{ "_id": "6acd721da14063b82c3ab9c0", "Package": "scSimEval", "Type": "Package", "Title": "Unified Benchmarking and Accuracy Evaluation for Single-Cell\nMultiomics Simulation Methods", "Version": "0.99.7", "Authors@R": "c(\nperson(\"Kabilan\", \"Sakthivel\", role = c(\"aut\", \"cre\"), email = \"kabilan151414@gmail.com\"),\nperson(\"Dr\", \"Dwijesh Chandra Mishra\", role = c(\"aut\", \"ths\"), email = \"dwij.mishra@gmail.com\"),\nperson(\"Dr\", \"Shashi Bhushan Lal\", role = c(\"aut\"), email = \"sblall16@gmail.com\"),\nperson(\"Dr\", \"Sudhir Srivastava\", role = c(\"aut\"), email = \"sudhir0401bm@gmail.com\"),\nperson(\"Dr\", \"Krishna Kumar Chaturvedi\", role = c(\"aut\"), email = \"kkcchaturvedi@gmail.com\"),\nperson(\"Dr\", \"Sharanbasappa\", role = c(\"ctb\"), email = \"smadival509@gmail.com\")\n)", "Description": "A simple and unified evaluation toolkit for benchmarking\nsingle-cell and multiomics data simulation methods. Evaluates\nhow realistic simulated data are compared to real biological\ndatasets across three multiomics experimental designs: (i)\npaired multiomics (simultaneous co-assay from identical cells,\ne.g. 10x Chromium Multiome, SHARE-seq, SNARE-seq), which\nunlocks the full Category 7 cross-modal coupling suite\nincluding FOSCTTM alignment scores and peak-to-gene regulatory\nlinkage; (ii) unpaired multiomics (independent scRNA-seq and\nscATAC-seq profiled from separate cells of the same tissue),\nsupported through per-modality unimodal evaluation (Categories\n1-6, 8) and population-level cross-modal metrics such as\nco-expression module fidelity and network Jaccard similarity;\nand (iii) mosaic multiomics (partial co-measurement where only\na subset of cells has both modalities, e.g. DOGMA-seq or mosaic\npooling designs), handled by subsetting the paired cell\nfraction for coupling metrics and evaluating each modality\nindependently otherwise. The toolkit quantifies simulation\nrealism using 62 metrics organized into 8 easy-to-understand\ncategories: (1) Distribution Differences: Statistical distances\nbetween real and simulated values (such as KS, Wasserstein,\nMAD, and RMSE), (2) Summary Properties: Gene correlations, zero\ncounts, and dropout patterns, (3) Cell Structure and\nClustering: How well distinct cell types and clusters are\npreserved (such as Silhouette width, ARI, and NMI), (4) Batch\nand Confounder Mixing: How well technical batch variations are\ncaptured or mixed, (5) Biological Signals and Marker Genes:\nPreservation of differentially expressed genes (DEGs) across\nSimpipe, SimBench, and Shaky Foundations frameworks, (6) Cell\nLineage and Trajectories: Preservation of continuous\ndevelopmental pathways (inferred directly from scRNA-seq\ncounts), (7) Multiomics Relationships: Connections between\ndifferent layers (such as gene expression and chromatin\naccessibility) — fully active for paired data; population-level\nmetrics available for unpaired and mosaic data, (8)\nComputational Resources: Speed (runtime in seconds) and memory\nusage (peak RAM in MiB). Includes non-linear low-dimensional\ncell embedding projections (UMAP, t-SNE, PCA) with quantitative\ncluster separability (Silhouette) and fidelity (ARI) metrics,\nautomated score normalization, leaderboards, 600 DPI bubble\nplots, and an interactive Shiny app studio.", "License": "MIT + file LICENSE", "URL": "https://kabilanbio.github.io/scSimEval/,\nhttps://github.com/kabilanbio/scSimEval", "BugReports": "https://github.com/kabilanbio/scSimEval/issues", "biocViews": "SingleCell, Transcriptomics, ATACSeq, QualityControl,\nDimensionReduction, DifferentialExpression, GeneExpression,\nNormalization, Software", "Encoding": "UTF-8", "LazyData": "false", "VignetteBuilder": "knitr", "Config/roxygen2/version": "8.1.0", "Config/pak/sysreqs": "zlib1g-dev", "Repository": "https://biocstaging.r-universe.dev", "Date/Publication": "2026-10-12 