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
270 lines
9.9 KiB
Python
270 lines
9.9 KiB
Python
"""Integration tests for animal model evidence layer."""
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import tempfile
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from pathlib import Path
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from unittest.mock import patch, Mock
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import polars as pl
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import pytest
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from usher_pipeline.evidence.animal_models import (
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process_animal_model_evidence,
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load_to_duckdb,
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)
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from usher_pipeline.persistence import PipelineStore, ProvenanceTracker
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@pytest.fixture
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def mock_hcop_data():
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"""Mock HCOP ortholog mapping data."""
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mouse_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
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123\tENSG00000001\tHGNC:1\tUSH2A\tUSH2A\t1\t\t456\tENSMUSG001\tMGI:1\tUsh2a\tUsh2a\t1\t\tdb1,db2,db3,db4,db5,db6,db7,db8
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456\tENSG00000002\tHGNC:2\tMYO7A\tMYO7A\t11\t\t789\tENSMUSG002\tMGI:2\tMyo7a\tMyo7a\t7\t\tdb1,db2,db3,db4,db5"""
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zebrafish_data = """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
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123\tENSG00000001\tHGNC:1\tUSH2A\tUSH2A\t1\t\t111\tENSDART001\tZDB-GENE-1\tush2a\tush2a\t1\t\tdb1,db2,db3,db4,db5,db6"""
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return {'mouse': mouse_data, 'zebrafish': zebrafish_data}
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@pytest.fixture
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def mock_phenotype_data():
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"""Mock MGI, ZFIN, and IMPC phenotype data."""
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mgi_data = """Marker Symbol\tMammalian Phenotype ID
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Ush2a\tMP:0001967
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Ush2a\tMP:0005377
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Myo7a\tMP:0001968"""
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zfin_data = """Gene Symbol\tAffected Structure or Process 1
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ush2a\tabnormal ear morphology
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ush2a\tabnormal retina morphology"""
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impc_responses = {
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'Ush2a': {
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'response': {
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'docs': [
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{
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'marker_symbol': 'Ush2a',
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'mp_term_id': 'MP:0001967',
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'mp_term_name': 'deafness',
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'p_value': 0.001
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}
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]
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}
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},
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'Myo7a': {
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'response': {
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'docs': [
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{
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'marker_symbol': 'Myo7a',
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'mp_term_id': 'MP:0001968',
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'mp_term_name': 'abnormal cochlea morphology',
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'p_value': 0.0005
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}
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]
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}
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}
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}
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return {'mgi': mgi_data, 'zfin': zfin_data, 'impc': impc_responses}
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def test_full_pipeline(mock_hcop_data, mock_phenotype_data):
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"""Test full animal model evidence pipeline with mocked data sources."""
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gene_ids = ['ENSG00000001', 'ENSG00000002']
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with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_hcop, \
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patch('usher_pipeline.evidence.animal_models.fetch._download_text') as mock_text, \
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patch('httpx.get') as mock_http:
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# Mock HCOP downloads
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mock_hcop.side_effect = [
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mock_hcop_data['mouse'].encode('utf-8'),
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mock_hcop_data['zebrafish'].encode('utf-8'),
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]
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# Mock MGI and ZFIN downloads
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mock_text.side_effect = [
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mock_phenotype_data['mgi'],
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mock_phenotype_data['zfin'],
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]
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# Mock IMPC API responses
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def mock_impc_response(url, **kwargs):
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response = Mock()
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response.raise_for_status = Mock()
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# Extract gene symbol from query
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query = kwargs.get('params', {}).get('q', '')
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if 'Ush2a' in query:
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response.json = Mock(return_value=mock_phenotype_data['impc']['Ush2a'])
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elif 'Myo7a' in query:
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response.json = Mock(return_value=mock_phenotype_data['impc']['Myo7a'])
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else:
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response.json = Mock(return_value={'response': {'docs': []}})
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return response
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mock_http.side_effect = mock_impc_response
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# Run pipeline
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result = process_animal_model_evidence(gene_ids)
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# Verify results
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assert len(result) == 2
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# Check USH2A (ENSG00000001)
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ush2a = result.filter(pl.col('gene_id') == 'ENSG00000001')
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assert len(ush2a) == 1
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assert ush2a['mouse_ortholog'][0] == 'Ush2a'
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assert ush2a['mouse_ortholog_confidence'][0] == 'HIGH' # 8 sources
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assert ush2a['zebrafish_ortholog'][0] == 'ush2a'
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assert ush2a['zebrafish_ortholog_confidence'][0] == 'MEDIUM' # 6 sources
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assert ush2a['sensory_phenotype_count'][0] is not None
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assert ush2a['animal_model_score_normalized'][0] is not None
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assert ush2a['animal_model_score_normalized'][0] > 0
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# Check MYO7A (ENSG00000002)
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myo7a = result.filter(pl.col('gene_id') == 'ENSG00000002')
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assert len(myo7a) == 1
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assert myo7a['mouse_ortholog'][0] == 'Myo7a'
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assert myo7a['mouse_ortholog_confidence'][0] == 'MEDIUM' # 5 sources
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def test_checkpoint_restart(mock_hcop_data, mock_phenotype_data):
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"""Test checkpoint-restart pattern: load from DuckDB if exists, skip reprocessing."""
