Package: scSimEval 0.99.7

1. Introduction | 2. Package Architecture and Workflow | Step 1: Input Real and Simulated Data | 2.1 Input Data Formats & Metadata Requirements (Cell Types & Batch Labels) | 2.2 Metric Measurement Capacity by Data & Metadata Mode | Step 2: Calculate Evaluation Measures | Step 3: Run the Complete Benchmark | Step 4: Review Scores, Rankings, and Figures | 3. Score Normalization and Visual Mapping Workflow | 3.1 Two-Step Score Normalization | 3.2 Visual Mapping to Bubble Matrices | 4. Installation and Getting Started | 5. Evaluating Data Properties and Distributions | 5.1 Comparative Distribution QC Plot (plot_distribution_qc) | 6. Evaluating Cell Clustering and Group Structure | 7. Evaluating Batch Effects and Technical Confounders | 8. Evaluating Cell Lineages and Developmental Trajectories | 9. Evaluating Biological Signals and Marker Genes (DEGs) | 10. Evaluating Computational Resource Usage (Scalability) | 11. Master Evaluation Pipeline: evaluate_multiomics_accuracy | 12. Visualization Suite | 12.1 Multi-Dimensional Benchmarking Bubble Matrix (plot_benchmark_bubble_matrix) | 12.2 Evaluation Summary Horizontal Bar Matrix (plot_evaluation_summary) | 12.2b Simulator Performance Leaderboard (compute_method_leaderboard) | 12.3 Metric Score Distributions Across Categories (plot_metric_boxplots) | 12.4 Comprehensive Benchmark Heatmap (plot_metric_heatmap) | 12.5 Dimension Reduction and Ordination Plots (PCA & MDS) | 12.6 Cell Embeddings (t-SNE, UMAP, & PCA) and Low-Dimensional Quality Metrics | 13. Multi-Dataset and Multi-Method Benchmarking | 14. Interactive Web Application (Shiny Studio) | 14.1 The 7 Modular Application Tabs | 15. Supported Multiomics Data Types and Metric Compatibility | 15.1 Three Experimental Designs | 15.2 Metric Compatibility by Data Type | 15.3 Recommended Functions by Data Type | 16. Large-Scale Multi-Dataset Benchmarking on HPC Clusters | 17. Extension to Other Single-Cell Data Types | 18. Summary | 19. Authors and Maintainers | 18. Session Information

Overview | Tab 1 — Home | Tab 2 — Data Hub | Subtab 1: Data Ingestion & Benchmark Studio | Mode 1 — Pre-Computed Benchmark (Demo Data) | Mode 2 — Single-Cell Evaluation (scRNA-seq / scATAC-seq) | Mode 3 — Multiomics Evaluation (scRNA-seq + scATAC-seq) | Metric Measurement Capacity by Upload Configuration | Mode 4 — Upload Pre-Computed Benchmark Table (.rds / .csv) | Subtab 2: View Uploaded Data & Matrices | Subtab 3: Dataset Properties Summary & HPC Cluster Execution | Tab 3 — Comparative Bubble Matrix & Leaderboard | Subtab 1: Comparative Bubble Matrix Figure | Subtab 2: Fidelity Leaderboard | Tab 4 — Visualizations | Tab 5 — Benchmark Results & Export | Interactive Master Benchmark Scores Table | Multi-Format Exports | Tab 6 — Help & Getting Started | Tab 7 — Contact | R API Cross-Reference

