Background | Study Overview | Dataset Description | Research Questions | Analysis Workflow | Step 1: Data Preparation | Step 2: Define Cell Populations | Step 3: Drug-Cell Connectivity Analysis | Step 4: Sensitive vs Resistant Classification | Step 5: Differential Connectivity Analysis | Key Findings | 1. Transcriptional State-Specific Drug Sensitivity | 2. Combination Therapy Candidates | 3. Patient-Level Heterogeneity | Biological Validation | Correlation with Known Resistance Markers | Conclusions | References | Session Info

Overview | 1. Drug-Cell Connectivity Score | Concept | Mathematical Formulation | Interpretation | 2. Fisher's Z-Transformation | Purpose | Properties | Variance Property | 3. Differential Connectivity Analysis | Linear Model Framework | Empirical Bayes Moderation | 4. Disease Signature Reversal Score | Algorithm | 5. MAST Differential Expression | Model Structure | Advantages for Single-Cell Data | 6. Computational Complexity | Time Complexity Analysis | Memory Requirements | Summary | References | Session Info

Introduction | Installation | From R-Universe (Recommended) | From GitHub | Dependencies | Launching scFOCAL | Workflow Overview | Step 1: Data Upload | Step 2: Pre-processing | Step 3: Disease Signature Generation | Step 4: Drug-Cell Connectivity Analysis | Step 5: Results Analysis | Built-in Data | Example Dataset | Next Steps | Citation | Session Info

Introduction | 1. Spearman Rank Correlation | Theoretical Foundation | Formula | Demonstration | 2. Fisher's Z-Transformation | Why Transform? | Statistical Properties | Standard Error | 3. limma Linear Models | Design Matrix Construction | Model Fitting | Empirical Bayes | 4. Multiple Testing Correction | The Problem | FDR Control | 5. Effect Size Interpretation | Log Fold Change in Z-space | Cohen's d Equivalent | 6. Power Analysis | Sample Size Considerations | Recommended Sample Sizes | 7. Quality Control Metrics | Gene Coverage | Connectivity Distribution QC | Summary Statistics Table | Best Practices | Session Info

Introduction | 1. Dimensional Reduction Plots | UMAP with Cell Type Annotations | UMAP with Drug Connectivity | 2. Violin Plots | Drug Connectivity by Cell Type | 3. Heatmaps | Disease Signature Heatmap | 4. Volcano Plots | Differential Connectivity Volcano | 5. Reversal Score Visualization | Bar Plot of Top Reversal Compounds | 6. Multi-Panel Publication Figure | Customization Tips | Color Palettes | Export Settings | Session Info

Introduction | Parallel Computing | Setup Parallel Backend | Parallel Consensus Clustering | Parallel Feature Selection | Cleanup | Data Preprocessing Pipeline | Complete Preprocessing Workflow | Handling Missing Values | Batch Effect Correction | Custom Analysis Pipelines | Multi-Algorithm Comparison | Optimal K Selection | Ensemble Clustering | Feature Selection | Bootstrap-Based Selection | MAD-Based Filtering | Classification | Train Subtype Classifier | Ensemble Classifier | Low-Level API Access | Direct Algorithm Functions | Factor Analysis Components | Integration with Bioconductor | Gene Set Analysis | Survival Analysis | Performance Tips | Memory Management | Algorithm Selection by Data Size | Parallelization Strategy | Troubleshooting | Common Issues | Debug Mode | Session Info

Overview | Algorithm Classification | Network-Based Methods | Similarity Network Fusion (SNF) | CIMLR (Cancer Integration via Multi-kernel Learning) | Matrix Factorization Methods | Integrative Non-negative Matrix Factorization (IntNMF) | LRAcluster (Low-Rank Approximation) | Factor Analysis Methods | Multi-Omics Factor Analysis (MOFA) | Multiple Co-Inertia Analysis (MCIA) | Canonical Correlation Methods | RGCCA (Regularized Generalized CCA) | SGCCA (Sparse GCCA) | Bayesian Methods | iClusterBayes | Bayesian Consensus Clustering (BCC) | Ensemble Methods | PINSPlus (Perturbation Clustering) | Algorithm Selection Guide | References | Session Info

Introduction | Simulation Framework | Algorithm Benchmark | Define Algorithms | Run Benchmark | Summary Statistics | Visualization | ARI Comparison | Runtime Comparison | Trade-off Analysis | Noise Sensitivity Analysis | Sample Size Scaling | Recommendations | Algorithm Selection Flowchart | Session Info

Introduction | Author | Installation | Quick Start | Generate Simulated Multi-Omics Data | Run SNF Clustering | UMAP Visualization | Compare Multiple Algorithms | Consensus Clustering | Data Preprocessing | Session Info

Introduction | Setup | UMAP Visualization | Basic UMAP Plot | Customized UMAP | UMAP Parameters | Consensus Matrix Heatmap | Generate Consensus Matrix | Basic Heatmap | Ordered by Clusters | Custom Colors | Cluster Quality Metrics | PAC (Proportion of Ambiguous Clustering) | Combined Metrics | Silhouette Analysis | Interpretation | Algorithm Comparison | ARI Matrix | Survival Analysis (Optional) | Base R vs ggplot2 | Check ggplot2 Availability | Consistent API | Saving Plots | With ggplot2 | With Base R | Color Palettes | Default Palette | Nature-Style Palettes | Summary | Session Info

Introduction | Parameter Tuning | Gamma Value (Metacell Aggregation) | Alpha Value (Learning Rate) | Hamming Threshold | Association Methods | Pearson Correlation (Default) | Spearman Correlation | Principal Component Regression (pcNet) | Comparison | Working with Seurat Objects | Filtering and Preprocessing | Filter Low-Expression Genes | Custom Gene Filtering | Parallel Processing | Output Analysis | Network Comparison | Differential Network Analysis | Best Practices | 1. Data Quality | 2. Prior Network Selection | 3. Parameter Selection | 4. Reproducibility | Session Information

Overview | The PANDA Algorithm | Core Concept | Mathematical Formulation | 1. Tanimoto Similarity | 2. Network Update Rules | Convergence Criterion | Metacell Aggregation | The Sparsity Problem | Solution: Metacells | Gamma Parameter | Network Normalization | Double Z-score Normalization | Computational Complexity | References | Session Information

