Datasets:
Formats:
parquet
Languages:
English
Size:
10M - 100M
Tags:
biology
chemistry
drug-discovery
clinical-trials
protein-protein-interaction
gene-essentiality
License:
| """Tests for NegBioDB PPI domain database layer.""" | |
| import sqlite3 | |
| from pathlib import Path | |
| import pytest | |
| from negbiodb_ppi.ppi_db import ( | |
| create_ppi_database, | |
| get_connection, | |
| refresh_all_ppi_pairs, | |
| run_ppi_migrations, | |
| ) | |
| MIGRATIONS_DIR = Path(__file__).resolve().parent.parent / "migrations_ppi" | |
| def ppi_db(tmp_path): | |
| """Create a temporary PPI database with all migrations applied.""" | |
| db_path = tmp_path / "test_ppi.db" | |
| run_ppi_migrations(db_path, MIGRATIONS_DIR) | |
| return db_path | |
| class TestPPIMigrations: | |
| """Test PPI schema creation and migrations.""" | |
| def test_create_ppi_database(self, tmp_path): | |
| db_path = tmp_path / "test.db" | |
| result = create_ppi_database(db_path, MIGRATIONS_DIR) | |
| assert result == db_path | |
| assert db_path.exists() | |
| def test_migration_001_applied(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| versions = conn.execute( | |
| "SELECT version FROM schema_migrations" | |
| ).fetchall() | |
| assert ("001",) in versions | |
| finally: | |
| conn.close() | |
| def test_idempotent_migrations(self, ppi_db): | |
| """Running migrations twice should not fail.""" | |
| applied = run_ppi_migrations(ppi_db, MIGRATIONS_DIR) | |
| assert applied == [] | |
| def test_all_tables_exist(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| tables = { | |
| row[0] | |
| for row in conn.execute( | |
| "SELECT name FROM sqlite_master WHERE type='table'" | |
| ).fetchall() | |
| } | |
| expected = { | |
| "schema_migrations", | |
| "dataset_versions", | |
| "proteins", | |
| "ppi_experiments", | |
| "ppi_negative_results", | |
| "protein_protein_pairs", | |
| "ppi_split_definitions", | |
| "ppi_split_assignments", | |
| } | |
| assert expected.issubset(tables), f"Missing: {expected - tables}" | |
| finally: | |
| conn.close() | |
| def test_all_indices_exist(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| indices = { | |
| row[0] | |
| for row in conn.execute( | |
| "SELECT name FROM sqlite_master WHERE type='index'" | |
| ).fetchall() | |
| } | |
| expected = { | |
| "idx_proteins_uniprot", | |
| "idx_proteins_gene", | |
| "idx_ppi_exp_source", | |
| "idx_ppi_nr_protein1", | |
| "idx_ppi_nr_protein2", | |
| "idx_ppi_nr_pair", | |
| "idx_ppi_nr_tier", | |
| "idx_ppi_nr_source", | |
| "idx_ppi_nr_unique_source", | |
| "idx_ppp_protein1", | |
| "idx_ppp_protein2", | |
| "idx_ppp_confidence", | |
| "idx_ppi_splits_fold", | |
| } | |
| assert expected.issubset(indices), f"Missing: {expected - indices}" | |
| finally: | |
| conn.close() | |
| class TestPPISchema: | |
| """Test PPI schema constraints and foreign keys.""" | |
| def test_protein_unique_constraint(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('P12345')" | |
| ) | |
| conn.commit() | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('P12345')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_evidence_type_check(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| # Insert required proteins | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| # Valid evidence type | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| conn.commit() | |
| # Invalid evidence type | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'invalid_type', 'gold', " | |
| "'intact', 'R002', 'database_direct')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_confidence_tier_check(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'experimental_non_interaction', 'platinum', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_canonical_ordering_enforced(self, ppi_db): | |
| """protein1_id must be < protein2_id.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| # protein1_id=2, protein2_id=1 violates CHECK | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (2, 1, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_canonical_ordering_equal_ids_rejected(self, ppi_db): | |
| """protein1_id == protein2_id violates CHECK (strict less-than).""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.commit() | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 1, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_extraction_method_check(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'invalid_method')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_foreign_key_enforcement(self, ppi_db): | |
| """FK violations should raise errors.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (999, 1000, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_dedup_unique_index(self, ppi_db): | |
| """Same (pair, experiment, source, record) should fail.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| conn.commit() | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'ml_predicted_negative', 'silver', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_dedup_allows_different_source_records(self, ppi_db): | |
