""" TWAS Results Export and Reporting This module provides functions for generating comprehensive target reports with genetic evidence, directionality, causality, and druggability. """ import pandas as pd from pathlib import Path def generate_target_report(twas_results, coloc_results, therapeutic_df, mr_results=None, druggability_scores=None, output_file="reports/target_report.xlsx"): """ Generate comprehensive Excel report for biopharma target prioritization. Parameters ---------- twas_results : pandas.DataFrame Full TWAS results coloc_results : pandas.DataFrame Colocalization results therapeutic_df : pandas.DataFrame Therapeutic directionality recommendations mr_results : pandas.DataFrame, optional Mendelian Randomization results druggability_scores : pandas.DataFrame, optional Druggability assessment output_file : str Output Excel file path Returns ------- str Path to generated report """ output_path = Path(output_file) output_path.parent.mkdir(parents=True, exist_ok=True) print(f"Generating comprehensive target report...") # Create Excel writer with pd.ExcelWriter(output_file, engine='openpyxl') as writer: # Sheet 1: Executive Summary executive_summary = create_executive_summary( therapeutic_df, coloc_results, druggability_scores, mr_results ) executive_summary.to_excel(writer, sheet_name='Executive Summary', index=False) print(" ✓ Executive Summary") # Sheet 2: Full TWAS Results twas_results.to_excel(writer, sheet_name='TWAS Results', index=False) print(" ✓ TWAS Results") # Sheet 3: Colocalization Details coloc_results.to_excel(writer, sheet_name='Colocalization', index=False) print(" ✓ Colocalization") # Sheet 4: Therapeutic Directionality therapeutic_df.to_excel(writer, sheet_name='Directionality', index=False) print(" ✓ Therapeutic Directionality") # Sheet 5: Mendelian Randomization (if available) if mr_results is not None and not mr_results.empty: mr_results.to_excel(writer, sheet_name='Mendelian Randomization', index=False) print(" ✓ Mendelian Randomization") # Sheet 6: Druggability Assessment (if available) if druggability_scores is not None and not druggability_scores.empty: druggability_scores.to_excel(writer, sheet_name='Druggability', index=False) print(" ✓ Druggability") print(f"\nReport saved: {output_file}") return str(output_file) def create_executive_summary(therapeutic_df, coloc_results, druggability_scores, mr_results): """ Create executive summary sheet with top 20 prioritized targets. Parameters ---------- therapeutic_df : pandas.DataFrame Therapeutic directionality coloc_results : pandas.DataFrame Colocalization results druggability_scores : pandas.DataFrame or None Druggability scores mr_results : pandas.DataFrame or None MR results Returns ------- pandas.DataFrame Executive summary table """ # Merge all data summary = therapeutic_df.copy() # Add colocalization if not coloc_results.empty: coloc_subset = coloc_results[['GENE', 'PP.H4']].rename(columns={'PP.H4': 'coloc_pp4'}) summary = summary.merge(coloc_subset, left_on='gene', right_on='GENE', how='left') summary = summary.drop('GENE', axis=1, errors='ignore') # Add druggability if druggability_scores is not None and not druggability_scores.empty: summary = summary.merge( druggability_scores[['gene', 'druggability_score', 'druggability_tier', 'known_drugs']], on='gene', how='left' ) # Add MR evidence if mr_results is not None and not mr_results.empty: mr_subset = mr_results[['gene', 'ivw_beta', 'ivw_pval']].rename( columns={'ivw_beta': 'mr_effect', 'ivw_pval': 'mr_pval'} ) summary = summary.merge(mr_subset, on='gene', how='left') # Calculate priority score summary['priority_score'] = calculate_priority_score(summary) # Sort by priority and take top 20 summary = summary.sort_values('priority_score', ascending=False).head(20) # Reorder columns column_order = [ 'gene', 'priority_score', 'therapeutic_strategy', 'twas_pval', 'coloc_pp4', 'confidence', 'druggability_tier', 'known_drugs' ] # Include only columns that exist column_order = [col for col in column_order if col in summary.columns] summary = summary[column_order] return summary def calculate_priority_score(summary_df): """ Calculate composite priority score for target ranking. Scoring components: - TWAS significance: 30% - Colocalization: 30% - Druggability: 25% - MR causal evidence: 15% Parameters ---------- summary_df : pandas.DataFrame Summary dataframe with evidence columns Returns ------- pandas.Series Priority scores (0-100) """ scores = pd.Series(0.0, index=summary_df.index) # TWAS significance (30 points) if 'twas_pval' in summary_df.columns: twas_score = -pd.Series(summary_df['twas_pval']).apply(lambda x: min(-np.log10(x) / 10, 1)) scores += twas_score * 30 # Colocalization (30 points) if 'coloc_pp4' in summary_df.columns: scores += summary_df['coloc_pp4'].fillna(0) * 30 # Druggability (25 points) if 'druggability_score' in summary_df.columns: scores += summary_df['druggability_score'].fillna(0.1) * 25 # MR causal evidence (15 points) if 'mr_pval' in summary_df.columns: mr_score = summary_df['mr_pval'].apply(lambda x: 1 if pd.notna(x) and x < 0.05 else 0) scores += mr_score * 15 return scores def export_csv_summary(twas_results, output_file="results/twas_summary.csv"): """ Export simplified CSV summary of TWAS results. Parameters ---------- twas_results : pandas.DataFrame TWAS results output_file : str Output CSV file Returns ------- str Path to CSV file """ # Select key columns summary_cols = ['GENE', 'CHR', 'TISSUE', 'TWAS.Z', 'TWAS.P'] summary_cols = [col for col in summary_cols if col in twas_results.columns] summary = twas_results[summary_cols].copy() # Add significance flag summary['Significant'] = summary['TWAS.P'] < (0.05 / len(twas_results)) # Save summary.to_csv(output_file, index=False) print(f"CSV summary saved: {output_file}") return output_file # Import numpy for priority score calculation import numpy as np