Introduction | Running SCORPION | Network Statistics Visualization | Edge Weight Distribution | TF Targeting Statistics | Heatmap Visualization | Regulatory Network Heatmap | TF Cooperation Heatmap | Network Graph Visualization | Building igraph Network | Network Layout and Plotting | Subnetwork for Specific TF | Circular Network Plot | Network Centrality Analysis | Exporting Networks | Export to Cytoscape | Export to GraphML | Summary | Session Information

Introduction | Installation | Loading the Package | Example Data | Running SCORPION | Output Structure | Extracting Top Regulatory Edges | Visualizing Network Statistics | Session Information

Introduction | Problem Formulation | Non-Negative Matrix Factorization (NMF) | Projection Problem | Cell-wise Decomposition | NNLS Problem | Standard Form | KKT Conditions | Data Preprocessing | Scaling | Why Not Center? | Usage Normalization | Computational Complexity | Visualization of the Algorithm | Example: Geometric Interpretation | Summary | References | Session Info

Overview | The NNLS Problem | Active Set Method (Lawson-Hanson) | Algorithm Description | Convergence Properties | Implementation | Coordinate Descent Method | Advantages | Comparison | Benchmark | Choosing a Solver | Numerical Considerations | Tolerance Parameter | Maximum Iterations | Division by Zero Protection | Session Info

Introduction | Installation | Quick Example | Available References | Input Formats | Working with Results | Access Usage Matrix | Access Scores | Save Results | Demonstration with Simulated Data | Performance Tips | Next Steps | Session Info

Introduction | Reference Format | File Format | Method 1: From cNMF Results | Running cNMF | Loading cNMF Results | Method 2: Building Consensus Reference | Method 3: Manual Construction | From Known Gene Signatures | From Expression Data | Validating Custom References | Quality Checks | Visualize Reference | Saving References | As TSV File | With Metadata | Using Custom References | Best Practices | Reference Construction | Gene Overlap | Recommendations | Troubleshooting | Low Gene Overlap | All-Zero Usage | High Reconstruction Error | Session Info

Introduction | Load Example Data | Simulated Example Data | Usage Matrix Heatmap | Program Activity on UMAP | Score Distributions | Violin Plots | Ridge Plot | Program Correlation Analysis | Correlation Heatmap | Network Visualization | Publication-Ready Figures | Combined Panel Figure | Saving High-Resolution Figures | Integration with Seurat | Tips for Effective Visualization | Session Info

Introduction | Setup | Performance Optimization | Parallel Processing | NOVA Options | Memory Efficiency | Custom Ligand-Receptor Database | Creating Custom Database | Using Custom Database | Extending Built-in Database | Filtering and Subsetting | Advanced Filtering | Subsetting by Cluster | Programmatic Workflows | Batch Processing | Custom Analysis Pipeline | Integration with Other Tools | Export for Cytoscape | Integration with CellChat/LIANA | Troubleshooting | Common Issues | Session Info | Author

Overview | Theoretical Background | Cell-Cell Communication | Mathematical Framework | 1. Gene Expression Statistics | Detection Rate (Percentage of Expressing Cells) | Mean Expression | 2. Expression Specificity | 3. Edge Weight Calculation | Expression-based Weight | Specificity-based Weight | 4. Filtering Criteria | Algorithmic Implementation | Cluster Statistics Computation | Edge Computation | Differential Analysis | Fold Change Calculation | Delta Metrics | Ligand-Receptor Database | connectomeDB2020 | Database Structure | Multi-Species Support | Homology Mapping | Supported Species | Computational Efficiency | Vectorization Strategy | Complexity Analysis | References | Author

Introduction | Setup | Simulating Two Conditions | Running Individual Analyses | Differential Analysis | Running DiffEdges | Understanding the Output | Visualization | Differential Heatmap | Volcano Plot | Summarization Functions | By Cluster Pair | By LR Pair | Filtering Significant Changes | Interpreting Results | Key Metrics | Biological Interpretation | Exporting Results | Best Practices | 1. Sample Matching | 2. Multiple Testing Consideration | 3. Biological Validation | Session Info | Author

Introduction | Key Features | Installation | Quick Start | Loading the Package | Simulating Example Data | Loading LR Database | Running NOVA Analysis | Exploring Results | Working with Seurat Objects | Basic Visualization | Communication Heatmap | Network Graph | Session Info | Author | Citation

Introduction | Supported Species | Species Details | Homology Mapping | How It Works | Converting Gene Symbols | Analyzing Mouse Data | Standard Workflow | Cross-Species Comparison | Comparative Study Design | Gene ID Types | Converting Between ID Types | Special Considerations | 1. One-to-Many Mappings | 2. Missing Orthologs | 3. Species-Specific Genes | Best Practices | Workflow Recommendations | Quality Control | Session Info | Author

Introduction | Setup | Creating Example Data | Running Analysis | 1. Communication Heatmap | Basic Heatmap | Specificity-weighted Heatmap | 2. Network Visualization | Circular Layout | Force-directed Layout | Kamada-Kawai Layout | 3. Chord Diagram | Specificity-based Chord | 4. Ligand-Receptor Pairs Visualization | 5. Color Palette | 6. Custom Styling | Custom Colors | 7. Saving Plots | Summary Table | Session Info | Author

Overview | The Challenge of Cell Clustering Evaluation | Self-Projection Framework | Core Concept | Algorithm Steps | Mathematical Formulation | 1. Data Partitioning | 2. Classification Model | 3. Confusion Matrix | Confusion Matrix Normalization | R1 Normalization | R2 Normalization | Visualization of Confusion Analysis | Cluster Merging Strategy | Graph-Based Approach | Community Detection | Iterative Optimization | Convergence Criteria | Cutoff Adaptation | Classifier Comparison | Supported Algorithms | Mathematical Details | Performance Metrics | Per-Cluster Accuracy | Overall Accuracy | Summary | References

Introduction | Installation | Loading the Package | Creating Example Data | Basic Assessment | Running Self-Projection | Understanding Results | Visualization | ROC Curves | Confusion Matrix Heatmaps | Simulating Over-Clustering | Assessment of Over-Clustered Data | Single Optimization Round | Full Optimization Pipeline | Optimization History | Compare Before and After | Sankey Diagram | Using Different Classifiers | Summary

