Mercurial > repos > bimib > cobraxy
changeset 503:8dd07e59f631 draft
Uploaded
| author | francesco_lapi |
|---|---|
| date | Tue, 30 Sep 2025 18:01:30 +0000 |
| parents | 054c872e3880 |
| children | ef3df9b697b1 |
| files | COBRAxy/metabolicModel2Tabular.xml COBRAxy/test_gpr_translation_comprehensive.py COBRAxy/utils/model_utils.py |
| diffstat | 3 files changed, 12 insertions(+), 626 deletions(-) [+] |
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--- a/COBRAxy/metabolicModel2Tabular.xml Tue Sep 30 17:18:55 2025 +0000 +++ b/COBRAxy/metabolicModel2Tabular.xml Tue Sep 30 18:01:30 2025 +0000 @@ -43,7 +43,7 @@ <param name="gene_format" argument="--gene_format" type="select" label="Gene nomenclature format:"> <option value="Default" selected="true">Keep original gene nomenclature (HGNC Symbol)</option> - <option value="ENSG">ENSNG (Ensembl Gene ID)</option> + <option value="ENSG">ENSG (Ensembl Gene ID)</option> <option value="HGNC_ID">HGNC ID</option> <option value="entrez_id">Entrez Gene ID</option> </param>
--- a/COBRAxy/test_gpr_translation_comprehensive.py Tue Sep 30 17:18:55 2025 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,618 +0,0 @@ -#!/usr/bin/env python3 -""" -Comprehensive test suite for GPR translation functionality in COBRAxy. - -This test suite covers: -- Basic 1:1, 1:many, many:1 gene mappings -- Complex GPR expressions with AND/OR logic -- Translation issues tracking -- OR-only GPR flattening functionality -- Edge cases and nested expressions -- Statistical reporting -""" - -import cobra -import pandas as pd -import sys -import os -import logging -from typing import Dict, List, Tuple -import re - -# Add the COBRAxy utils directory to the path -sys.path.append('/hdd/home/flapi/COBRAxy') -from utils import model_utils - -# Configure logging -logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s') -logger = logging.getLogger(__name__) - -class GPRTranslationTester: - """Comprehensive GPR translation test suite""" - - def __init__(self): - self.test_results = {} - self.failed_tests = [] - - def create_comprehensive_test_model(self) -> cobra.Model: - """Create a comprehensive test model with diverse GPR patterns""" - model = cobra.Model('comprehensive_test_model') - - # Create metabolites - metabolites = [] - for i in range(30): - met = cobra.Metabolite(f'met_{chr(65+i%26)}{i//26}', compartment='c') - metabolites.append(met) - - reactions_data = [ - # === BASIC CASES === - ('BASIC_1to1', 'GENE1', 0, 1), # Simple 1:1 mapping - ('BASIC_1tomany', 'GENE2', 1, 2), # 1:many mapping - ('BASIC_manyto1', 'GENE3', 2, 3), # many:1 mapping - ('BASIC_unmapped', 'UNMAPPED_GENE', 3, 4), # unmapped gene - - # === SIMPLE OR CASES (candidates for flattening) === - ('OR_simple', 'GENE4 or GENE5', 4, 5), # Simple OR with many:1 - ('OR_three', 'GENE6 or GENE7 or GENE8', 5, 6), # Three genes OR - ('OR_parentheses', '(GENE9 or GENE10)', 6, 7), # OR with parentheses - ('OR_duplicates', 'GENE11 or GENE12 or GENE11', 7, 8), # OR with duplicates after translation - - # === COMPLEX OR CASES (candidates for flattening) === - ('OR_nested_simple', '(GENE13 or GENE14) or (GENE15 or GENE16)', 8, 9), # Nested OR only - ('OR_many_parentheses', '((GENE17 or GENE18) or GENE19) or GENE20', 9, 10), # Multiple nesting levels - ('OR_mixed_mapping', 'GENE21 or GENE22 or GENE23', 10, 11), # Mixed 1:1, 1:many, many:1 - - # === AND CASES (should NOT be flattened) === - ('AND_simple', 'GENE24 and GENE25', 11, 12), # Simple AND - ('AND_complex', '(GENE26 and GENE27) and GENE28', 