gbanyan
bcd3c4ffbe
- load.py: DuckDB persistence with provenance tracking, ortholog confidence distribution stats - CLI animal-models command: checkpoint-restart pattern, top scoring genes display - 10 unit tests: ortholog confidence scoring, keyword filtering, multi-organism bonus, NULL preservation - 4 integration tests: full pipeline, checkpoint-restart, provenance tracking, empty phenotype handling - All tests pass (14/14): validates fetch->transform->load->CLI flow - Fixed polars deprecations: str.join replaces str.concat, pl.len replaces pl.count
281 lines
11 KiB
Python
281 lines
11 KiB
Python
"""Unit tests for animal model evidence layer."""
|
|
|
|
import io
|
|
from unittest.mock import Mock, patch, MagicMock
|
|
|
|
import polars as pl
|
|
import pytest
|
|
|
|
from usher_pipeline.evidence.animal_models import (
|
|
fetch_ortholog_mapping,
|
|
filter_sensory_phenotypes,
|
|
score_animal_evidence,
|
|
SENSORY_MP_KEYWORDS,
|
|
)
|
|
|
|
|
|
def test_ortholog_confidence_high():
|
|
"""Test that 8+ supporting sources results in HIGH confidence."""
|
|
# Mock HCOP data with 8 supporting databases
|
|
hcop_data = """human_entrez_gene\thuman_ensembl_gene\thgnc_id\thuman_name\thuman_symbol\thuman_chr\thuman_assert_ids\tmouse_entrez_gene\tmouse_ensembl_gene\tmgi_id\tmouse_name\tmouse_symbol\tmouse_chr\tmouse_assert_ids\tsupport
|
|
123\tENSG00000001\tHGNC:1\tGene 1\tGENE1\t1\t\t456\tENSMUSG001\tMGI:1\tGene1\tGene1\t1\t\tdb1,db2,db3,db4,db5,db6,db7,db8"""
|
|
|
|
with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_download:
|
|
mock_download.return_value = hcop_data.encode('utf-8')
|
|
|
|
result = fetch_ortholog_mapping(['ENSG00000001'])
|
|
|
|
assert len(result) == 1
|
|
assert result['mouse_ortholog_confidence'][0] == 'HIGH'
|
|
|
|
|
|
def test_ortholog_confidence_low():
|
|
"""Test that 1-3 supporting sources results in LOW confidence."""
|
|
# Mock HCOP data with 2 supporting databases
|
|
hcop_mouse = """human_entrez_gene\thuman_ensembl_gene\thgnc_id\thuman_name\thuman_symbol\thuman_chr\thuman_assert_ids\tmouse_entrez_gene\tmouse_ensembl_gene\tmgi_id\tmouse_name\tmouse_symbol\tmouse_chr\tmouse_assert_ids\tsupport
|
|
123\tENSG00000001\tHGNC:1\tGene 1\tGENE1\t1\t\t456\tENSMUSG001\tMGI:1\tGene1\tGene1\t1\t\tdb1,db2"""
|
|
|
|
hcop_zebrafish = """human_entrez_gene\thuman_ensembl_gene\thgnc_id\thuman_name\thuman_symbol\thuman_chr\thuman_assert_ids\tzebrafish_entrez_gene\tzebrafish_ensembl_gene\tzfin_id\tzebrafish_name\tzebrafish_symbol\tzebrafish_chr\tzebrafish_assert_ids\tsupport
|
|
"""
|
|
|
|
with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_download:
|
|
# Return mouse data first, then zebrafish data
|
|
mock_download.side_effect = [
|
|
hcop_mouse.encode('utf-8'),
|
|
hcop_zebrafish.encode('utf-8')
|
|
]
|
|
|
|
result = fetch_ortholog_mapping(['ENSG00000001'])
|
|
|
|
assert len(result) == 1
|
|
assert result['mouse_ortholog_confidence'][0] == 'LOW'
|
|
|
|
|
|
def test_one_to_many_best_selected():
|
|
"""Test that for one-to-many ortholog mappings, the highest confidence is kept."""