Overview | Compatibility | Key Functions for Seurat | Basic Workflow | Loading a Seurat Object | Quick Assessment | Full Assessment | Feature Space Options | Clustering Optimization | Standard Optimization Workflow | Visualizing Optimization Results | Accessing Optimization History | Constrained Optimization | Adding Cluster Reliability Scores | Working with Multiple Assays | Seurat v5 Specific Notes | Complete Workflow Example | Troubleshooting | Common Issues | Summary

Overview | Preparing Example Data | ROC and Precision-Recall Curves | Basic ROC Plot | Precision-Recall Curves | Combined ROC and PRC | Customizing ROC Plots | Confusion Matrix Heatmaps | Raw Confusion Matrix | R1-Normalized (Default) | R2-Normalized | Custom Color Schemes | Side-by-Side Comparison | Per-Cluster Accuracy Plots | Assessment Summary | Custom Accuracy Plot | Optimization Visualization | Preparing Optimization Results | Optimization History | Custom Optimization Plot | Sankey Diagrams | Basic Sankey | Custom Sankey | Creating Publication-Ready Figures | Combined Assessment Figure | Saving Plots | Theme Customization | Applying Custom Themes | Summary

Introduction | GPU Acceleration | Checking GPU Availability | Specifying Device | Custom Model Architectures | Creating Custom Networks | Architecture Selection Guidelines | Training Optimization | Early Stopping | Learning Rate Tuning | Batch Size Considerations | Data Quality Optimization | Handling Unknown Cell Types | Optimal Sample Simulation | Gene Selection Strategies | Working with Multiple Datasets | Merging Simulations | Cross-validation Strategy | Model Persistence and Deployment | Saving Models with Metadata | Loading and Deploying | Troubleshooting | Common Issues and Solutions | Memory Management | Reproducibility | Setting Seeds | Best Practices Summary

Introduction | Problem Formulation | The Deconvolution Problem | Traditional Approaches vs. Deep Learning | Algorithmic Pipeline | Overview | Step 1: Bulk RNA-seq Simulation | Mathematical Formulation | Sparse Sample Generation | Step 2: Data Preprocessing | Log Transformation | Sample-wise Min-Max Normalization | Gene Filtering | Step 3: Neural Network Architecture | Fully Connected Network | Output Layer (Softmax) | Dropout Regularization | Step 4: Training | Loss Function | Adam Optimizer | Step 5: Ensemble Prediction | Architecture Specifications | Theoretical Considerations | Universal Approximation | Advantages over Linear Models | Limitations | References

Introduction | Setup | Generate Example Data and Run Deconvolution | Visualization Methods | 1. Stacked Bar Plot | 2. Heatmap Visualization | 3. Box Plot Comparison | 4. Training History Plot | 5. Correlation Plot (with Ground Truth) | 6. Per-Cell Type Accuracy | 7. Pie Chart for Individual Samples | Advanced Visualization | Ensemble Model Comparison | Summary

Overview | Installation | Quick Start | Loading the package | Basic Assessment with Matrix Input | With Seurat Objects | Clustering Optimization | The Optimization Process | Visualization Functions | ROC Curves | Confusion Matrix Heatmap | Optimization History | Classifier Options | Advanced Usage | Using Constraints | Parallel Processing | Session Info | References

Overview | Key Features | Installation | Quick Start | Step-by-Step Workflow | Step 1: Simulate Bulk Data | Step 2: Process Training Data | Step 3: Create and Train Model | Step 4: Predict Cell Fractions | Step 5: Save and Load Model | Model Architecture | Evaluation | Tips for Best Results | Citation | Session Info

Introduction | Mathematical Framework | Problem Formulation | Model Architecture | Encoder Network | Time Inference | Latent Space Inference | Neural ODE | Continuous Dynamics | Integration | ODE Function Architecture | Decoder Network | Reconstruction Likelihood | 1. Negative Binomial (NB) | 2. Zero-Inflated Negative Binomial (ZINB) | 3. Mean Squared Error (MSE) | Loss Function | Components | Time Direction Determination | Demonstration | Key Innovations | 1. Automatic Time Inference | 2. Continuous Dynamics | 3. Unified Framework | References | Session Info

Introduction | Hyperparameter Tuning | Latent Space Dimensionality | ODE Network Architecture | VAE Network Architecture | Loss Function Selection | Loss Weight Balancing | Training Configuration | Learning Rate Scheduling | Batch Size and Epochs | Data Subsampling | Handling Complex Trajectories | Branching Trajectories | Cyclic Trajectories | Prediction on Query Data | Coarse vs Fine Mode | Predicting Future States | Integration with Seurat Workflows | Adding Results to Seurat Object | Downstream Analysis | Performance Optimization | GPU Acceleration | Memory Management | Batched Inference | Model Persistence | Saving and Loading | Model Inspection | Troubleshooting | Training Issues | Quality Issues | Memory Issues | Reproducibility | Best Practices Summary | Session Info

Introduction | Installation | From R-universe (Recommended) | From GitHub | Basic Workflow | 1. Load Libraries | 2. Prepare Data | 3. Create and Train Model | 4. Extract Pseudotime | 5. Get Latent Space | 6. Compute Vector Field | 7. Save Model | Quick Example with Synthetic Data | Parameter Guidelines | Loss Mode Selection | Latent Dimensions | Next Steps | Session Info

Introduction | Quick Start: Using CellODE Functions | Create Example Data | Simulate CellODE Model Output | Vector Field Computation with CellODE | Step 1: Compute Cosine Similarity | Step 2: Compute Embedding Velocity | Step 3: Grid-Based Vector Field | Visualization with CellODE | Plot Pseudotime with Direction Arrows | Plot Vector Field - Grid Mode | Plot Vector Field - Stream Mode | Plot Vector Field - Raw Mode | Customization Options | Arrow Styling | Clean Publication Figure | Mathematical Background | From Latent Dynamics to Velocity | Cosine Similarity | Transition Probability | Embedding Velocity | Comparison with RNA Velocity | Best Practices | 1. Neighbor Selection | 2. Visualization Density | 3. Self-Transition | Summary | Session Info