12, 13), # Complex AND - - # === MIXED AND/OR (should NOT be flattened) === - ('MIXED_basic', 'GENE29 and (GENE30 or GENE31)', 13, 14), # AND with OR - ('MIXED_complex', '(GENE32 or GENE33) and (GENE34 or GENE35)', 14, 15), # OR and AND - ('MIXED_nested', '((GENE36 and GENE37) or GENE38) and GENE39', 15, 16), # Complex nesting - - # === EDGE CASES === - ('EDGE_single', 'GENE40', 16, 17), # Single gene - ('EDGE_empty', '', 17, 18), # Empty GPR - ('EDGE_whitespace', ' GENE41 or GENE42 ', 18, 19), # Whitespace - ('EDGE_case_sensitive', 'Gene43 OR gene44', 19, 20), # Case variations - - # === STRESS TESTS === - ('STRESS_long_or', 'GENE45 or GENE46 or GENE47 or GENE48 or GENE49 or GENE50', 20, 21), # Long OR chain - ('STRESS_deep_nest', '(((GENE51 or GENE52) or GENE53) or GENE54)', 21, 22), # Deep nesting - ('STRESS_complex', '(GENE55 or (GENE56 or GENE57)) or ((GENE58 or GENE59) or GENE60)', 22, 23), # Complex structure - - # === TRANSLATION ISSUE TRIGGERS === - ('ISSUE_1many_or', 'GENE61 or GENE62', 23, 24), # 1:many in OR (should be flattened) - ('ISSUE_manyto1_and', 'GENE63 and GENE64', 24, 25), # many:1 in AND (should NOT be flattened) - ('ISSUE_mixed_problems', '(GENE65 or GENE66) and GENE67', 25, 26), # Mixed problems - - # === REAL-WORLD INSPIRED CASES === - ('REAL_metabolism', '(ENSG001 or ENSG002) or (ENSG003 or ENSG004)', 26, 27), # Metabolic pathway - ('REAL_transport', 'TRANSPORTER1 and (COFACTOR1 or COFACTOR2)', 27, 28), # Transport reaction - ('REAL_complex_enzyme', '((SUBUNIT1 and SUBUNIT2) or SUBUNIT3) and COFACTOR3', 28, 29), # Complex enzyme - ] - - # Create reactions - for rxn_id, gpr, met_in, met_out in reactions_data: - rxn = cobra.Reaction(rxn_id) - if met_in < len(metabolites) and met_out < len(metabolites): - rxn.add_metabolites({metabolites[met_in]: -1, metabolites[met_out]: 1}) - rxn.gene_reaction_rule = gpr - model.add_reactions([rxn]) - - return model - - def create_comprehensive_mapping(self) -> pd.DataFrame: - """Create a comprehensive gene mapping covering all test scenarios""" - mapping_data = { - 'hgnc_symbol': [], - 'ensg': [] - } - - # === BASIC MAPPINGS === - # 1:1 mappings - one_to_one = [ - ('GENE1', 'TARGET1'), - ('GENE24', 'TARGET24'), - ('GENE25', 'TARGET25'), - ('GENE26', 'TARGET26'), - ('GENE27', 'TARGET27'), - ('GENE28', 'TARGET28'), - ('GENE29', 'TARGET29'), - ('GENE40', 'TARGET40'), - ('GENE41', 'TARGET41'), - ('GENE42', 'TARGET42'), - ] - - # 1:many mappings (one source gene maps to multiple targets) - one_to_many = [ - ('GENE2', 'TARGET2A'), - ('GENE2', 'TARGET2B'), - ('GENE30', 'TARGET30A'), - ('GENE30', 'TARGET30B'), - ('GENE61', 'TARGET61A'), - ('GENE61', 'TARGET61B'), - ('GENE61', 'TARGET61C'), # Maps to 3 targets - ('GENE65', 'TARGET65A'), - ('GENE65', 'TARGET65B'), - ] - - # many:1 mappings (multiple source genes map to one target) - many_to_one = [ - ('GENE3', 'SHARED_TARGET1'), - ('GENE4', 'SHARED_TARGET1'), - ('GENE5', 'SHARED_TARGET1'), - ('GENE6', 'SHARED_TARGET2'), - ('GENE7', 'SHARED_TARGET2'), - ('GENE8', 'SHARED_TARGET2'), - ('GENE9', 'SHARED_TARGET3'), - ('GENE10', 'SHARED_TARGET3'), - ('GENE11', 'SHARED_TARGET4'), - ('GENE12', 'SHARED_TARGET4'), - ('GENE13', 'SHARED_TARGET5'), - ('GENE14', 'SHARED_TARGET5'), - ('GENE15', 'SHARED_TARGET5'), - ('GENE16', 'SHARED_TARGET5'), - ('GENE17', 'SHARED_TARGET6'), - ('GENE18', 'SHARED_TARGET6'), - ('GENE19', 'SHARED_TARGET6'), - ('GENE20', 'SHARED_TARGET6'), - ('GENE45', 'SHARED_TARGET7'), - ('GENE46', 'SHARED_TARGET7'), - ('GENE47', 'SHARED_TARGET7'), - ('GENE48', 