| """Same pair but different source_record_id is OK.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, confidence_tier, " | |
| " source_db, source_record_id, extraction_method) " | |
| "VALUES (1, 2, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R002', 'database_direct')" | |
| ) | |
| conn.commit() | |
| count = conn.execute( | |
| "SELECT COUNT(*) FROM ppi_negative_results" | |
| ).fetchone()[0] | |
| assert count == 2 | |
| finally: | |
| conn.close() | |
| def test_experiment_source_db_check(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| # Valid source_db | |
| conn.execute( | |
| "INSERT INTO ppi_experiments " | |
| "(source_db, source_experiment_id) " | |
| "VALUES ('intact', 'EXP001')" | |
| ) | |
| conn.commit() | |
| # Invalid source_db | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_experiments " | |
| "(source_db, source_experiment_id) " | |
| "VALUES ('invalid_db', 'EXP002')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_experiment_huri_source_db(self, ppi_db): | |
| """HuRI is a valid source_db.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO ppi_experiments " | |
| "(source_db, source_experiment_id) " | |
| "VALUES ('huri', 'HI-III-20')" | |
| ) | |
| conn.commit() | |
| count = conn.execute( | |
| "SELECT COUNT(*) FROM ppi_experiments WHERE source_db = 'huri'" | |
| ).fetchone()[0] | |
| assert count == 1 | |
| finally: | |
| conn.close() | |
| def test_split_strategy_check(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| # Valid strategy | |
| conn.execute( | |
| "INSERT INTO ppi_split_definitions " | |
| "(split_name, split_strategy) " | |
| "VALUES ('random_v1', 'random')" | |
| ) | |
| conn.commit() | |
| # Invalid strategy | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO ppi_split_definitions " | |
| "(split_name, split_strategy) " | |
| "VALUES ('bad_v1', 'invalid_strategy')" | |
| ) | |
| finally: | |
| conn.close() | |
| def test_pairs_canonical_ordering(self, ppi_db): | |
| """protein_protein_pairs also enforces protein1 < protein2.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.commit() | |
| with pytest.raises(sqlite3.IntegrityError): | |
| conn.execute( | |
| "INSERT INTO protein_protein_pairs " | |
| "(protein1_id, protein2_id, num_experiments, num_sources, " | |
| " best_confidence) " | |
| "VALUES (2, 1, 1, 1, 'gold')" | |
| ) | |
| finally: | |
| conn.close() | |
| class TestRefreshPPIPairs: | |
| """Test protein_protein_pairs aggregation.""" | |
| def _insert_test_data(self, conn): | |
| """Insert minimal test data for aggregation testing.""" | |
| # 3 proteins: A(1), B(2), C(3) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('A00001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('B00002')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO proteins (uniprot_accession) VALUES ('C00003')" | |
| ) | |
| # Experiments | |
| conn.execute( | |
| "INSERT INTO ppi_experiments " | |
| "(source_db, source_experiment_id) " | |
| "VALUES ('intact', 'EXP001')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO ppi_experiments " | |
| "(source_db, source_experiment_id) " | |
| "VALUES ('humap', 'HUMAP001')" | |
| ) | |
| # A-B: 2 results (gold + silver, different sources) | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, experiment_id, evidence_type, " | |
| " confidence_tier, source_db, source_record_id, " | |
| " extraction_method, publication_year, interaction_score) " | |
| "VALUES (1, 2, 1, 'experimental_non_interaction', 'gold', " | |
| "'intact', 'R001', 'database_direct', 2020, 0.05)" | |
| ) | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, experiment_id, evidence_type, " | |
| " confidence_tier, source_db, source_record_id, " | |
| " extraction_method, publication_year, interaction_score) " | |
| "VALUES (1, 2, 2, 'ml_predicted_negative', 'silver', " | |
| "'humap', 'R002', 'ml_classifier', 2023, 0.01)" | |
| ) | |
| # A-C: 1 result (bronze) | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, " | |
| " confidence_tier, source_db, source_record_id, " | |
| " extraction_method, publication_year) " | |
| "VALUES (1, 3, 'low_score_negative', 'bronze', " | |
| "'string', 'R003', 'score_threshold', 2022)" | |
| ) | |
| # B-C: 1 result (copper) | |
| conn.execute( | |
| "INSERT INTO ppi_negative_results " | |
| "(protein1_id, protein2_id, evidence_type, " | |
| " confidence_tier, source_db, source_record_id, " | |
| " extraction_method, publication_year) " | |
| "VALUES (2, 3, 'compartment_separated', 'copper', " | |
| "'string', 'R004', 'score_threshold', 2021)" | |
| ) | |
| conn.commit() | |
| def test_refresh_pair_count(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| count = refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| assert count == 3 # A-B, A-C, B-C | |
| finally: | |
| conn.close() | |