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with tempfile.TemporaryDirectory() as tmpdir:
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db_path = Path(tmpdir) / "test.duckdb"
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store = PipelineStore(db_path)
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# Initial load
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gene_ids = ['ENSG00000001', 'ENSG00000002']
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with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_hcop, \
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patch('usher_pipeline.evidence.animal_models.fetch._download_text') as mock_text, \
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patch('httpx.get') as mock_http:
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mock_hcop.side_effect = [
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mock_hcop_data['mouse'].encode('utf-8'),
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mock_hcop_data['zebrafish'].encode('utf-8'),
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]
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mock_text.side_effect = [
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mock_phenotype_data['mgi'],
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mock_phenotype_data['zfin'],
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]
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def mock_impc_response(url, **kwargs):
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response = Mock()
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response.raise_for_status = Mock()
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response.json = Mock(return_value={'response': {'docs': []}})
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return response
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mock_http.side_effect = mock_impc_response
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df = process_animal_model_evidence(gene_ids)
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# Save to DuckDB (use mock provenance tracker)
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provenance = Mock()
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provenance.record_step = Mock()
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load_to_duckdb(df, store, provenance)
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# Check checkpoint exists
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assert store.has_checkpoint('animal_model_phenotypes')
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# Load from checkpoint
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loaded_df = store.load_dataframe('animal_model_phenotypes')
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assert loaded_df is not None
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assert len(loaded_df) == 2
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store.close()
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def test_provenance_tracking(mock_hcop_data, mock_phenotype_data):
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"""Test that provenance metadata is correctly recorded."""
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with tempfile.TemporaryDirectory() as tmpdir:
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db_path = Path(tmpdir) / "test.duckdb"
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store = PipelineStore(db_path)
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gene_ids = ['ENSG00000001', 'ENSG00000002']
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with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_hcop, \
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patch('usher_pipeline.evidence.animal_models.fetch._download_text') as mock_text, \
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patch('httpx.get') as mock_http:
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mock_hcop.side_effect = [
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mock_hcop_data['mouse'].encode('utf-8'),
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mock_hcop_data['zebrafish'].encode('utf-8'),
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]
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mock_text.side_effect = [
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mock_phenotype_data['mgi'],
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mock_phenotype_data['zfin'],
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]
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def mock_impc_response(url, **kwargs):
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response = Mock()
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response.raise_for_status = Mock()
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response.json = Mock(return_value={'response': {'docs': []}})
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return response
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mock_http.side_effect = mock_impc_response
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df = process_animal_model_evidence(gene_ids)
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# Track provenance (use mock)
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provenance = Mock()
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provenance.record_step = Mock()
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provenance.get_steps = Mock(return_value=[
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{'step': 'load_animal_model_phenotypes', 'row_count': 2}
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])
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load_to_duckdb(df, store, provenance, description="Test animal model data")
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# Check provenance was recorded
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steps = provenance.get_steps()
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assert len(steps) > 0
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load_step = next((s for s in steps if s['step'] == 'load_animal_model_phenotypes'), None)
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assert load_step is not None
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assert 'row_count' in load_step
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assert load_step['row_count'] == 2
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store.close()
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def test_empty_phenotype_handling(mock_hcop_data):
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"""Test handling of genes with orthologs but no phenotypes."""
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gene_ids = ['ENSG00000001']
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with patch('usher_pipeline.evidence.animal_models.fetch._download_gzipped') as mock_hcop, \
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patch('usher_pipeline.evidence.animal_models.fetch._download_text') as mock_text, \
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patch('httpx.get') as mock_http:
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mock_hcop.side_effect = [
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mock_hcop_data['mouse'].encode('utf-8'),
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mock_hcop_data['zebrafish'].encode('utf-8'),
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]
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# Empty phenotype data
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empty_mgi = """Marker Symbol\tMammalian Phenotype ID
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"""
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empty_zfin = """Gene Symbol\tAffected Structure or Process 1
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"""
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mock_text.side_effect = [empty_mgi, empty_zfin]
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def mock_impc_response(url, **kwargs):
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response = Mock()
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response.raise_for_status = Mock()
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response.json = Mock(return_value={'response': {'docs': []}})
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return response
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mock_http.side_effect = mock_impc_response
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result = process_animal_model_evidence(gene_ids)
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# Should have ortholog mapping but NULL sensory phenotype count
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assert len(result) == 1
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assert result['mouse_ortholog'][0] == 'Ush2a'
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assert result['sensory_phenotype_count'][0] is None
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# Score should still be calculated (but low since no phenotypes)
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assert result['animal_model_score_normalized'][0] is not None
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