'SHARED_TARGET7'), - ('GENE49', 'SHARED_TARGET7'), - ('GENE50', 'SHARED_TARGET7'), - ('GENE51', 'SHARED_TARGET8'), - ('GENE52', 'SHARED_TARGET8'), - ('GENE53', 'SHARED_TARGET8'), - ('GENE54', 'SHARED_TARGET8'), - ('GENE55', 'SHARED_TARGET9'), - ('GENE56', 'SHARED_TARGET9'), - ('GENE57', 'SHARED_TARGET9'), - ('GENE58', 'SHARED_TARGET9'), - ('GENE59', 'SHARED_TARGET9'), - ('GENE60', 'SHARED_TARGET9'), - ('GENE63', 'SHARED_TARGET10'), - ('GENE64', 'SHARED_TARGET10'), - ('GENE66', 'SHARED_TARGET11'), - ] - - # Mixed mappings for complex cases - mixed_mappings = [ - ('GENE21', 'TARGET21'), # 1:1 - ('GENE22', 'TARGET22A'), # 1:many - ('GENE22', 'TARGET22B'), - ('GENE23', 'SHARED_TARGET1'), # many:1 (shares with GENE3-5) - ('GENE31', 'SHARED_TARGET12'), - ('GENE32', 'SHARED_TARGET13'), - ('GENE33', 'SHARED_TARGET13'), - ('GENE34', 'TARGET34'), - ('GENE35', 'TARGET35'), - ('GENE36', 'TARGET36'), - ('GENE37', 'TARGET37'), - ('GENE38', 'TARGET38'), - ('GENE39', 'TARGET39'), - ('GENE62', 'TARGET62A'), - ('GENE62', 'TARGET62B'), - ('GENE67', 'TARGET67'), - ] - - # Case sensitivity tests - case_mappings = [ - ('Gene43', 'TARGET43'), - ('gene44', 'TARGET44'), - ] - - # Real-world inspired mappings - real_mappings = [ - ('ENSG001', 'HUMAN_GENE1'), - ('ENSG002', 'HUMAN_GENE2'), - ('ENSG003', 'HUMAN_GENE1'), # many:1 - ('ENSG004', 'HUMAN_GENE2'), # many:1 - ('TRANSPORTER1', 'SLC_FAMILY1'), - ('COFACTOR1', 'COFACTOR_A'), - ('COFACTOR2', 'COFACTOR_A'), # many:1 - ('COFACTOR3', 'COFACTOR_B'), - ('SUBUNIT1', 'COMPLEX_SUBUNIT1'), - ('SUBUNIT2', 'COMPLEX_SUBUNIT2'), - ('SUBUNIT3', 'COMPLEX_ALTERNATIVE'), - ] - - # Combine all mappings - all_mappings = one_to_one + one_to_many + many_to_one + mixed_mappings + case_mappings + real_mappings - - for source, target in all_mappings: - mapping_data['hgnc_symbol'].append(source) - mapping_data['ensg'].append(target) - - return pd.DataFrame(mapping_data) - - def analyze_mapping_statistics(self, mapping_df: pd.DataFrame) -> Dict: - """Analyze mapping statistics""" - stats = {} - - source_counts = mapping_df.groupby('hgnc_symbol')['ensg'].count() - target_counts = mapping_df.groupby('ensg')['hgnc_symbol'].count() - - stats['total_mappings'] = len(mapping_df) - stats['unique_sources'] = len(source_counts) - stats['unique_targets'] = len(target_counts) - - stats['one_to_one'] = (source_counts == 1).sum() - stats['one_to_many'] = (source_counts > 1).sum() - stats['many_to_one_targets'] = (target_counts > 1).sum() - - stats['one_to_many_details'] = {} - for gene, count in source_counts[source_counts > 1].items(): - targets = mapping_df[mapping_df['hgnc_symbol'] == gene]['ensg'].tolist() - stats['one_to_many_details'][gene] = targets - - stats['many_to_one_details'] = {} - for target, count in target_counts[target_counts > 1].items(): - sources = mapping_df[mapping_df['ensg'] == target]['hgnc_symbol'].tolist() - stats['many_to_one_details'][target] = sources - - return stats - - def predict_translation_issues(self, model: cobra.Model, mapping_df: pd.DataFrame) -> Dict: - """Predict which reactions will have translation issues""" - predictions = {} - mapping_dict = {} - - # Build mapping dictionary - for _, row in mapping_df.iterrows(): - source = row['hgnc_symbol'] - target = row['ensg'] - if source not in mapping_dict: - mapping_dict[source] = [] - mapping_dict[source].append(target) - - for rxn in model.reactions: - if not rxn.gene_reaction_rule or rxn.gene_reaction_rule.strip() == '': - continue - - # Extract genes from GPR - token_pattern = r'\b[A-Za-z0-9:_.