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fix(isProteinCoding): get all overlapping genes from the variant index #948

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fix(isProteinCoding): get all overlapping genes from the variant index #948

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be3ba48
feat: rewrite `is_protein_coding_feature_logic` to take info from the…
ireneisdoomed Dec 13, 2024
b270c9e
chore: correct genomic_window definition
ireneisdoomed Dec 13, 2024
f507fe1
test: adapt `TestCommonProteinCodingFeatureLogic`
ireneisdoomed Dec 13, 2024
424ccd4
test: adapt `test_build_feature_matrix`
ireneisdoomed Dec 16, 2024
b750d8b
test: adapt `test_build_feature_matrix`
ireneisdoomed Dec 16, 2024
2133e82
Merge branch 'dev' into il-3674
project-defiant Dec 16, 2024
1820ff9
test: adapt `test_build_feature_matrix`
ireneisdoomed Dec 16, 2024
a22c74b
Merge branch 'dev' of https://github.com/opentargets/gentropy into il…
ireneisdoomed Dec 16, 2024
dbd8be6
Merge branch 'il-3674' of https://github.com/opentargets/gentropy int…
ireneisdoomed Dec 16, 2024
34f5f3f
Merge branch 'dev' into il-3674
ireneisdoomed Dec 19, 2024
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56 changes: 35 additions & 21 deletions src/gentropy/dataset/l2g_features/other.py
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Original file line number Diff line number Diff line change
Expand Up @@ -85,42 +85,56 @@ def common_genecount_feature_logic(
def is_protein_coding_feature_logic(
study_loci_to_annotate: StudyLocus | L2GGoldStandard,
*,
gene_index: GeneIndex,
variant_index: VariantIndex,
feature_name: str,
genomic_window: int,
genomic_window: int = 500_000,
) -> DataFrame:
"""Computes the feature to indicate if a gene is protein-coding or not.

Args:
study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci
that will be used for annotation
gene_index (GeneIndex): Dataset containing information related to all genes in release.
variant_index (VariantIndex): Dataset containing information related to all overlapping genes within a genomic window.
feature_name (str): The name of the feature
genomic_window (int): The maximum window size to consider
genomic_window (int): The window size around the locus to consider. Defaults to its maximum value: 500kb up and downstream the locus

Returns:
DataFrame: Feature dataset, with 1 if the gene is protein-coding, 0 if not.
"""
study_loci_window = (
study_loci_to_annotate.df.withColumn(
"window_start", f.col("position") - (genomic_window / 2)
assert genomic_window <= 500_000, "Genomic window must be less than 500kb."
genes_in_window = (
variant_index.df.withColumn(
"transcriptConsequence", f.explode("transcriptConsequences")
)
.withColumn("window_end", f.col("position") + (genomic_window / 2))
.withColumnRenamed("chromosome", "SL_chromosome")
.select(
"variantId",
f.col("transcriptConsequence.targetId").alias("geneId"),
f.col("transcriptConsequence.biotype").alias("biotype"),
f.col("transcriptConsequence.distanceFromFootprint").alias(
"distanceFromFootprint"
),
)
.filter(f.col("distanceFromFootprint") <= genomic_window)
)
if isinstance(study_loci_to_annotate, StudyLocus):
variants_df = study_loci_to_annotate.df.select(
f.explode_outer("locus.variantId").alias("variantId"),
"studyLocusId",
).filter(f.col("variantId").isNotNull())
elif isinstance(study_loci_to_annotate, L2GGoldStandard):
variants_df = study_loci_to_annotate.df.select("studyLocusId", "variantId")
return (
study_loci_window.join(
gene_index.df.alias("genes"),
on=(
(f.col("SL_chromosome") == f.col("genes.chromosome"))
& (f.col("genes.tss") >= f.col("window_start"))
& (f.col("genes.tss") <= f.col("window_end"))
),
how="inner",
# Annotate all genes in the window of a locus
variants_df.join(
genes_in_window,
on="variantId",
)
# Apply flag across all variants in the locus
.withColumn(
feature_name,
f.when(f.col("biotype") == "protein_coding", f.lit(1)).otherwise(f.lit(0)),
f.when(f.col("biotype") == "protein_coding", f.lit(1.0)).otherwise(
f.lit(0.0)
),
)
.select("studyLocusId", "geneId", feature_name)
.distinct()
Expand Down Expand Up @@ -211,7 +225,7 @@ def compute(
class ProteinCodingFeature(L2GFeature):
"""Indicates whether a gene is protein-coding within a specified window size from the study locus."""