| def test_best_confidence_aggregation(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| row = conn.execute( | |
| "SELECT best_confidence, num_experiments, num_sources " | |
| "FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 2" | |
| ).fetchone() | |
| assert row[0] == "gold" # best of gold + silver | |
| assert row[1] == 2 # 2 distinct experiments | |
| assert row[2] == 2 # 2 distinct sources (intact, humap) | |
| finally: | |
| conn.close() | |
| def test_best_evidence_type_priority(self, ppi_db): | |
| """experimental_non_interaction should rank above ml_predicted.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| row = conn.execute( | |
| "SELECT best_evidence_type FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 2" | |
| ).fetchone() | |
| assert row[0] == "experimental_non_interaction" | |
| finally: | |
| conn.close() | |
| def test_score_range(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| row = conn.execute( | |
| "SELECT min_interaction_score, max_interaction_score " | |
| "FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 2" | |
| ).fetchone() | |
| assert row[0] == pytest.approx(0.01) | |
| assert row[1] == pytest.approx(0.05) | |
| finally: | |
| conn.close() | |
| def test_earliest_year(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| row = conn.execute( | |
| "SELECT earliest_year FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 2" | |
| ).fetchone() | |
| assert row[0] == 2020 | |
| finally: | |
| conn.close() | |
| def test_degree_computation_protein1(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| # Protein A (id=1) appears as protein1 in A-B and A-C → degree 2 | |
| row = conn.execute( | |
| "SELECT protein1_degree FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 2" | |
| ).fetchone() | |
| assert row[0] == 2 | |
| finally: | |
| conn.close() | |
| def test_degree_computation_protein2(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| # Protein C (id=3) appears as protein2 in A-C and B-C → degree 2 | |
| row = conn.execute( | |
| "SELECT protein2_degree FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 3" | |
| ).fetchone() | |
| assert row[0] == 2 | |
| finally: | |
| conn.close() | |
| def test_degree_computation_both_sides(self, ppi_db): | |
| """Protein B (id=2) appears as protein2 in A-B and protein1 in B-C. | |
| True degree = 2 (partners A and C) regardless of which side it's on.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| # In A-B row, protein2 is B → protein2_degree = B's full degree = 2 | |
| row = conn.execute( | |
| "SELECT protein2_degree FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 2" | |
| ).fetchone() | |
| assert row[0] == 2 | |
| # In B-C row, protein1 is B → protein1_degree = B's full degree = 2 | |
| row = conn.execute( | |
| "SELECT protein1_degree FROM protein_protein_pairs " | |
| "WHERE protein1_id = 2 AND protein2_id = 3" | |
| ).fetchone() | |
| assert row[0] == 2 | |
| finally: | |
| conn.close() | |
| def test_refresh_is_idempotent(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| count1 = refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| count2 = refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| assert count1 == count2 | |
| finally: | |
| conn.close() | |
| def test_refresh_clears_split_assignments(self, ppi_db): | |
| """Refresh should succeed even when split assignments exist (FK).""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| # Create a split and assign a pair | |
| conn.execute( | |
| "INSERT INTO ppi_split_definitions " | |
| "(split_name, split_strategy) VALUES ('test_v1', 'random')" | |
| ) | |
| conn.execute( | |
| "INSERT INTO ppi_split_assignments (pair_id, split_id, fold) " | |
| "VALUES (1, 1, 'train')" | |
| ) | |
| conn.commit() | |
| # Re-refresh should not crash despite FK to split_assignments | |
| count = refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| assert count == 3 | |
| # Split assignments should be cleared | |
| sa_count = conn.execute( | |
| "SELECT COUNT(*) FROM ppi_split_assignments" | |
| ).fetchone()[0] | |
| assert sa_count == 0 | |
| finally: | |
| conn.close() | |
| def test_null_experiment_counted(self, ppi_db): | |
| """Results without experiment_id should use COALESCE sentinel.""" | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| # A-C has NULL experiment_id → COALESCE(-1) → counted as 1 | |
| row = conn.execute( | |
| "SELECT num_experiments FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 3" | |
| ).fetchone() | |
| assert row[0] == 1 | |
| finally: | |
| conn.close() | |
| def test_single_source_count(self, ppi_db): | |
| conn = get_connection(ppi_db) | |
| try: | |
| self._insert_test_data(conn) | |
| refresh_all_ppi_pairs(conn) | |
| conn.commit() | |
| # A-C has 1 source (string) | |
| row = conn.execute( | |
| "SELECT num_sources FROM protein_protein_pairs " | |
| "WHERE protein1_id = 1 AND protein2_id = 3" | |
| ).fetchone() | |
| assert row[0] == 1 | |
| finally: | |
| conn.close() | |