-]+\b' - tokens = re.findall(token_pattern, rxn.gene_reaction_rule) - logical_operators = {'and', 'or', 'AND', 'OR', '(', ')'} - genes = [t for t in tokens if t not in logical_operators] - - issues = [] - has_1_to_many = False - has_many_to_1 = False - has_unmapped = False - - for gene in set(genes): - norm_gene = model_utils._normalize_gene_id(gene) - if norm_gene in mapping_dict: - targets = mapping_dict[norm_gene] - if len(targets) > 1: - has_1_to_many = True - issues.append(f"1:many - {gene} -> {targets}") - else: - has_unmapped = True - issues.append(f"unmapped - {gene}") - - # Check for many:1 mappings - target_to_sources = {} - for gene in set(genes): - norm_gene = model_utils._normalize_gene_id(gene) - if norm_gene in mapping_dict: - for target in mapping_dict[norm_gene]: - if target not in target_to_sources: - target_to_sources[target] = [] - target_to_sources[target].append(gene) - - for target, sources in target_to_sources.items(): - if len(sources) > 1: - has_many_to_1 = True - issues.append(f"many:1 - {sources} -> {target}") - - if issues: - predictions[rxn.id] = { - 'issues': issues, - 'has_1_to_many': has_1_to_many, - 'has_many_to_1': has_many_to_1, - 'has_unmapped': has_unmapped, - 'is_or_only': self._check_if_or_only(rxn.gene_reaction_rule), - 'predicted_flattening': has_1_to_many or has_many_to_1 and self._check_if_or_only(rxn.gene_reaction_rule) - } - - return predictions - - def _check_if_or_only(self, gpr: str) -> bool: - """Check if GPR contains only OR operators (and parentheses)""" - if not gpr or gpr.strip() == '': - return False - - # Remove gene names and whitespace, keep only logical operators - token_pattern = r'\b[A-Za-z0-9:_.-]+\b' - logic_only = re.sub(token_pattern, '', gpr) - logic_only = re.sub(r'\s+', ' ', logic_only.strip()) - - # Check for AND operators - and_pattern = r'\b(and|AND)\b' - return not bool(re.search(and_pattern, logic_only)) - - def run_comprehensive_test(self) -> Dict: - """Run the comprehensive translation test""" - print("="*80) - print("COMPREHENSIVE GPR TRANSLATION TEST SUITE") - print("="*80) - - # Create test model and mapping - print("\n1. Creating test model and mapping...") - model = self.create_comprehensive_test_model() - mapping_df = self.create_comprehensive_mapping() - - print(f" ✓ Created model with {len(model.reactions)} reactions") - print(f" ✓ Created mapping with {len(mapping_df)} entries") - - # Analyze mapping statistics - print("\n2. Analyzing mapping statistics...") - mapping_stats = self.analyze_mapping_statistics(mapping_df) - print(f" ✓ Unique source genes: {mapping_stats['unique_sources']}") - print(f" ✓ Unique target genes: {mapping_stats['unique_targets']}") - print(f" ✓ 1:1 mappings: {mapping_stats['one_to_one']}") - print(f" ✓ 1:many mappings: {mapping_stats['one_to_many']}") - print(f" ✓ Many:1 target genes: {mapping_stats['many_to_one_targets']}") - - # Predict translation issues - print("\n3. Predicting translation issues...") - predicted_issues = self.predict_translation_issues(model, mapping_df) - predicted_or_only = sum(1 for pred in predicted_issues.values() if pred['is_or_only']) - predicted_flattening = sum(1 for pred in predicted_issues.values() if pred['predicted_flattening']) - - print(f" ✓ Reactions with predicted issues: {len(predicted_issues)}") - print(f" ✓ OR-only reactions: {predicted_or_only}") - print(f" ✓ Predicted for flattening: {predicted_flattening}") - - # Display original GPRs - print("\n4. Original model GPRs:") - for rxn in