feature_dependency_type = GeneIndex
feature_dependency_type = VariantIndex
feature_name = "isProteinCoding"

@classmethod
Expand All @@ -224,12 +238,12 @@ def compute(

Args:
study_loci_to_annotate (StudyLocus | L2GGoldStandard): The dataset containing study loci that will be used for annotation
feature_dependency (dict[str, Any]): Dictionary containing dependencies, including gene index
feature_dependency (dict[str, Any]): Dictionary containing dependencies, including variant index

Returns:
ProteinCodingFeature: Feature dataset with 1 if the gene is protein-coding, 0 otherwise
"""
genomic_window = 1000000
genomic_window = 500_000
protein_coding_df = is_protein_coding_feature_logic(
study_loci_to_annotate=study_loci_to_annotate,
feature_name=cls.feature_name,
Expand Down
84 changes: 52 additions & 32 deletions tests/gentropy/dataset/test_l2g_feature.py
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Original file line number Diff line number Diff line change
Expand Up @@ -238,9 +238,11 @@ def sample_variant_index_schema() -> StructType:
ArrayType(
StructType(
[
StructField("distanceFromFootprint", LongType(), True),
StructField("distanceFromTss", LongType(), True),
StructField("targetId", StringType(), True),
StructField("isEnsemblCanonical", BooleanType(), True),
StructField("biotype", StringType(), True),
]
)
),
Expand Down Expand Up @@ -624,13 +626,17 @@ def _setup(
[
{
"distanceFromTss": 10,
"distanceFromFootprint": 0,
"targetId": "gene1",
"isEnsemblCanonical": True,
"biotype": "protein_coding",
},
{
"distanceFromTss": 2,
"distanceFromFootprint": 0,
"targetId": "gene2",
"isEnsemblCanonical": True,
"biotype": "protein_coding",
},
],
),
Expand All @@ -643,8 +649,10 @@ def _setup(
[
{
"distanceFromTss": 5,
"distanceFromFootprint": 0,
"targetId": "gene1",
"isEnsemblCanonical": True,
"biotype": "protein_coding",
},
],
),
Expand Down Expand Up @@ -928,9 +936,8 @@ class TestCommonProteinCodingFeatureLogic:
[
(
[
{"studyLocusId": "1", "geneId": "gene1", "isProteinCoding500kb": 1},
{"studyLocusId": "1", "geneId": "gene2", "isProteinCoding500kb": 1},
{"studyLocusId": "1", "geneId": "gene3", "isProteinCoding500kb": 0},
{"studyLocusId": "1", "geneId": "gene1", "isProteinCoding": 1.0},
{"studyLocusId": "1", "geneId": "gene2", "isProteinCoding": 0.0},
]
),
],
Expand All @@ -944,25 +951,28 @@ def test_is_protein_coding_feature_logic(
observed_df = (
is_protein_coding_feature_logic(
study_loci_to_annotate=self.sample_study_locus,
gene_index=self.sample_gene_index,
feature_name="isProteinCoding500kb",
genomic_window=500000,
variant_index=self.sample_variant_index,
feature_name="isProteinCoding",
)
.select("studyLocusId", "geneId", "isProteinCoding500kb")
.select("studyLocusId", "geneId", "isProteinCoding")
.orderBy("studyLocusId", "geneId")
)

expected_df = (
spark.createDataFrame(expected_data)
.select("studyLocusId", "geneId", "isProteinCoding500kb")
.select("studyLocusId", "geneId", "isProteinCoding")
.orderBy("studyLocusId", "geneId")
)
assert (
observed_df.collect() == expected_df.collect()
), "Expected and observed DataFrames do not match."