Introduction | Mathematical Formulation | Variance-Adjusted Mahalanobis (VAM) Distance | CDF Transformation | Bidirectional Scoring (CytoSig) | Gene Set Information | CytoSig Database | Weight Distribution | Reactome Database | Score Interpretation | References | Session Information

Overview | Key Features | Installation | Supported Cytokines | CytoSig Database (n = 41) | Reactome Database (n = 30) | Quick Start | Basic Usage with Seurat | Basic Usage with Matrix | Gene Set Construction | CytoSig Gene Set Example | Reactome Gene Set Example | Score Interpretation | Session Information

Introduction | Basic Heatmap | Seurat Integration | Distribution Analysis | Correlation Analysis | Best Practices | Session Information

Overview | Prerequisites | Basic Workflow | Loading Data | Standard Preprocessing | Running MAGIC | Accessing MAGIC Results | Visualization Comparison | Gene Expression Before/After MAGIC | Gene-Gene Scatter Plots | Advanced Usage | Imputing Specific Genes | Using Automatic t Selection | Custom Parameters | Seurat v4 vs v5 Compatibility | Seurat v5 Layers | Integration with Downstream Analysis | Trajectory Analysis | Gene Regulatory Networks | Best Practices | When to Use MAGIC with Seurat | Memory Considerations | Troubleshooting | Common Issues | Session Info

Overview | Installation | Quick Start | Running MAGIC | Accessing Results | Visualizing Results | Before vs After Imputation | Gene-Gene Relationships | Parameter Tuning | Diffusion Time (t) | Key Parameters | Session Info

Introduction | The Dropout Problem in scRNA-seq | MAGIC: A Diffusion-Based Solution | Algorithm Steps | Step 1: Dimensionality Reduction (Optional) | Step 2: k-Nearest Neighbor Graph | Step 3: α-Decaying Kernel | Step 4: Markov Normalization | Step 5: Diffusion (Powering) | Automatic t Selection | Procrustes Disparity | Convergence Criterion | Solver Options | Exact Solver | Approximate Solver | Spectral Interpretation | Practical Recommendations | Parameter Selection Guidelines | When to Use MAGIC | References | Session Info

Overview | Setup | Data Size Impact | Generating Test Data | Benchmarking Different Sizes | Visualization | Solver Comparison | Exact vs Approximate | Accuracy Comparison | Parameter Impact | Effect of t (Diffusion Time) | Effect of knn | Effect of npca | Memory Usage | Sparse vs Dense Input | Recommendations | Small Datasets (<1,000 cells) | Medium Datasets (1,000-10,000 cells) | Large Datasets (>10,000 cells) | Memory-Constrained Environments | Summary Table | Session Info

Introduction | Mathematical Foundation | The Transcriptional Switch Model | ODE System | Analytical Solutions | The EM Algorithm | Overview | E-Step: Time Assignment | M-Step: Parameter Update | Implementation in scVeloR | Basic Usage | Step-by-Step Approach | Parameter Details | Interpreting Results | Kinetic Parameters | Latent Time | Quality Control | Gene Fit Quality | Convergence Check | Velocity Confidence | Troubleshooting | Common Issues | Best Practices | Advanced Topics | Scaling Factor | Parallel Processing | Summary | Session Information

Introduction | The Central Dogma and mRNA Kinetics | Biological Background | The Kinetic Model | Analytical Solutions | Constant Transcription (Induction Phase) | Zero Transcription (Repression Phase) | Steady State | Velocity Estimation Models | Model 1: Deterministic (Steady-State) | Model 2: Stochastic | Model 3: Dynamical | Velocity Graph Construction | Cosine Similarity | Transition Probability | Latent Time Inference | Gene-Shared Latent Time | Root Cell Identification | Model Selection Guidelines | References | Session Information

Introduction | Performance Architecture | Memory Optimization | Sparse Matrix Usage | Memory Profiling | Chunked Processing | Parallel Computing | Using the future Package | Platform-Specific Configuration | Parallel Best Practices | Algorithmic Optimizations | Gene Selection | Neighbor Graph Approximation | Benchmark Comparison | EM Algorithm Optimization | Hardware Recommendations | Memory Requirements | Compute Requirements | Profiling Your Analysis | Timing Code Blocks | Identifying Bottlenecks | Practical Workflow for Large Data | Optimized Pipeline | Memory-Efficient Visualization | Troubleshooting Performance Issues | Common Issues and Solutions | Quick Diagnostics | Summary | Session Information

Introduction | Model Overview | 1. Deterministic (Steady-State) Model | 2. Stochastic Model | 3. Dynamical Model | Comparison Table | Visual Comparison | When to Use Each Model | Use Steady-State When: | Use Stochastic When: | Use Dynamical When: | Diagnostic Plots | Phase Portrait Comparison | R-squared Distribution | Computational Benchmarks | Practical Workflow | Recommended Approach | Session Information

Introduction | Velocity Embedding Plot | Basic Usage | Customization Options | Example Output | Streamline Plot | Grid Plot | Phase Portrait | Latent Time Visualization | Velocity Confidence | Combined Visualizations | Multi-panel Figure | Customizing Colors | Exporting Figures | Session Information

Introduction | Installation | Quick Start Workflow | Load packages | Prepare your data | Step 1: Preprocessing | Step 2: Compute Moments | Step 3: Velocity Estimation | Steady-State Model (Fastest) | Stochastic Model (Recommended) | Dynamical Model (Most Accurate) | Step 4: Velocity Graph | Step 5: Project to Embedding | Step 6: Visualization | Velocity Embedding Plot | Stream Plot | Grid Plot | Phase Portrait | Heatmap | Parallel Computing | Terminal States and Pseudotime | Gene Ranking | Quality Metrics | Model Selection Guide | Session Info | References | Support

Introduction | Prepare Data | Custom Objective Functions | Requirements | Example: Variance Objective | Example: Marker Score Objective | Using Custom Objectives | Three Objectives | Fixed Gene Count Mode | Selection Strategies | 1. Weighted Selection | 2. Index-Based Selection | 3. Target Value Selection | Parallel Computing | Performance Comparison | Parameter Tuning | Population Size | Mutation Probability | Save and Load | Integration Examples | With Seurat | Export to Other Deconvolution Tools | Troubleshooting | Common Issues | Diagnostic Checks | Session Info