sorted(model.reactions, key=lambda x: x.id): - status = "🔍" if rxn.id in predicted_issues else "✓" - or_only = "🔗" if predicted_issues.get(rxn.id, {}).get('is_or_only', False) else " " - print(f" {status}{or_only} {rxn.id:20} : {rxn.gene_reaction_rule}") - - # Run translation - print("\n5. Running translation...") - try: - translated_model, translation_issues = model_utils.translate_model_genes( - model=model, - mapping_df=mapping_df, - target_nomenclature='ensg', - source_nomenclature='hgnc_symbol', - allow_many_to_one=True - ) - print(" ✓ Translation completed successfully") - except Exception as e: - print(f" ❌ Translation failed: {e}") - import traceback - traceback.print_exc() - return {'success': False, 'error': str(e)} - - # Display translated GPRs - print("\n6. Translated model GPRs:") - for rxn in sorted(translated_model.reactions, key=lambda x: x.id): - has_issues = "🚨" if rxn.id in translation_issues else "✓" - print(f" {has_issues} {rxn.id:20} : {rxn.gene_reaction_rule}") - - # Analyze translation issues - print("\n7. Translation issues analysis:") - if translation_issues: - for rxn_id, issues_str in sorted(translation_issues.items()): - predicted = predicted_issues.get(rxn_id, {}) - prediction_status = "✓ PREDICTED" if rxn_id in predicted_issues else "❓ UNEXPECTED" - print(f" 🚨 {rxn_id:20} ({prediction_status})") - # Split issues string by semicolon separator - if issues_str: - issues_list = [issue.strip() for issue in issues_str.split(';') if issue.strip()] - for issue in issues_list: - print(f" - {issue}") - else: - print(f" - No specific issues reported") - else: - print(" ✅ No translation issues detected") - - # Compare predictions vs actual - print("\n8. Prediction accuracy:") - true_positive = set(predicted_issues.keys()) & set(translation_issues.keys()) - false_positive = set(predicted_issues.keys()) - set(translation_issues.keys()) - false_negative = set(translation_issues.keys()) - set(predicted_issues.keys()) - - print(f" ✓ Correctly predicted issues: {len(true_positive)}") - print(f" ⚠ False positives: {len(false_positive)}") - print(f" ❌ False negatives: {len(false_negative)}") - - if false_positive: - print(" False positive reactions:") - for rxn_id in false_positive: - print(f" - {rxn_id}") - - if false_negative: - print(" False negative reactions:") - for rxn_id in false_negative: - print(f" - {rxn_id}") - - # Test specific functionality - print("\n9. Testing OR-only GPR flattening...") - flattening_tests = self.test_or_only_flattening(translated_model, translation_issues) - - # Summary statistics - print("\n10. Summary:") - results = { - 'success': True, - 'model_reactions': len(model.reactions), - 'mapping_entries': len(mapping_df), - 'predicted_issues': len(predicted_issues), - 'actual_issues': len(translation_issues), - 'prediction_accuracy': { - 'true_positive': len(true_positive), - 'false_positive': len(false_positive), - 'false_negative': len(false_negative), - 'precision': len(true_positive) / len(predicted_issues) if predicted_issues else 0, - 'recall': len(true_positive) / len(translation_issues) if translation_issues else 0, - }, - 'mapping_stats': mapping_stats, - 'flattening_tests': flattening_tests, - 'models': { - 'original': model, - 'translated': translated_model - }, - 'issues': { - 'predicted': predicted_issues, - 'actual': translation_issues - } - } - - precision = results['prediction_accuracy']['precision'] - recall = results['prediction_accuracy']['recall'] - f1 = 2 * precision * recall / (precision + recall) if (precision + recall) > 0 else 0 - - print(f" 📊 Total reactions: {len(model.reactions)}") - print(f" 📊 Reactions with issues: {len(translation_issues)}") - print(f" 📊 Prediction precision: {precision:.2%}") - print(f" 📊 Prediction recall: {recall:.2%}") - print(f" 📊 Prediction F1-score: {f1:.2%}") - print(f" 📊 OR-only flattening tests: {flattening_tests['passed']}/{flattening_tests['total']}") - - print("\n" + "="*80) - print("TEST SUITE COMPLETED") - print("="*80) - - return results - - def test_or_only_flattening(self, model: cobra.Model, translation_issues: Dict) -> Dict: - """Test the OR-only GPR flattening functionality""" - test_cases = [ - # (original_gpr, expected_after_flattening, should_be_flattened) - ("SHARED_TARGET1 or SHARED_TARGET1", "SHARED_TARGET1", True), - ("(SHARED_TARGET2 or SHARED_TARGET2) or SHARED_TARGET2", "SHARED_TARGET2", True), - ("TARGET1 or TARGET2 or TARGET1", "TARGET1 or TARGET2", True), - ("(TARGET1 or TARGET2) and TARGET3", "(TARGET1 or TARGET2) and TARGET3", False), # Contains AND - ("TARGET1 and TARGET1", "TARGET1", True), # Should simplify AND duplicates too - ] - - results = {'total': 0, 'passed': 0, 'failed': [], 'details': []} - - print(" Testing OR-only flattening functionality:") - - # Test the helper functions directly - for original, expected, should_flatten in test_cases: - results['total'] += 1 - - # Test _is_or_only_expression - is_or_only = model_utils._is_or_only_expression(original) - - # Test _flatten_or_only_gpr if it should be OR-only - if should_flatten and 'and' not in original.lower(): - flattened = model_utils._flatten_or_only_gpr(original) - passed = flattened == expected - else: - passed = not should_flatten or is_or_only == (not 'and' in original.lower()) - flattened = original - - status = "✓" if passed else "❌" - results['details'].append({ - 'original': original, - 'expected': expected, - 'flattened': flattened, - 'is_or_only': is_or_only, - 'should_flatten': should_flatten, - 'passed': passed - }) - - if passed: - results['passed'] += 1 - else: - results['failed'].append(f"{original} -> {flattened} (expected: {expected})") - - print(f" {status} '{original}' -> '{flattened}' (OR-only: {is_or_only})") - - # Test actual model reactions that should have been flattened - for rxn in model.reactions: - if rxn.id in translation_issues: - original_gpr = rxn.gene_reaction_rule - is_or_only = model_utils._is_or_only_expression(original_gpr) - if is_or_only: - print(f" 🔍 Real case: {rxn.id} has OR-only GPR: '{original_gpr}'") - - return results - -def run_individual_tests(): - """Run individual component tests""" - print("\n" + "="*80) - print("INDIVIDUAL COMPONENT TESTS") - print("="*80) - - # Test 1: OR-only detection - print("\n1. Testing OR-only detection...") - or_only_cases = [ - ("GENE1 or GENE2", True), - ("(GENE1 or GENE2)", True), - ("GENE1 or GENE2 or GENE3", True), - ("(GENE1 or GENE2) or GENE3", True), - ("((GENE1 or GENE2) or GENE3) or GENE4", True), - ("GENE1 and GENE2", False), - ("GENE1 or (GENE2 and GENE3)", False), - ("(GENE1 or GENE2) and GENE3", False), - ("GENE1", False), # Single gene - ("", False), # Empty - ] - - for gpr, expected in or_only_cases: - result = model_utils._is_or_only_expression(gpr) - status = "✓" if result == expected else "❌" - print(f" {status} '{gpr}' -> {result} (expected: {expected})") - - # Test 2: GPR flattening - print("\n2. Testing GPR flattening...") - flattening_cases = [ - ("GENE1 or GENE1", "GENE1"), - ("(GENE1 or GENE1) or GENE2", "GENE1 or GENE2"), - ("GENE1 or GENE2 