@pytest.fixture(autouse=True)
def _setup(self: TestCommonProteinCodingFeatureLogic, spark: SparkSession) -> None:
def _setup(
self: TestCommonProteinCodingFeatureLogic,
spark: SparkSession,
sample_variant_index_schema: StructType,
) -> None:
"""Set up sample data for the test."""
# Sample study locus data
self.sample_study_locus = StudyLocus(
Expand All @@ -974,39 +984,47 @@ def _setup(self: TestCommonProteinCodingFeatureLogic, spark: SparkSession) -> No
"studyId": "study1",
"chromosome": "1",
"position": 1000000,
"locus": [
{
"variantId": "var1",
},
],
},
],
StudyLocus.get_schema(),
),
_schema=StudyLocus.get_schema(),
)

# Sample gene index data with biotype
self.sample_gene_index = GeneIndex(
self.sample_variant_index = VariantIndex(
_df=spark.createDataFrame(
[
{
"geneId": "gene1",
"chromosome": "1",
"tss": 950000,
"biotype": "protein_coding",
},
{
"geneId": "gene2",
"chromosome": "1",
"tss": 1050000,
"biotype": "protein_coding",
},
{
"geneId": "gene3",
"chromosome": "1",
"tss": 1010000,
"biotype": "non_coding",
},
(
"var1",
"chrom",
1,
"A",
"T",
[
{
"distanceFromFootprint": 0,
"distanceFromTss": 10,
"targetId": "gene1",
"biotype": "protein_coding",
"isEnsemblCanonical": True,
},
{
"distanceFromFootprint": 0,
"distanceFromTss": 20,
"targetId": "gene2",
"biotype": "non_coding",
"isEnsemblCanonical": True,
},
],
),
],
GeneIndex.get_schema(),
sample_variant_index_schema,
),
_schema=GeneIndex.get_schema(),
_schema=VariantIndex.get_schema(),
)


Expand Down Expand Up @@ -1067,8 +1085,10 @@ def _setup(
[
{
"distanceFromTss": 10,
"distanceFromFootprint": 0,
"targetId": "gene1",
"isEnsemblCanonical": True,
"biotype": "protein_coding",
},
],
)
Expand Down
7 changes: 3 additions & 4 deletions tests/gentropy/datasource/open_targets/test_l2g_gold_standard.py
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Original file line number Diff line number Diff line change
Expand Up @@ -29,7 +29,6 @@
from pyspark.sql.session import SparkSession

from gentropy.dataset.colocalisation import Colocalisation
from gentropy.dataset.gene_index import GeneIndex
from gentropy.dataset.study_locus import StudyLocus


Expand Down Expand Up @@ -162,15 +161,15 @@ def test_build_feature_matrix(
mock_study_locus: StudyLocus,
mock_colocalisation: Colocalisation,
mock_study_index: StudyIndex,
mock_gene_index: GeneIndex,
mock_variant_index: VariantIndex,
) -> None:
"""Test building feature matrix with the eQtlColocH4Maximum feature."""
"""Test building feature matrix with the eQtlColocH4Maximum and isProteinCoding features."""
features_list = ["eQtlColocH4Maximum", "isProteinCoding"]
loader = L2GFeatureInputLoader(
colocalisation=mock_colocalisation,
study_index=mock_study_index,
study_locus=mock_study_locus,
gene_index=mock_gene_index,
variant_index=mock_variant_index,
)
fm = mock_study_locus.build_feature_matrix(features_list, loader)
assert isinstance(
Expand Down
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