Introduction | The Multi-Objective Optimization Problem | Problem Formulation | Pareto Dominance | Pareto Front | NSGA-II Algorithm | Algorithm Overview | Key Components | 1. Non-dominated Sorting | 2. Crowding Distance | 3. Selection | 4. Genetic Operators | Objective Functions | Correlation Objective (Minimize) | Distance Objective (Maximize) | Condition Number (Minimize) | Parameter Guidelines | References | Session Info

Introduction | The Deconvolution Problem | Mathematical Formulation | Why Gene Selection Matters | Complete Workflow | Step 1: Load Package and Data | Step 2: Create Reference Matrix | Step 3: Create Simulated Bulk Data | Step 4: Optimize Gene Selection | Step 5: Select Optimal Genes | Step 6: Perform Deconvolution | Non-Negative Least Squares (NNLS) | Linear Regression | Step 7: Evaluate Accuracy | Comparing Gene Selection Strategies | Best Practices | 1. Reference Matrix Quality | 2. Gene Selection | 3. Method Selection | 4. Validation | Session Info

Introduction | Why darwin? | Installation | Quick Start | Load the Package | Prepare Reference Data | Initialize darwin | Run Optimization | Visualize Pareto Front | Select Optimal Solution | View Fitness Values | Working with Seurat Objects | Basic Deconvolution | Summary | Session Info

Introduction | Performance Architecture | C++ vs R Performance | Scaling with Problem Size | Number of Genes | Number of Cell Types | Optimization Benchmarks | Complete Workflow Timing | Scaling with Generations | Scaling with Population Size | Objective Function Comparison | Memory Usage | Recommendations | Performance Tips | Session Info

Introduction | Setup | Prepare Example Data | Pareto Front Visualization | Basic Pareto Plot | Customized Pareto Plot | Manual Pareto Plot with ggplot2 | Gene Selection Analysis | Gene Count Distribution | Fitness vs Gene Count | Expression Profile Visualization | Heatmap of Selected Genes | Cell Type Similarity | Solution Comparison | Compare Different Selection Methods | Advanced: 3D Pareto Front | Summary | Session Info

Overview | The Spatial Mapping Problem | Problem Statement | Mathematical Formulation | Algorithm Pipeline | Step 1: Cell Type Fraction Estimation | Step 2: Cell Count Estimation | Step 3: Reference Cell Sampling | Step 4: Cost Matrix Construction | Step 5: Linear Assignment Problem Solving | The Jonker-Volgenant Algorithm | Implementation Details | Distance Metrics | Pearson Correlation | Spearman Correlation | Euclidean Distance | Normalization | Handling Large Datasets | Tie-Breaking | References | Session Info

Introduction | Performance Architecture | Benchmarking | C++ vs R Implementation | LAP Solver Performance | Memory Optimization | Sparse Matrix Handling | Downsampling | Parallel Processing | Setting Up Workers | Parallel LAP Solving | Using Future Plans | Chunked Processing | How Chunking Works | Optimization Guidelines | Dataset Size Recommendations | Memory Guidelines | Best Practices | Profiling | Time Profiling | Troubleshooting Performance Issues | Issue: Out of Memory | Issue: Slow Performance | Session Info

Introduction | Installation | Load the Package | Simulated Example | Run CytoSPACER | Explore Results | Visualization | Spatial Cell Type Distribution | With Jitter for Dense Regions | Cell Type Composition | Summary Statistics | Save Results | Next Steps | Session Info

Introduction | Prerequisites | Workflow Overview | Loading Data | From Seurat Objects | From 10x Visium Output | Running CytoSPACER with Seurat | Direct Analysis | Using Idents | Adding Results to Seurat | Basic Integration | Visualizing with Seurat | Data Extraction Utilities | Extract scRNA-seq Data | Extract Spatial Data | Save to Files | Complete Workflow Example | Working with Seurat v5 | Tips and Best Practices | 1. Cell Type Annotation Quality | 2. Gene Overlap | 3. Memory Management | Troubleshooting | Common Issues | Session Info

Introduction | Setup | Generate Example Data | Basic Spatial Plots | Cell Type Distribution | Customizing Point Size and Transparency | Adding Jitter | Custom Colors | Custom Title | Cell Type Composition Plots | Global Composition | With Custom Colors | Advanced Visualizations | Combining with ggplot2 | Faceted by Cell Type | Saving Plots | Color Palettes | Session Info

Introduction | 1. Gene Regulatory Network (GRN) Inference | 1.1 Mathematical Formulation | 1.2 Ridge Regression | 1.3 Bootstrap Aggregation (Bagging) | 2. Signal Propagation Simulation | 2.1 Perturbation Model | 2.2 Iterative Propagation | 2.3 Non-negativity Constraint | 3. Transition Probability Estimation | 3.1 Correlation-Based Approach | 3.2 Probability Conversion | 3.3 Embedding Shift Calculation | 4. Markov Chain Simulation | 4.1 State Transition Model | 4.2 Trajectory Simulation | 4.3 Fate Probability | 5. Network Analysis Metrics | 5.1 Centrality Measures | 5.2 Network Entropy | Summary | References | Session Info

Introduction | Biological Background | Hematopoietic Hierarchy | Key Transcription Factors | Simulated Data Setup | Analysis Workflow | Step 1: Create Oracle Object | Step 2: Import Base GRN | Step 3: Fit GRN | Step 4: Simulate Perturbations | Step 5: Analyze Fate Changes | Step 6: Network Analysis | Results Interpretation | GATA1 Knockout | PU.1 Knockout | Key Findings | Code for Full Analysis | Conclusions | References | Session Info

Introduction | Key Features | Workflow Overview | Installation | Load Libraries | Step 1: Prepare Data | Creating Demo Data | Step 2: Create Oracle Object | Step 3: Import Base GRN | Option A: From Motif Analysis (Recommended) | Option B: From Pre-built Database | Option C: Custom Dictionary | Import to Oracle | Step 4: Preprocessing | Step 5: Fit Cluster-Specific GRN | Step 6: Simulate Perturbation | Step 7: Calculate Transition Probabilities | Step 8: Visualize Results | Cluster Overview | Simulation Flow Field | Gene Expression Changes | Step 9: Network Analysis | Top Regulators | Saving and Loading | Advanced: Comparing Multiple Perturbations | Summary | Next Steps | Session Info