|
|
# Mock HCOP data with two orthologs for same human gene
|
|
hcop_mouse = """human_entrez_gene\thuman_ensembl_gene\thgnc_id\thuman_name\thuman_symbol\thuman_chr\thuman_assert_ids\tmouse_entrez_gene\tmouse_ensembl_gene\tmgi_id\tmouse_name\tmouse_symbol\tmouse_chr\tmouse_assert_ids\tsupport
|
|
123\tENSG00000001\tHGNC:1\tGene 1\tGENE1\t1\t\t456\tENSMUSG001\tMGI:1\tGene1a\tGene1a\t1\t\tdb1,db2
|
|
123\tENSG00000001\tHGNC:1\tGene 1\tGENE1\t1\t\t789\tENSMUSG002\tMGI:2\tGene1b\tGene1b\t2\t\tdb1,db2,db3,db4,db5,db6,db7,db8"""
|
|
|
|
hcop_zebrafish = """human_entrez_gene\thuman_ensembl_gene\thgnc_id\thuman_name\thuman_symbol\thuman_chr\thuman_assert_ids\tzebrafish_entrez_gene\tzebrafish_ensembl_gene\tzfin_id\tzebrafish_name\tzebrafish_symbol\tzebrafish_chr\tzebrafish_assert_ids\tsupport
|
|
"""
|
|
|
|
with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_download:
|
|
mock_download.side_effect = [
|
|
hcop_mouse.encode('utf-8'),
|
|
hcop_zebrafish.encode('utf-8')
|
|
]
|
|
|
|
result = fetch_ortholog_mapping(['ENSG00000001'])
|
|
|
|
# Should select Gene1b with 8 sources (HIGH confidence)
|
|
assert len(result) == 1
|
|
assert result['mouse_ortholog'][0] == 'Gene1b'
|
|
assert result['mouse_ortholog_confidence'][0] == 'HIGH'
|
|
|
|
|
|
def test_sensory_keyword_match():
|
|
"""Test that phenotype terms matching SENSORY_MP_KEYWORDS are retained."""
|
|
phenotypes = pl.DataFrame({
|
|
'mouse_gene': ['Gene1', 'Gene1', 'Gene2'],
|
|
'mp_term_id': ['MP:0001', 'MP:0002', 'MP:0003'],
|
|
'mp_term_name': ['hearing loss', 'abnormal cochlea morphology', 'irrelevant phenotype'],
|
|
})
|
|
|
|
result = filter_sensory_phenotypes(phenotypes, SENSORY_MP_KEYWORDS, 'mp_term_name')
|
|
|
|
# Should keep first two rows (hearing, cochlea match keywords)
|
|
assert len(result) == 2
|
|
assert 'hearing loss' in result['mp_term_name'].to_list()
|
|
assert 'abnormal cochlea morphology' in result['mp_term_name'].to_list()
|
|
|
|
|
|
def test_non_sensory_filtered():
|
|
"""Test that non-sensory phenotypes are filtered out."""
|
|
phenotypes = pl.DataFrame({
|
|
'mouse_gene': ['Gene1', 'Gene2'],
|
|
'mp_term_id': ['MP:0001', 'MP:0002'],
|
|
'mp_term_name': ['increased body weight', 'abnormal coat color'],
|
|
})
|
|
|
|
result = filter_sensory_phenotypes(phenotypes, SENSORY_MP_KEYWORDS, 'mp_term_name')
|
|
|
|
# Should filter out both rows
|
|
assert len(result) == 0
|
|
|
|
|
|
def test_score_with_confidence_weighting():
|
|
"""Test that HIGH confidence orthologs score higher than LOW confidence."""
|
|
# Gene with HIGH confidence mouse ortholog
|
|
high_conf = pl.DataFrame({
|
|
'gene_id': ['ENSG00000001'],
|
|
'mouse_ortholog': ['Gene1'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [5],
|
|
})
|
|
|
|
# Gene with LOW confidence mouse ortholog
|
|
low_conf = pl.DataFrame({
|
|
'gene_id': ['ENSG00000002'],
|
|
'mouse_ortholog': ['Gene2'],
|
|
'mouse_ortholog_confidence': ['LOW'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [5],
|
|
})
|
|
|
|
high_result = score_animal_evidence(high_conf)
|
|
low_result = score_animal_evidence(low_conf)
|
|
|
|
high_score = high_result['animal_model_score_normalized'][0]
|
|
low_score = low_result['animal_model_score_normalized'][0]
|
|
|
|
# HIGH confidence should score higher (0.4 * 1.0 vs 0.4 * 0.4)
|
|
assert high_score > low_score
|
|
|
|
|
|
def test_score_null_no_ortholog():
|
|
"""Test that genes without orthologs get NULL score, not zero."""
