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test(method): improved performance in coloc tests #536

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merged 9 commits into from
Mar 21, 2024
Merged

test(method): improved performance in coloc tests #536

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7ab8da3
fix: pytest for coloc unit tests
xyg123 Mar 12, 2024
e5c0e56
Merge branch 'dev' of https://github.com/opentargets/gentropy into py…
xyg123 Mar 12, 2024
8aa507d
fix: removed unused coloc tests
xyg123 Mar 12, 2024
5e58c4f
test(coloc): add coloc semantic test (#538)
ireneisdoomed Mar 13, 2024
694afee
Merge branch 'dev' of https://github.com/opentargets/gentropy into py…
xyg123 Mar 13, 2024
4b3b789
Merge branch 'dev' into pytest_coloc
d0choa Mar 19, 2024
3b47f52
Merge branch 'dev' of https://github.com/opentargets/gentropy into py…
xyg123 Mar 20, 2024
a6ad0c4
fix: remove unused threshold variable
xyg123 Mar 20, 2024
f659fa5
Merge branch 'dev' of https://github.com/opentargets/gentropy into py…
xyg123 Mar 21, 2024
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44 changes: 44 additions & 0 deletions tests/gentropy/conftest.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,6 +3,7 @@
from __future__ import annotations

from pathlib import Path
from typing import Any

import dbldatagen as dg
import hail as hl
Expand Down Expand Up @@ -647,3 +648,46 @@ def sample_data_for_susie_inf() -> list[np.ndarray]:
lbf_moments = np.loadtxt("tests/gentropy/data_samples/01_test_lbf_moments.csv")
lbf_mle = np.loadtxt("tests/gentropy/data_samples/01_test_lbf_mle.csv")
return [ld, z, lbf_moments, lbf_mle]


@pytest.fixture()
def sample_data_for_coloc(spark: SparkSession) -> list[Any]:
"""Sample data for Coloc tests."""
overlap_df = spark.read.parquet(
"tests/gentropy/data_samples/coloc_test_data.snappy.parquet"
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How was this file generated? For semantic tests, it's easier to understand if you create a data subset in the testing module directly.
Instead of reading a file of 500 rows, create a dataframe with 2 overlapping variants, for example.

The same testing function can be parametrised for both scenarios: associations that overlap on multiple SNPs, and on a single SNP.

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It was directly extracted from the test dataset from the R package

)
expected_df = spark.createDataFrame(
[
{
"h0": 1.3769995397857477e-18,
"h1": 2.937336451601565e-10,
"h2": 8.593226431647826e-12,
"h3": 8.338916748775843e-4,
"h4": 0.9991661080227981,
}
]
)
single_snp_coloc = spark.createDataFrame(
[
{
"leftStudyLocusId": 1,
"rightStudyLocusId": 2,
"chromosome": "1",
"tagVariantId": "snp",
"left_logBF": 10.3,
"right_logBF": 10.5,
}
]
)
expected_single_snp_coloc = spark.createDataFrame(
[
{
"h0": 9.254841951638903e-5,
"h1": 2.7517068829182966e-4,
"h2": 3.3609423764447284e-4,
"h3": 9.254841952564387e-13,
"h4": 0.9992961866536217,
}
]
)
return [overlap_df, expected_df, single_snp_coloc, expected_single_snp_coloc]
147 changes: 13 additions & 134 deletions tests/gentropy/method/test_colocalisation_method.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,10 +2,11 @@

from __future__ import annotations

from typing import Any

from gentropy.dataset.colocalisation import Colocalisation
from gentropy.dataset.study_locus_overlap import StudyLocusOverlap
from gentropy.method.colocalisation import Coloc, ECaviar
from pyspark.sql import SparkSession
from pyspark.sql import functions as f


Expand All @@ -15,161 +16,39 @@ def test_coloc(mock_study_locus_overlap: StudyLocusOverlap) -> None:


def test_coloc_colocalise(
spark: SparkSession,
threshold: float = 1e-5,
sample_data_for_coloc: list[Any],
threshold: float = 1e-4,
) -> None:
"""Compare COLOC results with R implementation, using provided sample dataset from R package (StudyLocusOverlap)."""
test_overlap_df = spark.read.parquet(
"tests/gentropy/data_samples/coloc_test_data.snappy.parquet", header=True
test_overlap_df = sample_data_for_coloc[0]
test_overlap = StudyLocusOverlap(
_df=test_overlap_df, _schema=StudyLocusOverlap.get_schema()
)
test_overlap = StudyLocusOverlap(test_overlap_df, StudyLocusOverlap.get_schema())
test_result = Coloc.colocalise(test_overlap)