Overview | Pipeline Architecture | Step 1: Input Data Preparation | Expression Matrix | TF-Target Prior Dictionary | Step 2: Cluster-Specific GRN Fitting | Per-Cluster Fitting | Step 3: Ridge Regression Details | Regularization Parameter (α) | Handling Zero Variance | Step 4: Bootstrap Aggregation | Algorithm | Coefficient Stability Assessment | Step 5: Links Object | Links Data Structure | Performance Considerations | Computational Complexity | Parallelization | C++ Acceleration | Best Practices | 1. Data Quality | 2. Appropriate Clustering | 3. TF-Target Prior Quality | Summary | Session Info

Introduction | Cell Embedding Visualizations | Basic Cluster Plot | Gene Expression Overlay | Simulation Flow Visualizations | Quiver Plot (Vector Field) | Streamline Plot | Network Visualizations | Network Graph | Degree Distribution | Network Scores Ranking | Pseudotime Visualizations | Pseudotime on Embedding | Gene Expression along Pseudotime | Comparison Visualizations | Perturbation Comparison | Customization Guide | Color Palettes | Theme Options | Summary | Session Info

Introduction | The Cell-Cell Communication Problem | Traditional Approaches | The Optimal Transport Formulation | Mathematical Formulation | Classical Optimal Transport (Kantorovich Problem) | Entropy-Regularized OT | Unbalanced Optimal Transport | The Sinkhorn Algorithm | Dual Formulation | Iterative Updates | Numerical Stability | Collective Optimal Transport | The Collective Framework | COT Strategies | Spatial Distance Cost | Distance Threshold | Biological Interpretation | Communication Direction | Vector Field Computation | Spatial Coherence (Moran's I) | Visualization Example | Parameter Selection Guide | Recommended Settings | References

Overview | Installation | Setup | Create Demo Data | Step 1: Load LR Database | Step 2: Infer Spatial Communication | Step 3: Communication Direction | Step 4: Cluster Communication | Step 5: Visualization | Spatial Communication Plot | Vector Field Visualization | Cluster Communication Heatmap | Summary | Next Steps

Introduction | Demo Data Setup | 1. Spatial Distribution Plots | 1.1 Cell Type Distribution | 1.2 Communication Signal Heatmap | 2. Vector Field Visualizations | 2.1 Communication Direction Arrows | 2.2 Streamline-Style Visualization | 3. Cluster Communication Plots | 3.1 Communication Heatmap | 3.2 Network Diagram | 4. Multi-Pathway Comparisons | 4.1 Dot Plot | 4.2 Bar Plot Comparison | 5. Publication-Ready Combined Figure | Customization Tips | Color Palettes | Export Settings

Overview | The Challenge | Algorithm Steps | Step 1: Data Standardization | Step 2: Dimensionality Reduction | Step 3: Matrix Orthogonalization | Step 4: Principal Vectors Computation | Step 5: Projection | Step 6: Weighted k-NN Imputation | Comparison with Python Implementation | Summary | References | Session Information

Introduction | Why SpaGER? | Installation | Basic Usage | Load Package | Generate Simulated Data | Run SpaGE Prediction | Predict Specific Genes | Cross-Validation | Visualize CV Results | Accessing Metadata | Session Information

Overview | Prerequisites | Basic Workflow with Seurat | Load Your Data | Prepare Data (Optional) | Run SpaGE | Access Predictions | Seurat v4 vs v5 | Explicit Version Control | Predict Specific Genes | Return Data Frame Instead | Working with Different Assays | Visualization After Prediction | Batch Processing Multiple Gene Sets | Tips for Best Results | 1. Matching Cell Types | 2. Gene Filtering | 3. Normalize Consistently | Complete Example Workflow | Troubleshooting | Common Issues | Session Information

Introduction | Simulated Dataset | Run SpaGE Prediction | Visualizing Principal Vector Selection | Spatial Expression Patterns | Cross-Validation Results | Measured vs Predicted Scatter Plots | Expression Distribution Comparison | Correlation Heatmap | Summary Statistics | Exporting Results | Session Information

Introduction | Key Features | Installation | Quick Start | Step 1: Load Ligand-Receptor Database | Filter LR Pairs by Expression | Step 2: Infer Spatial Communication | Access Results | Step 3: Communication Direction Analysis | Spatial Autocorrelation of Direction | Step 4: Cluster-Level Analysis | Visualize Cluster Communication | Dotplot for Multiple Pathways | Step 5: Downstream Analysis | Communication-Dependent Gene Detection | Communication Impact Analysis | Group Cells by Communication Patterns | Parameters Guide | Key Parameters for spatial_communication() | COT Strategies | Citation | Session Info

Introduction | 1. Threshold Calibration with Normal Tissue | Using scPharmGenNullDist | Apply Calibrated Thresholds | Threshold Selection Strategy | 2. Multi-Drug Analysis | Pan-Drug Screening | Drug Class Analysis | Multi-Cancer Analysis | 3. Combination Therapy Optimization | scPharmCombo Analysis | Combination Selection Criteria | 4. Integration with Seurat Workflows | Pre-computed Embeddings | Cluster-Level Analysis | 5. Performance Tuning | Memory Management | Parallel Processing | Parameter Optimization | 6. Custom Drug Signatures | Using External Gene Sets | 7. Quality Control | Pre-analysis Checks | Post-analysis Validation | 8. Troubleshooting | Common Issues | Debug Mode | 9. Exporting Results | To Data Frame | To Seurat Object | Session Info

Overview | Workflow Architecture | Step 1: Copy Number Variation Detection | Algorithm Overview | Mathematical Framework | Step 2: Multiple Correspondence Analysis (MCA) | Why MCA? | Mathematical Formulation | C++ Implementation | Step 3: Cell Identity Signatures | Distance Metric | Signature Extraction | Step 4: Gene Set Enrichment Analysis | GSEA Algorithm | Drug Sensitivity Gene Sets | Step 5: Cell Classification | Gaussian Mixture Model | Classification Criteria | Step 6: Drug Scoring Metrics | Drug Prioritization Score (Dr) | Drug Side Effect Score (Dse) | Computational Complexity | References | Session Info