|
|
df = pl.DataFrame({
|
|
'gene_id': ['ENSG00000001'],
|
|
'mouse_ortholog': [None],
|
|
'mouse_ortholog_confidence': [None],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [False],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [None],
|
|
})
|
|
|
|
result = score_animal_evidence(df)
|
|
|
|
# Should be NULL, not 0.0
|
|
assert result['animal_model_score_normalized'][0] is None
|
|
|
|
|
|
def test_multi_organism_bonus():
|
|
"""Test that phenotypes in both mouse and zebrafish result in higher score."""
|
|
# Gene with only mouse phenotype
|
|
mouse_only = pl.DataFrame({
|
|
'gene_id': ['ENSG00000001'],
|
|
'mouse_ortholog': ['Gene1'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [3],
|
|
})
|
|
|
|
# Gene with both mouse and zebrafish phenotypes
|
|
both = pl.DataFrame({
|
|
'gene_id': ['ENSG00000002'],
|
|
'mouse_ortholog': ['Gene2'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': ['gene2'],
|
|
'zebrafish_ortholog_confidence': ['HIGH'],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [True],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [3],
|
|
})
|
|
|
|
mouse_result = score_animal_evidence(mouse_only)
|
|
both_result = score_animal_evidence(both)
|
|
|
|
mouse_score = mouse_result['animal_model_score_normalized'][0]
|
|
both_score = both_result['animal_model_score_normalized'][0]
|
|
|
|
# Both organisms should score higher (0.4 + 0.3 vs 0.4)
|
|
assert both_score > mouse_score
|
|
|
|
|
|
def test_phenotype_count_scaling():
|
|
"""Test that more sensory phenotypes lead to higher scores (with diminishing returns)."""
|
|
# Gene with 1 phenotype
|
|
few = pl.DataFrame({
|
|
'gene_id': ['ENSG00000001'],
|
|
'mouse_ortholog': ['Gene1'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [1],
|
|
})
|
|
|
|
# Gene with 10 phenotypes
|
|
many = pl.DataFrame({
|
|
'gene_id': ['ENSG00000002'],
|
|
'mouse_ortholog': ['Gene2'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [10],
|
|
})
|
|
|
|
few_result = score_animal_evidence(few)
|
|
many_result = score_animal_evidence(many)
|
|
|
|
few_score = few_result['animal_model_score_normalized'][0]
|
|
many_score = many_result['animal_model_score_normalized'][0]
|
|
|
|
# More phenotypes should score higher
|
|
assert many_score > few_score
|
|
# But not linearly (diminishing returns via log)
|
|
# log2(11) / log2(11) = 1.0 vs log2(2) / log2(11) = 0.29
|
|
assert many_score < few_score * 10 # Not 10x higher
|
|
|
|
|
|
def test_impc_integration():
|
|
"""Test that IMPC phenotypes contribute to score."""
|
|
# Gene without IMPC
|
|
no_impc = pl.DataFrame({
|
|
'gene_id': ['ENSG00000001'],
|
|
'mouse_ortholog': ['Gene1'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [False],
|
|
'sensory_phenotype_count': [3],
|
|
})
|
|
|
|
# Gene with IMPC
|
|
with_impc = pl.DataFrame({
|
|
'gene_id': ['ENSG00000002'],
|
|
'mouse_ortholog': ['Gene2'],
|
|
'mouse_ortholog_confidence': ['HIGH'],
|
|
'zebrafish_ortholog': [None],
|
|
'zebrafish_ortholog_confidence': [None],
|
|
'has_mouse_phenotype': [True],
|
|
'has_zebrafish_phenotype': [False],
|
|
'has_impc_phenotype': [True],
|
|
'sensory_phenotype_count': [3],
|
|
})
|
|
|
|
no_impc_result = score_animal_evidence(no_impc)
|
|
with_impc_result = score_animal_evidence(with_impc)
|
|
|
|
no_impc_score = no_impc_result['animal_model_score_normalized'][0]
|
|
with_impc_score = with_impc_result['animal_model_score_normalized'][0]
|
|
|
|
# IMPC should add to score (+0.3)
|
|
assert with_impc_score > no_impc_score
|