Overview of the Evaluation Architecture | Summary Table of the 8 Categories | Complete Master Reference Table (62 Metrics) | Category 1: Distribution Properties (14 Metrics) | 1. Kolmogorov-Smirnov (KS) Distance (KS) | 2. Wasserstein Metric (Earth Mover's Distance 1D) (Wasserstein) | 3. Median Absolute Deviation (MAD) | 4. Mean Absolute Error (MAE) | 5. Root Mean Squared Error (RMSE) | 6. Density Curve Overlap (OV) | 7. Bhattacharyya Distance (Bhattacharyya) | 8. Empirical Cumulative Distribution Area Difference (ECDF_DiffArea) | 9. Wald-Wolfowitz Runs Test Statistic (Runs_Statistic) | 10. 2D Fasano-Franceschini Test Statistic (Fasano_Franceschini_2D_KS) | 11. 2D Peacock Bivariate Test Statistic (Peacock_2D_KS) | 12. 2D Bivariate Kernel Density Estimation z-Statistic (KDE_Bivariate_zstat) | 13. Maximum Mean Discrepancy (MMD) | 14. Fréchet Single-Cell Distance (Frechet_SingleCell_Distance) | Category 2: Correlations and Zero Patterns (6 Metrics) | 15. Biological Coefficient of Variation Distance (bcv_discrepancy) | 16. Logistic Dropout Curve Midpoint Error (dropout_curve_midpoint_x0) | 17. Logistic Dropout Curve Slope Error (dropout_curve_slope_k) | 18. Mean-Variance Fit / Dropout Goodness-of-Fit (mean_variance_r2) | 19. Cell-Cell Pearson Correlation Distance (cell_pearson_cor) | 20. Feature/Gene-Gene Pearson Correlation Distance (feature_pearson_cor) | Category 3: Cell Structure and Clustering (10 Metrics) | 21. Average Silhouette Width Discrepancy (silhouette_sim) | 22. Dunn Index Discrepancy (dunn_sim) | 23. Davies-Bouldin Index Discrepancy (davies_bouldin_sim) | 24. Calinski-Harabasz Index Discrepancy (calinski_harabasz_sim) | 25. Adjusted Rand Index Concordance (ari) | 26. Normalized Mutual Information (nmi) | 27. Adjusted Mutual Information (ami) | 28. $V$-Measure (v_measure) | 29. Neighborhood Purity (neighborhood_purity) | 30. Generative Precision / Hungarian Optimal Matching (Generative_Precision) | Category 4: Batch Effects and Technical Confounders (7 Metrics) | 31. Batch Silhouette ASW (batch_silhouette) | 32. Batch Shannon Entropy (shannon_entropy) | 33. Principal Component Regression Batch Variance (pcr_r2) | 34. CellMixS Mixing Score (cms) | 35. Local Inverse Simpson Index (isi) | 36. Seurat Mixing Metric (seurat_mixing_metric) | 37. Local Density Factor Discrepancy (ldf_diff) | Category 5: Marker Genes and Biological Signals (15 Metrics) | 38. Symmetric Mean Absolute Percentage Error (SimBench_SMAPE) | 39. SimBench Composite DE Fidelity Score (SimBench_DE_Fidelity_Score) | 40. Log2 Fold-Change Pearson Correlation (Log2FC_Pearson_Corr) | 41. Log2 Fold-Change Spearman Correlation (Log2FC_Spearman_Corr) | 42. Top DEG Jaccard Overlap (Top_DEG_Jaccard_Overlap) | 43. DEG Detection Ratio (DEG_Ratio) | 44. Uniform P-Value Distribution Discrepancy (PValue_Uniformity_Chisq) | 45. Null P-Value Distribution Preservation Score (Distribution_Score) | 46. Machine Learning Cell Type Classification Accuracy (Classifier_Accuracy) | 47. Macro-Averaged F1 Score (Classifier_Macro_F1) | 48. Macro-Averaged Recall Score (Classifier_Macro_Recall) | 49. Shaky Foundations Group Silhouette Similarity (Silhouette_Sim) | 50. Shaky Foundations Silhouette Discrepancy (Silhouette_Discrepancy) | 51. Shaky Foundations Group PVE Similarity (PVE_Group_Sim) | 52. Shaky Foundations PVE Discrepancy (PVE_Discrepancy) | Category 6: Trajectory & Lineage Dynamics (2 Metrics) | 53. Pseudotime Rank Correlation (pseudotime_correlation) | 54. Lineage Tree Height Discrepancy (tree_height_rmse) | Category 7: Cross-Modal Coupling & Modularity (6 Metrics) | 55. Cross-Modal Cell Type Transfer Accuracy (cross_modal_accuracy) | 56. Cross-Modal Cell Type Transfer Macro-F1 Score (cross_modal_F1) | 57. Fraction of Samples Closer Than The True Match (FOSCTTM) | 58. Top-1 Cross-Modal Match Rate (match_at_1) | 59. Regulatory Coupling RV Coefficient (rv_coefficient) | 60. Cross-Modal Module Correlation Fidelity (module_correlation_r) | Category 8: Computational Scalability (2 Metrics) | 61. Wall-Clock Execution Time (elapsed_time_seconds) | 62. Peak Memory Allocation (peak_memory_mb) | Automated Score Normalization Pipeline

Introduction | Part 1: Understanding Multiomics Data Types | 1.1 Paired Multiomics (Co-assay) | 1.2 Unpaired Multiomics (Independent profiling) | 1.3 Mosaic Multiomics (Partial co-measurement) | 1.4 Metric Compatibility Summary | Part 2: Evaluating Paired Multiomics | 2.1 Loading the Example Paired Dataset | 2.2 Alignment in Joint Embedding Space (FOSCTTM & Match@1) | 2.3 Cross-Modal Cell Type Label Transfer | 2.4 Master Paired Pipeline: evaluate_multiomics_accuracy() | Part 3: Evaluating Unpaired Multiomics | 3.1 Step-by-Step Workflow for Unpaired Data | Part 4: Evaluating Mosaic Multiomics | 4.1 Step-by-Step Workflow for Mosaic Data | Part 5: Visualization | 5.1 Low-Dimensional Multiomics Cell Embeddings | Part 6: Summary

Introduction | 1. Loading the Package and Example Dataset | 2. Step-by-Step Category Evaluation | 2.1 Distribution Properties & Summary Measures (Categories 1 and 2) | 2.2 Cell Clustering and Concordance (Category 3) | 2.3 Batch Effects and Confounder Mixing (Category 4) | 2.4 Differential Expression (DEG) Fidelity (Category 5) | 2.5 Trajectory and Lineage Dynamics (Category 6) | 3. Master Unimodal Pipeline: evaluate_simulation_accuracy | 4. Diagnostic Visualizations | 5. Conclusion

Introduction | 1. Loading the Packaged scATAC-seq Dataset | 2. Epigenomic Distribution & Summary Properties | 3. Epigenomic Cluster & Cell-Type Concordance | 4. Epigenomic Batch Integration & Confounder Handling | 5. Chromatin Peak Co-Accessibility & Regulatory Coupling | 6. Master Unimodal scATAC-seq Pipeline | 7. Conclusion