Introduction | Installation | Load Required Packages | Prepare Example Data | Basic Workflow | Step 1: Identify Pharmacological Subpopulations | Step 2: Drug Prioritization | Step 3: Predict Drug Side Effects | Step 4: Identify Drug Combinations | Understanding Output | Cell Labels | Drug Prioritization Score (Dr) | Drug Side Effect Score (Dse) | Parameter Guidelines | Supported Cancer Types | Next Steps | Session Info

Introduction | Simulated Analysis Results | 1. Cell Type Distribution | UMAP with Cell Labels | UMAP with Drug Response | UMAP with NES Values | 2. NES Distribution Analysis | Histogram by Cell Type | Density Plot | Violin Plot | 3. Drug Prioritization Visualization | Bar Plot of Dr Scores | Dr vs Dse Scatter Plot | 4. Cell Proportion Analysis | Stacked Bar Plot | Pie Chart | 5. Multi-Drug Comparison | Heatmap of Drug Effects | 6. Combined Dashboard | Summary Panel | 7. Export Functions | Save High-Quality Figures | Session Info

Introduction | Installation | Quick Workflow | Step 1: Load Required Packages | Step 2: Load Example Data | Step 3: Fit GAM Models | Step 4: Run Differential Expression Tests | Association Test | Differential End Points Test | Pattern Test | Step 5: Visualize Results | Plot Gene Expression Smoothers | Plot Gene Count | Summary | Session Info

Overview | The Statistical Model | Negative Binomial GAM | Cubic Regression Splines | Knot Selection | Statistical Tests | Association Test | Differential End Test | Pattern Test | Early DE Test | Wald Test Framework | Fold Change Thresholds | Multiple Conditions | Computational Considerations | Parallelization | Memory Efficiency | References | Session Info

Introduction | Setup | Fit GAM Models | Gene Expression Smoothers | Basic Smoother Plot | Customized Smoother Plot | Custom Color Schemes | Plot Specific Lineages | Gene Count Plots | Basic Gene Count Plot | With Model Knots | Multiple Gene Comparison | Comparing Expression Patterns | Heatmap Visualization | Expression Heatmap Along Trajectory | Knot Evaluation Plot | Volcano Plot of Results | Ranked Gene Plot | Publication-Ready Figures | Combined Figure | Tips for Effective Visualization | Session Info

Introduction | Load data | Monocle3 | Constructing the trajectory | Extracting the pseudotimes and cell weights for tradeSeq | Session | References

Introduction | Installation | Load data | Choosing K: a deeper dive into the output from evaluateK | Fit additive models | Adding covariates to the model | Parallel computing | Fitting only a subset of genes | Zero inflation | Convergence issues on small or zero-inflated datasets | tradeSeq list output | Session | References

Installation | Load data | Fit negative binomial model | Within-lineage comparisons | Association of gene expression with pseudotime | Discovering progenitor marker genes | Comparing specific pseudotime values within a lineage | Between-lineage comparisons | Discovering differentiated cell type markers | Discovering genes with different expression patterns | Example on combining patternTest with diffEndTest results | Early drivers of differentiation | Differential expression in large datasets | Clustering of genes according to their expression pattern | Extracting fitted values to use with any clustering method | Clustering using RSEC, clusterExperiment | Contributing and requesting | Session | References

Overview | Theoretical Framework | The Cell-Cell Communication Inference Problem | Why Multi-Sample Analysis? | Core Algorithms | 1. Pseudobulk Aggregation | 2. Differential Expression Analysis | 3. NicheNet Ligand Activity Inference | 4. Multi-Criteria Prioritization | Biological Scenarios | Mathematical Details | Expression Fraction Calculation | Cell-Type Specificity Score | Prioritization Score Aggregation | Comparison with Other Methods | Performance Considerations | Computational Complexity | Parallelization | References

Introduction | Key Features | Installation | From R-Universe (Recommended) | From GitHub | Quick Start Example | Load Required Packages | Load Example Data | Define Analysis Parameters | Load Prior Knowledge Networks | Run MultiNicheNet Analysis | Explore Results | Basic Visualization | Output Structure | Next Steps | Session Info | Citation

Introduction | Overview of Visualization Functions | Sample-Level Visualizations | Ligand-Receptor Product Plots | Differential Expression Visualization | Network Visualizations | Circos Plots | Chord Diagrams | Multi-Criteria Bubble Plots | Mushroom Plots | Ligand Activity Visualizations | Activity Heatmaps | Target Gene Visualizations | Target Gene Expression | Summary Statistics | Overview Barplots | Customization Tips | Color Palettes | Export High-Quality Figures | Session Information

Introduction | Null Distribution Selection | Available Methods | Choosing the Right Method | Assessing Data Characteristics | Parallel Computing | Cross-Platform Configuration | Memory Management | Count Splitting Alternative | Method Comparison | Resolution Tuning | Iterative Resolution Reduction | Finding Optimal Starting Resolution | Clustering Algorithm Selection | Louvain vs Leiden | Integration with Seurat Workflow | Complete Analysis Pipeline | Using seurat_workflow Helper | Batch Processing Multiple Samples | Troubleshooting | Common Issues | Session Information

Introduction | The Double-Dipping Problem | Mathematical Framework | The Knockoff Filter | The W Statistic | FDR-Controlled Selection | The recall Algorithm | Stage 1: Synthetic Null Variable Generation | Supported Distributions | Stage 2: Joint Analysis | Stage 3: Iterative Calibration | Zero-Inflated Poisson Estimation | Maximum Likelihood Estimation | Implementation | Negative Binomial Estimation | Copula Models | Gaussian Copula | Theoretical Guarantees | FDR Control | Comparison with Count Splitting | References

Introduction | Setup | Loading Data | Standard Preprocessing | Running recall | Accessing Results | Visualization | Comparison with Standard Clustering | Next Steps | Session Info

Introduction | Setup | Data Preparation | Comparing Clustering Methods | Standard vs Calibrated Clustering | Side-by-Side UMAP Comparison | Cluster Quality Assessment | Cluster Size Distribution | Marker Gene Heatmap | Feature Visualization | Expression Patterns | Violin Plots | Resolution Analysis | Multi-Resolution Comparison | Publication-Ready Figures | Custom Theme | Exporting Figures | Session Info