or GENE1", "GENE1 or GENE2"), - ("(GENE1 or GENE2) or (GENE1 or GENE3)", "GENE1 or GENE2 or GENE3"), - ("((A or A) or B) or C", "A or B or C"), - ] - - for original, expected in flattening_cases: - result = model_utils._flatten_or_only_gpr(original) - status = "✓" if result == expected else "❌" - print(f" {status} '{original}' -> '{result}' (expected: '{expected}')") - -def main(): - """Main test function""" - print("COBRAxy GPR Translation Comprehensive Test Suite") - print("=" * 80) - - # Run individual component tests first - run_individual_tests() - - # Run comprehensive test suite - tester = GPRTranslationTester() - results = tester.run_comprehensive_test() - - # Save results for further analysis if needed - if results['success']: - print(f"\n✅ All tests completed successfully!") - print(f"📁 Test models and results available in results object") - - # Optionally save to file - try: - import pickle - with open('/tmp/gpr_translation_test_results.pkl', 'wb') as f: - pickle.dump(results, f) - print(f"📁 Detailed results saved to /tmp/gpr_translation_test_results.pkl") - except: - pass - else: - print(f"\n❌ Tests failed: {results.get('error', 'Unknown error')}") - return False - - return True - -if __name__ == "__main__": - success = main() - sys.exit(0 if success else 1) \ No newline at end of file
--- a/COBRAxy/utils/model_utils.py Tue Sep 30 17:18:55 2025 +0000 +++ b/COBRAxy/utils/model_utils.py Tue Sep 30 18:01:30 2025 +0000 @@ -240,11 +240,15 @@ for reaction in model.reactions: # Get unique pathways from all metabolites in the reaction - if type(reaction.annotation['pathways']) == list: - reaction_pathways[reaction.id] = reaction.annotation['pathways'] - max_pathways = max(max_pathways, len(reaction.annotation['pathways'])) + if 'pathways' in reaction.annotation: + if type(reaction.annotation['pathways']) == list: + reaction_pathways[reaction.id] = reaction.annotation['pathways'] + max_pathways = max(max_pathways, len(reaction.annotation['pathways'])) + else: + reaction_pathways[reaction.id] = [reaction.annotation['pathways']] else: - reaction_pathways[reaction.id] = [reaction.annotation['pathways']] + # No pathway annotation - use empty list + reaction_pathways[reaction.id] = [] # Create column names for pathways pathway_columns = [f"Pathway_{i+1}" for i in range(max_pathways)] @@ -617,7 +621,7 @@ # normalize temporary columns for grouping (without altering the original df) tmp = mapping_df[[source_col, target_col]].copy() - tmp['_src_norm'] = tmp[source_col].astype(str).map(_normalize_gene_id) + tmp['_src_norm'] = tmp[source_col].astype(str).apply(_normalize_gene_id) tmp['_tgt_norm'] = tmp[target_col].astype(str).str.strip() # optionally filter to the set of model source genes @@ -885,7 +889,7 @@ logger.info(f"Filtering mapping to {len(model_source_genes)} source genes present in model (normalized).") tmp_map = mapping_df[[col_for_src, col_for_tgt]].dropna().copy() - tmp_map[col_for_src + "_norm"] = tmp_map[col_for_src].astype(str).map(_normalize_gene_id) + tmp_map[col_for_src + "_norm"] = tmp_map[col_for_src].astype(str).apply(_normalize_gene_id) filtered_map = tmp_map[tmp_map[col_for_src + "_norm"].isin(model_source_genes)].copy() @@ -955,7 +959,7 @@ """ df = mapping_df[[source_col, target_col]].dropna().copy() # normalize to string - df[source_col] = df[source_col].astype(str).map(_normalize_gene_id) + df[source_col] = df[source_col].astype(str).apply(_normalize_gene_id) df[target_col] = df[target_col].astype(str).str.strip() df = df.drop_duplicates()