expected = spark.createDataFrame(
[
{
"h0": 1.3769995397857477e-18,
"h1": 2.937336451601565e-10,
"h2": 8.593226431647826e-12,
"h3": 8.338916748775843e-4,
"h4": 0.9991661080227981,
}
]
)
expected = sample_data_for_coloc[1]
difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
for col in difference.columns:
assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0


def test_single_snp_coloc(
spark: SparkSession,
sample_data_for_coloc: list[Any],
threshold: float = 1e-5,
) -> None:
"""Test edge case of coloc where only one causal SNP is present in the StudyLocusOverlap."""
test_overlap_df = spark.createDataFrame(
[
{
"leftStudyLocusId": 1,
"rightStudyLocusId": 2,
"chromosome": "1",
"tagVariantId": "snp",
"left_logBF": 10.3,
"right_logBF": 10.5,
}
]
)
test_overlap = StudyLocusOverlap(
test_overlap_df.select(
"leftStudyLocusId",
"rightStudyLocusId",
"chromosome",
"tagVariantId",
f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
),
StudyLocusOverlap.get_schema(),
)
test_result = Coloc.colocalise(test_overlap)

expected = spark.createDataFrame(
[
{
"h0": 9.254841951638903e-5,
"h1": 2.7517068829182966e-4,
"h2": 3.3609423764447284e-4,
"h3": 9.254841952564387e-13,
"h4": 0.9992961866536217,
}
]
)
difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
for col in difference.columns:
assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0


def test_single_snp_coloc_one_negative(
spark: SparkSession,
threshold: float = 1e-5,
) -> None:
"""Test edge case of coloc where only one causal SNP is present (On one side!) in the StudyLocusOverlap."""
test_overlap_df = spark.createDataFrame(
[
{
"leftStudyLocusId": 1,
"rightStudyLocusId": 2,
"chromosome": "1",
"tagVariantId": "snp",
"left_logBF": 18.3,
"right_logBF": 0.01,
}
]
)
test_overlap = StudyLocusOverlap(
test_overlap_df.select(
"leftStudyLocusId",
"rightStudyLocusId",
"chromosome",
"tagVariantId",
f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
),
StudyLocusOverlap.get_schema(),
)
test_result = Coloc.colocalise(test_overlap)
test_result.df.show(1, False)
expected = spark.createDataFrame(
[
{
"h0": 1.0246538505087709e-4,
"h1": 0.9081680002273896,
"h2": 1.0349517929098209e-8,
"h3": 1.0246538506112363e-12,
"h4": 0.09172952403701702,
}
]
)
difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
for col in difference.columns:
assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0


def test_single_snp_coloc_both_negative(
spark: SparkSession,
threshold: float = 1e-5,
) -> None:
"""Test edge case of coloc where only one non-causal SNP overlaps in the StudyLocusOverlap."""
test_overlap_df = spark.createDataFrame(
[
{
"leftStudyLocusId": 1,
"rightStudyLocusId": 2,
"chromosome": "1",
"tagVariantId": "snp",
"left_logBF": 0.03,
"right_logBF": 0.01,
}
]
)
test_overlap_df = sample_data_for_coloc[2]
test_overlap = StudyLocusOverlap(
test_overlap_df.select(
_df=test_overlap_df.select(
"leftStudyLocusId",
"rightStudyLocusId",
"chromosome",
"tagVariantId",
f.struct(f.col("left_logBF"), f.col("right_logBF")).alias("statistics"),
),
StudyLocusOverlap.get_schema(),
_schema=StudyLocusOverlap.get_schema(),
)
test_result = Coloc.colocalise(test_overlap)
expected = spark.createDataFrame(
[
{
"h0": 0.9997855774090624,
"h1": 1.0302335812225042e-4,
"h2": 1.0098335895103664e-4,
"h3": 9.9978557750904e-9,
"h4": 1.0405876008495098e-5,
}
]
)
expected = sample_data_for_coloc[3]
difference = test_result.df.select("h0", "h1", "h2", "h3", "h4").subtract(expected)
for col in difference.columns:
assert difference.filter(f.abs(f.col(col)) > threshold).count() == 0
Expand Down