17:54:42 UTC", "RemoteUrl": "https://github.com/BiocStaging/scSimEval", "RemoteRef": "HEAD", "RemoteSha": "0bbc61086f3ed67561145b594b5bc36941ac0bfd", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-12 23:16:00 UTC", "User": "root" }, "Author": "Kabilan Sakthivel [aut, cre],\nDr Dwijesh Chandra Mishra [aut, ths],\nDr Shashi Bhushan Lal [aut],\nDr Sudhir Srivastava [aut],\nDr Krishna Kumar Chaturvedi [aut],\nDr Sharanbasappa [ctb]", "Maintainer": "Kabilan Sakthivel ", "_user": "biocstaging", "_type": "src", "_file": "scSimEval_0.99.7.tar.gz", "_fileid": "https://r2.ropensci.org/8b10e56529f8cbe6c7785506a5143968db784059c4ecdc9e25eb56d09f7bcc08", "_filesize": 6942897, "_sha256": "8b10e56529f8cbe6c7785506a5143968db784059c4ecdc9e25eb56d09f7bcc08", "_expires": "2027-01-20T23:49:46.000Z", "_created": "2026-10-12T23:16:00.000Z", "_published": "2026-10-13T03:42:39.638Z", "_buildurl": "https://github.com/r-universe/biocstaging/actions/runs/38320659268", "_status": "success", "_host": "GitHub-Actions", "_bioccheck": { "error": 0, "warning": 0, "note": 8 }, "_upstream": "https://github.com/BiocStaging/scSimEval", "_commit": { "id": 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"calc_accessibility_sparsity_curve" ] }, { "page": "calc_all_bivariate_metrics", "title": "Compute All Bivariate (2D) Distance & Accuracy Metrics for Joint Distributions", "topics": [ "calc_all_bivariate_metrics" ] }, { "page": "calc_all_univariate_metrics", "title": "Compute All Univariate Distance & Accuracy Metrics for a Feature", "topics": [ "calc_all_univariate_metrics" ] }, { "page": "calc_ami", "title": "Calculate Adjusted Mutual Information (AMI)", "topics": [ "calc_ami" ] }, { "page": "calc_ari", "title": "Calculate Adjusted Rand Index (ARI)", "topics": [ "calc_ari" ] }, { "page": "calc_atac_rna_coupling", "title": "Chromatin Accessibility to RNA Regulatory Effect Coupling", "topics": [ "calc_atac_rna_coupling" ] }, { "page": "calc_between_dataset_silhouette", "title": "Calculate Between-Dataset Silhouette Width", "topics": [ "calc_between_dataset_silhouette" ] }, { "page": "calc_bhattacharyya", "title": "Calculate Bhattacharyya Distance (BH)", "topics": [ "calc_bhattacharyya" ] }, { "page": "calc_calinski_harabasz", "title": "Calculate Calinski-Harabasz Index (CH)", "topics": [ "calc_calinski_harabasz" ] }, { "page": "calc_cdi", "title": "Compute Clustering Deviation Index (CDI) for Single-Cell Clustering Evaluation", "topics": [ "calc_cdi" ] }, { "page": "calc_clustering_accuracy", "title": "Calculate Clustering Accuracy (ACC)", "topics": [ "calc_clustering_accuracy" ] }, { "page": "calc_cms", "title": "Cell-Specific Mixing Score (CMS)", "topics": [ "calc_cms" ] }, { "page": "calc_coexpression_module_fidelity", "title": "Evaluate Gene Co-Expression Module Fidelity (ESCO)", "topics": [ "calc_coexpression_module_fidelity" ] }, { "page": "calc_completeness", "title": "Calculate Completeness Score", "topics": [ "calc_completeness" ] }, { "page": "calc_connectivity", "title": "Calculate Cluster Connectivity", "topics": [ "calc_connectivity" ] }, { "page": "calc_coregulation_fidelity", "title": "Multi-Omics Co-Regulation & Modularity Fidelity", "topics": [ "calc_coregulation_fidelity" ] }, { "page": "calc_cross_modal_generation", "title": "Cross-Modality In Silico Generation & Translation Fidelity", "topics": [ "calc_cross_modal_generation" ] }, { "page": "calc_cross_modality_correlation", "title": "Calculate Cross-Modality Correlation Fidelity", "topics": [ "calc_cross_modality_correlation" ] }, { "page": "calc_davies_bouldin", "title": "Calculate Davies-Bouldin Index (DB)", "topics": [ "calc_davies_bouldin" ] }, { "page": "calc_deconvolution_accuracy", "title": "Evaluate Cell-Type Deconvolution & Mixture Proportion Accuracy", "topics": [ "calc_deconvolution_accuracy" ] }, { "page": "calc_delta_variance", "title": "Calculate Delta Variance (Pseudoreplication Bias Metric)", "topics": [ "calc_delta_variance" ] }, { "page": "calc_dunn", "title": "Calculate Dunn Index", "topics": [ "calc_dunn" ] }, { "page": "calc_ecdf_diffarea", "title": "Calculate Area Between Empirical Cumulative Distribution Functions (eCDFs)", "topics": [ "calc_ecdf_diffarea" ] }, { "page": "calc_emd_2d", "title": "Calculate 2D Earth Mover's Distance (2D EMD)", "topics": [ "calc_emd_2d" ] }, { "page": "calc_excess_zero_weights", "title": "Calculate Posterior Excess Zero Weights (zingeR & ZINB-WaVE)", "topics": [ "calc_excess_zero_weights" ] }, { "page": "calc_fasano_franceschini", "title": "Calculate Fasano-Franceschini 2D Kolmogorov-Smirnov Test Statistic", "topics": [ "calc_fasano_franceschini" ] }, { "page": "calc_fmi", "title": "Calculate Fowlkes-Mallows Index (FMI)", "topics": [ "calc_fmi" ] }, { "page": "calc_foscttm", "title": "Fraction of Samples Closer Than The True Match (FOSCTTM)", "topics": [ "calc_foscttm" ] }, { "page": "calc_frechet_singlecell_distance", "title": "Frechet Single-Cell Distance (FSD)", "topics": [ "calc_frechet_singlecell_distance" ] }, { "page": "calc_generative_precision_recall", "title": "Generative Precision and Recall for Single-Cell Manifolds", "topics": [ "calc_generative_precision_recall" ] }, { "page": "calc_homogeneity", "title": "Calculate Homogeneity Score", "topics": [ "calc_homogeneity" ] }, { "page": "calc_homogeneity_completeness_v_measure", "title": "Calculate Homogeneity, Completeness, and V-Measure", "topics": [ "calc_homogeneity_completeness_v_measure" ] }, { "page": "calc_intraclass_correlation", "title": "Calculate Intraclass Correlation Coefficient (ICC) (hierarchicell & rescueSim)", "topics": [ "calc_intraclass_correlation" ] }, { "page": "calc_isi", "title": "Inverse Simpson Index (ISI) for Batch Mixing", "topics": [ "calc_isi" ] }, { "page": "calc_kde_test", "title": "Calculate 2D Bivariate Kernel Density Estimation (KDE) Test Statistic", "topics": [ "calc_kde_test" ] }, { "page": "calc_kinetic_noise_decomposition", "title": "Kinetic Noise Decomposition (SymSim & Elowitz et al.)", "topics": [ "calc_kinetic_noise_decomposition" ] }, { "page": "calc_ks", "title": "Calculate Kolmogorov-Smirnov Distance (1D KS)", "topics": [ "calc_ks" ] }, { "page": "calc_ldf", "title": "Local Density Factor (LDF)", "topics": [ "calc_ldf" ] }, { "page": "calc_ldf_diff", "title": "Local Density Differences (ldfDiff)", "topics": [ "calc_ldf_diff" ] }, { "page": "calc_likelihood_ratio_test", "title": "Perform Likelihood Ratio Test for Comparing Nested Single-Cell Simulation Models", "topics": [ "calc_likelihood_ratio_test" ] }, { "page": "calc_local_structure_metric", "title": "Local Structure Preservation Metric", "topics": [ "calc_local_structure_metric" ] }, { "page": "calc_mad", "title": "Calculate Median Absolute Deviation (MAD)", "topics": [ "calc_mad" ] }, { "page": "calc_mae", "title": "Calculate Mean Absolute Error (MAE)", "topics": [ "calc_mae" ] }, { "page": "calc_marginal_aic_bic", "title": "Marginal Model Goodness of Fit and Information Criteria for Single-Cell Simulators", "topics": [ "calc_marginal_aic_bic" ] }, { "page": "calc_mmd", "title": "Maximum Mean Discrepancy (MMD) with Gaussian RBF Kernel", "topics": [ "calc_mmd" ] }, { "page": "calc_modality_alignment", "title": "Modality Alignment & Omics Layer Mixing in Joint Latent Space", "topics": [ "calc_modality_alignment" ] }, { "page": "calc_model_aic_bic", "title": "Compute Model Information Criteria (AIC and BIC)", "topics": [ "calc_model_aic_bic" ] }, { "page": "calc_neighborhood_purity", "title": "Calculate Neighborhood Purity", "topics": [ "calc_neighborhood_purity" ] }, { "page": "calc_network_jaccard", "title": "Jaccard Similarity of Predicted Regulatory Networks / Links", "topics": [ "calc_network_jaccard" ] }, { "page": "calc_nmi", "title": "Calculate Normalized Mutual Information (NMI)", "topics": [ "calc_nmi" ] }, { "page": "calc_nn_mismatch", "title": "Calculate Nearest-Neighbor Label Mismatch Proportion", "topics": [ "calc_nn_mismatch" ] }, { "page": "calc_outlier_proportion", "title": "Calculate Proportion of Outliers in a Numeric Vector", "topics": [ "calc_outlier_proportion" ] }, { "page": "calc_overlap", "title": "Calculate Distribution Overlapping Index (OV)", "topics": [ "calc_overlap" ] }, { "page": "calc_peacock_2d", "title": "Calculate Peacock 2D Kolmogorov-Smirnov Test Statistic", "topics": [ "calc_peacock_2d" ] }, { "page": "calc_peak_coaccessibility_fidelity", "title": "Evaluate Single-Cell ATAC Peak Co-Accessibility Fidelity (SCRIP)", "topics": [ "calc_peak_coaccessibility_fidelity" ] }, { "page": "calc_pseudotime_correlation", "title": "Calculate Correlation of Geodesic Pseudotime Distances", "topics": [ "calc_pseudotime_correlation" ] }, { "page": "calc_rmse", "title": "Calculate Root Mean Squared Error (RMSE)", "topics": [ "calc_rmse" ] }, { "page": "calc_runs_test", "title": "Calculate Wald-Wolfowitz Runs Test on Pooled Distributions", "topics": [ "calc_runs_test" ] }, { "page": "calc_seurat_mixing_metric", "title": "Seurat Mixing Metric", "topics": [ "calc_seurat_mixing_metric" ] }, { "page": "calc_signal_bd", "title": "Detect Bimodal Distribution (BD) Genes (Bimodal Separation Index)", "topics": [ "calc_signal_bd" ] }, { "page": "calc_signal_dd", "title": "Detect Differential Distribution (DD) Genes (Kolmogorov-Smirnov Test)", "topics": [ "calc_signal_dd" ] }, { "page": "calc_signal_de", "title": "Detect Differential Expression (DE) Genes (Mean Shift via Limma or T-Test)", "topics": [ "calc_signal_de" ] }, { "page": "calc_signal_dp", "title": "Detect Differential Proportion / Zero-Inflation (DP) Genes (Chisq Test)", "topics": [ "calc_signal_dp" ] }, { "page": "calc_signal_dv", "title": "Detect Differential Variability (DV) Genes (Bartlett Test)", "topics": [ "calc_signal_dv" ] }, { "page": "calc_silhouette", "title": "Calculate Average Silhouette Width (ASW)", "topics": [ "calc_silhouette" ] }, { "page": "calc_tree_height_discrepancy", "title": "Calculate Lineage Tree Branch Height Discrepancy (RMSE)", "topics": [ "calc_tree_height_discrepancy" ] }, { "page": "calc_v_measure", "title": "Calculate V-Measure", "topics": [ "calc_v_measure" ] }, { "page": "calc_variance_decomposition", "title": "Variance Component Decomposition (BASiCS & muscat)", "topics": [ "calc_variance_decomposition" ] }, { "page": "calc_wasserstein_1d", "title": "Calculate 1D Wasserstein Metric / Earth Mover's Distance (WS)", "topics": [ "calc_wasserstein_1d" ] }, { "page": "calc_zero_probability_curve", "title": "Fit Zero-Probability Dropout Curve (Splatter & ZINB-WaVE)", "topics": [ "calc_zero_probability_curve" ] }, { "page": "compute_dataset_embeddings", "title": "Compute Low-Dimensional Embeddings for Reference and Simulated Single-Cell Datasets", "topics": [ "compute_dataset_embeddings" ] }, { "page": "compute_embedding_quality_metrics", "title": "Compute Quantitative Quality Metrics for Low-Dimensional Cell Embeddings", "topics": [ "compute_embedding_quality_metrics" ] }, { "page": "compute_method_leaderboard", "title": "Compute Simulator Method Performance Leaderboard", "topics": [ "compute_method_leaderboard" ] }, { "page": "evaluate_accessibility_sparsity_curve", "title": "Evaluate Accessibility-Sparsity Curve Concordance (simATAC)", "topics": [ "evaluate_accessibility_sparsity_curve" ] }, { "page": "evaluate_batch_metrics", "title": "Calculate Batch Integration Metrics", "topics": [ "evaluate_batch_metrics" ] }, { "page": "evaluate_clustering_metrics", "title": "Full Clustering Performance Evaluation", "topics": [ "evaluate_clustering_metrics" ] }, { "page": "evaluate_cross_batch_prediction", "title": "Evaluate Cross-Batch Cell Classification Transfer Accuracy", "topics": [ "evaluate_cross_batch_prediction" ] }, { "page": "evaluate_cross_modal_prediction", "title": "Cross-Modal Cell-Type Label Transfer Accuracy", "topics": [ "evaluate_cross_modal_prediction" ] }, { "page": "evaluate_deg_fidelity", "title": "Evaluate Differentially Expressed Gene (DEG) Fidelity", "topics": [ "evaluate_deg_fidelity" ] }, { "page": "evaluate_epigenomic_annotation", "title": "Evaluate Epigenomic Supervised Cell-Type Annotation (EpiAnno / SCAN-ATAC-Sim)", "topics": [ "evaluate_epigenomic_annotation" ] }, { "page": "evaluate_multiomics_accuracy", "title": "Master Multiomics Simulation Benchmarking Pipeline", "topics": [ "evaluate_multiomics_accuracy" ] }, { "page": "evaluate_multiple_datasets", "title": "Evaluate Multiple Single-Cell Datasets Simultaneously", "topics": [ "evaluate_multiple_datasets" ] }, { "page": "evaluate_predictive_de_model", "title": "Predictive Cell Identity Classification Using DE / Top Variable Features", "topics": [ "evaluate_predictive_de_model" ] }, { "page": "evaluate_simbench_signals", "title": "Evaluate the 5 SimBench Biological Signal Proportions", "topics": [ "evaluate_simbench_signals" ] }, { "page": "evaluate_simulation_accuracy", "title": "Evaluate Comprehensive Simulation Accuracy (Unimodal Omics Layer)", "topics": [ "evaluate_simulation_accuracy" ] }, { "page": "evaluate_trajectory_metrics", "title": "Full Trajectory Accuracy Evaluation", "topics": [ "evaluate_trajectory_metrics" ] }, { "page": "evaluate_zero_probability_curve", "title": "Evaluate Zero-Probability Dropout Curve Fidelity (Splatter & ZINB-WaVE)", "topics": [ "evaluate_zero_probability_curve" ] }, { "page": "example_batch_info", "title": "Example Batch Annotations Vector", "topics": [ "example_batch_info" ] }, { "page": "example_cell_types", "title": "Example Cell-Type Annotations Vector", "topics": [ "example_cell_types" ] }, { "page": "example_multiomics", "title": "Synthetic Example Multiomics Benchmarking Dataset", "topics": [ "example_multiomics" ] }, { "page": "example_scatac", "title": "Synthetic Example scATAC-seq Benchmarking Dataset", "topics": [ "example_scatac" ] }, { "page": "example_scrna", "title": "Synthetic Example scRNA-seq Benchmarking Dataset", "topics": [ "example_scrna" ] }, { "page": "extract_cell_properties", "title": "Extract Comprehensive Cell-Level Properties", "topics": [ "extract_cell_properties" ] }, { "page": "extract_dataset_summary", "title": "Extract Dataset Dimensional Properties and Summary Statistics", "topics": [ "extract_dataset_summary" ] }, { "page": "extract_feature_properties", "title": "Extract Comprehensive Feature-Level (Gene / Peak) Properties", "topics": [ "extract_feature_properties" ] }, { "page": "fast_col_vars", "title": "Fast Column-Wise Variances for Dense or Sparse Single-Cell Matrices", "topics": [ "fast_col_vars" ] }, { "page": "fast_cross_distance_sq", "title": "Fast BLAS-Accelerated Cross-Distance Squared Matrix", "topics": [ "fast_cross_distance_sq" ] }, { "page": "fast_pca", "title": "Fast Truncated PCA on Top Highly Variable Features", "topics": [ "fast_pca" ] }, { "page": "fast_row_vars", "title": "Fast Row-Wise Variances for Dense or Sparse Single-Cell Matrices", "topics": [ "fast_row_vars" ] }, { "page": "fast_sparse_zero_fraction_cell", "title": "Fast Zero Fraction per Cell (Column-wise)", "topics": [ "fast_sparse_zero_fraction_cell" ] }, { "page": "fast_sparse_zero_fraction_feature", "title": "Fast Zero Fraction per Feature (Row-wise)", "topics": [ "fast_sparse_zero_fraction_feature" ] }, { "page": "infer_scrna_lineage_tree", "title": "Automatically Infer Lineage Tree (hclust) from scRNA-seq Counts & Cell Types", "topics": [ "infer_scrna_lineage_tree" ] }, { "page": "infer_scrna_pseudotime", "title": "Automatically Infer Pseudotime Trajectory from scRNA-seq Counts", "topics": [ "infer_scrna_pseudotime" ] }, { "page": "launch_scSimEval_app", "title": "Launch Interactive scSimEval Benchmarking Studio", "topics": [ "launch_scSimEval_app" ] }, { "page": "plot_benchmark_bubble_matrix", "title": "Plot Multi-Dimensional Benchmarking Bubble Matrix", "topics": [ "plot_benchmark_bubble_matrix", "plot_bubble_matrix" ] }, { "page": "plot_category_metric_bars", "title": "Plot Benchmark Metric Performance Barplots by Category", "topics": [ "plot_category_metric_bars" ] }, { "page": "plot_dataset_embeddings", "title": "Plot High-Dimensional Dataset Embeddings for Reference and Simulated Single-Cell Data", 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"topics": [ "plot_metric_mds" ] }, { "page": "plot_metric_pca", "title": "Principal Component Analysis (PCA) Dashboard of Benchmark Metrics and Methods", "topics": [ "plot_metric_pca" ] }, { "page": "plot_scalability_benchmark", "title": "Plot Multi-Dimensional Computational Scalability Benchmark", "topics": [ "plot_scalability_benchmark" ] }, { "page": "subsample_dist_vector", "title": "Subsample Large Vectors for Accelerated Distributional Distance Estimation", "topics": [ "subsample_dist_vector" ] } ], "_pkglogo": "https://github.com/BiocStaging/scSimEval/raw/HEAD/man/figures/logo.png", "_readme": "https://github.com/BiocStaging/scSimEval/raw/HEAD/README.md", "_rundeps": [ "abind", "Biobase", "BiocGenerics", "class", "cli", "cpp11", "DelayedArray", "farver", "generics", "GenomicRanges", "ggplot2", "glue", "gtable", "IRanges", "isoband", "labeling", "lattice", "lifecycle", "Matrix", "MatrixGenerics", "matrixStats", "R6", "RColorBrewer", "rlang", "S4Arrays", "S4Vectors", "S7", 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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. 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Wall-Clock Execution Time (elapsed_time_seconds)", "62. Peak Memory Allocation (peak_memory_mb)", "Automated Score Normalization Pipeline" ], "created": "2026-09-25 09:17:55", "modified": "2026-10-10 18:01:38", "commits": 5 }, { "source": "demo-scrna.Rmd", "filename": "demo-scrna.html", "title": "Demonstration 1: scRNA-seq Simulation Evaluation", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-09-25 09:17:55", "modified": "2026-09-27 19:41:47", "commits": 5 }, { "source": "demo-scatac.Rmd", "filename": "demo-scatac.html", "title": "Demonstration 2: scATAC-seq Simulation Evaluation", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-09-25 09:17:55", "modified": "2026-09-27 19:41:47", "commits": 5 }, { "source": "demo-multiomics.Rmd", "filename": "demo-multiomics.html", "title": "Demonstration 3: Multiomics (scRNA-seq + scATAC-seq) Simulation Evaluation", "engine": "knitr::rmarkdown", "headings": [ "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" ], "created": "2026-09-25 09:17:55", "modified": "2026-09-28 19:17:39", "commits": 6 }, { "source": "scSimEval-workflow.Rmd", "filename": "scSimEval-workflow.html", "title": "Getting Started with scSimEval: Unified Benchmarking for Single-Cell Multiomics Simulations", "author": "Kabilan S, Dr. Dwijesh Chandra Mishra, Dr. Shashi Bhushan Lal, Dr. Sudhir Srivastava, Dr. Krishna Kumar Chaturvedi, Dr. Sharanbasappa", "engine": "knitr::rmarkdown", "headings": [ "1. 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