantIndex,
     ]
@@ -333,7 +333,7 @@ class PQtlColocClppMaximumNeighbourhoodFeature(L2GFeature):
     feature_dependency_type = [
         Colocalisation,
         StudyIndex,
-        GeneIndex,
+        TargetIndex,
         StudyLocus,
         VariantIndex,
     ]
@@ -423,7 +423,7 @@ class SQtlColocClppMaximumNeighbourhoodFeature(L2GFeature):
     feature_dependency_type = [
         Colocalisation,
         StudyIndex,
-        GeneIndex,
+        TargetIndex,
         StudyLocus,
         VariantIndex,
     ]
@@ -513,7 +513,7 @@ class EQtlColocH4MaximumNeighbourhoodFeature(L2GFeature):
     feature_dependency_type = [
         Colocalisation,
         StudyIndex,
-        GeneIndex,
+        TargetIndex,
         StudyLocus,
         VariantIndex,
     ]
@@ -603,7 +603,7 @@ class PQtlColocH4MaximumNeighbourhoodFeature(L2GFeature):
     feature_dependency_type = [
         Colocalisation,
         StudyIndex,
-        GeneIndex,
+        TargetIndex,
         StudyLocus,
         VariantIndex,
     ]
@@ -693,7 +693,7 @@ class SQtlColocH4MaximumNeighbourhoodFeature(L2GFeature):
     feature_dependency_type = [
         Colocalisation,
         StudyIndex,
-        GeneIndex,
+        TargetIndex,
         StudyLocus,
         VariantIndex,
     ]

src/gentropy/dataset/l2g_features/distance.py

@@ -8,10 +8,10 @@
 from pyspark.sql import Window
 
 from gentropy.common.spark_helpers import convert_from_wide_to_long
-from gentropy.dataset.gene_index import GeneIndex
 from gentropy.dataset.l2g_features.l2g_feature import L2GFeature
 from gentropy.dataset.l2g_gold_standard import L2GGoldStandard
 from gentropy.dataset.study_locus import StudyLocus
+from gentropy.dataset.target_index import TargetIndex
 from gentropy.dataset.variant_index import VariantIndex
 
 if TYPE_CHECKING:
@@ -85,7 +85,7 @@ def common_neighbourhood_distance_feature_logic(
     variant_index: VariantIndex,
     feature_name: str,
     distance_type: str,
-    gene_index: GeneIndex,
+    target_index: TargetIndex,
     genomic_window: int = 500_000,
 ) -> DataFrame:
     """Calculate the distance feature that correlates any variant in a credible set with any protein coding gene nearby the locus. The distance is weighted by the posterior probability of the variant to factor in its contribution to the trait.
@@ -95,7 +95,7 @@ def common_neighbourhood_distance_feature_logic(
         variant_index (VariantIndex): The dataset containing distance to gene information
         feature_name (str): The name of the feature
         distance_type (str): The type of distance to gene
-        gene_index (GeneIndex): The dataset containing gene information
+        target_index (TargetIndex): The dataset containing gene information
         genomic_window (int): The maximum window size to consider
 
     Returns:
@@ -113,7 +113,7 @@ def common_neighbourhood_distance_feature_logic(
     return (
         # Then compute mean distance in the vicinity (feature will be the same for any gene associated with a studyLocus)
         local_metric.join(
-            gene_index.df.filter(f.col("biotype") == "protein_coding").select("geneId"),
+            target_index.df.filter(f.col("biotype") == "protein_coding").select("geneId"),
             "geneId",
             "inner",
         )
@@ -185,7 +185,7 @@ def compute(
 class DistanceTssMeanNeighbourhoodFeature(L2GFeature):
     """Minimum mean distance to TSS for all genes in the vicinity of a studyLocus."""
 
-    feature_dependency_type = [VariantIndex, GeneIndex]
+    feature_dependency_type = [VariantIndex, TargetIndex]
     feature_name = "distanceTssMeanNeighbourhood"
 
     @classmethod
@@ -261,7 +261,7 @@ def compute(
 class DistanceSentinelTssNeighbourhoodFeature(L2GFeature):
     """Distance between the sentinel variant and a gene TSS as a relation of the distnace with all the genes in the vicinity of a studyLocus. This is not weighted by the causal probability."""
 
-    feature_dependency_type = [VariantIndex, GeneIndex]
+    feature_dependency_type = [VariantIndex, TargetIndex]
     feature_name = "distanceSentinelTssNeighbourhood"
 
     @classmethod
@@ -342,7 +342,7 @@ def compute(
 class DistanceFootprintMeanNeighbourhoodFeature(L2GFeature):
     """Minimum mean distance to footprint for all genes in the vicinity of a studyLocus."""
 
-    feature_dependency_type = [VariantIndex, GeneIndex]
+    feature_dependency_type = [VariantIndex, TargetIndex]
     feature_name = "distanceFootprintMeanNeighbourhood"
 
     @classmethod
@@ -418,7 +418,7 @@ def compute(
 class DistanceSentinelFootprintNeighbourhoodFeature(L2GFeature):
     """Distance between the sentinel variant and a gene footprint as a relation of the distnace with all the genes in the vicinity of a studyLocus. This is not weighted by the causal probability."""
 
-    feature_dependency_type = [VariantIndex, GeneIndex]
+    feature_dependency_type = [VariantIndex, TargetIndex]
     feature_name = "distanceSentinelFootprintNeighbourhood"
 
     @classmethod