Introduction | Key Capabilities | Installation | From R-universe (Recommended) | From GitHub | Quick Start | Load the Package | Prepare Your Data | Run the Analysis | Understand the Output | Next Steps | Citation | Session Info

Introduction | Load Data | Run Pipeline | Results | Output Visualizations | 1. Classification Heatmap | 2. Subclone Heatmap | 3. Phylogenetic Tree | 4. Consensus Plot | 5. OncoPrint Plot | 6. Differential Expression | 7. Pathway Analysis | Summary | Session Info

Introduction | Load Data | Run Multi-Sample Comparison | Output Visualizations | Combined OncoPrint | Cross-Sample Phylogeny | Summary | Session Info

Introduction | Setup | Preparing Multi-Sample Data | Data Structure | Download Example Data | Running Multi-Sample Analysis | Basic Comparison | With Known Normal Cells | Output Interpretation | Generated Files | Comparative Heatmap | Visualization Functions | Plot All Clonal Profiles | Plot Subclonal Profiles | Advanced Analysis | Identifying Shared Alterations | Custom Comparison Plots | Case Study: Head & Neck Cancer | Primary vs Lymph Node Metastasis | Interpreting Primary vs Metastasis | Statistical Considerations | Sample Size Requirements | Batch Effect Considerations | Best Practices | Workflow Checklist | Common Pitfalls | Session Info

Introduction | Load Data | Run Multi-Sample Comparison | Biological Questions | Output Files | Summary | Session Info

Overview | Algorithmic Framework | 1. Data Preprocessing Pipeline | 1.1 Quality Control | 1.2 Variance Stabilization | 2. Confident Normal Cell Detection | 2.1 Single-Sample Gene Set Testing | 2.2 Cell Type Signatures | 3. VegaMC Segmentation Algorithm | 3.1 Greedy Optimization | 3.2 Statistical Testing | 4. Copy Number State Calling | 4.1 Gaussian Mixture Model | 4.2 EM Algorithm | 5. Subclone Detection | 5.1 Graph-Based Clustering | 5.2 Modularity Optimization | 6. Differential Analysis | 6.1 Subclone-Specific Alterations | 6.2 Pathway Enrichment | Performance Characteristics | Benchmarking Results | Computational Complexity | Parameter Guidelines | References | Session Info

Introduction | Setup | Extracting Data from Seurat | Using getCountMtxFromSeurat() | Manual Extraction | Running SCEVAN | Standard Analysis | Using Seurat Clusters as Prior | Adding Results to Seurat | Add Cell Classifications | Add CNA Scores | Visualization in Seurat | UMAP with Cell Classification | UMAP with Subclones | Feature Plot with CNA Scores | Violin Plots | Advanced Integration | Region-Specific CNA Visualization | Combined Visualization | Downstream Analysis | Differential Expression by Subclone | Pathway Analysis per Subclone | Creating New Seurat Object | From Scratch with SCEVAN Results | Best Practices | Memory Management | Saving Results | Troubleshooting | Common Issues | Cell Name Formatting | Session Info

Introduction | Setup | Load Required Packages | Download Example Data | Running the Pipeline | Basic Analysis | Understanding Parameters | Examining Results | Cell Classification Summary | Output Files | Output Visualizations | 1. Classification Heatmap (*heatmap.png) | 2. Subclone Heatmap (*heatmap_subclones.png) | 3. Clonal Tree (*CloneTree.png) | 4. Consensus Plot (*consensus.png) | 5. OncoPrint Visualization (*OncoHeat.png) | Downstream Analysis | Load CNA Matrix | Extract Region-Specific CN Values | Differential Expression in CNA Regions | Advanced Options | Using Known Normal Cells | Adjusting Segmentation | Adding Custom Gene Sets | Best Practices | Quality Control Checklist | Recommended Workflow | Troubleshooting | Common Issues | Session Info

Overview | Mathematical Framework | Problem Formulation | Ridge Regression Model | Implementation via Cholesky Decomposition | Statistical Significance | Permutation Testing | Z-score Calculation | P-value Computation | Signature Matrix Construction | SecAct Signature Database | Signature Grouping | Algorithm Comparison: R vs GSL | Practical Considerations | Lambda Selection | Number of Permutations | Summary | References | Session Info

Introduction | Installation | Load Package and Create Example Data | Workflow 1: Highly Expressed Gene Analysis | Step 1: Parse Expression Data | Step 2: Find Ligand-Receptor Pairs | Step 3: Visualize with Circos Plot | Step 4: Visualize with Network Plot | Workflow 2: Multi-Category Analysis | Workflow 3: Using the Ligand-Receptor Database | Summary | Session Info

Introduction | Setup | LRPlot: Circos Visualization | Basic Circos Plot | Understanding the Circos Plot | Customized Circos Plot | NetView: Network Visualization | Basic Network Plot | Understanding the Network | Customized Network with Labels | DEG Data Visualization | Color Recommendations | Scientific Color Palettes | Publication-Ready Export | Tips and Best Practices | Session Info

Mathematical Framework | 1. Consensus Clustering | 1.1 Subsampling Strategy | 1.2 Consensus Matrix Construction | 1.3 Visualizing the Consensus Matrix | 2. Proportion of Ambiguous Clustering (PAC) | 2.1 Definition | 2.2 Interpretation | 2.3 PAC Visualization | 2.4 Relative PAC (rPAC) | 3. Optimal K Selection | 3.1 Multi-Objective Optimization | 3.2 Pareto Frontier | 4. SigClust Statistical Testing | 4.1 Hypothesis Framework | 4.2 Cluster Index | 4.3 P-value Heatmap Interpretation | 4.4 Interpretation Guide | 5. Complete Workflow Visualization | 6. Computational Complexity | 7. Parameter Recommendations | References | Author

Overview | The Challenge | The MultiK Solution | Installation | Quick Start | Load Package and Data | Step 1: Run MultiK Algorithm | Step 2: Diagnostic Visualization | Step 3: Extract Clusters | Step 4: Visualize Clusters | Step 5: Statistical Validation | Key Functions | Summary | Author | Session Info

Overview | 1. Diagnostic Plots (DiagMultiKPlot) | 1.1 K Frequency Distribution | 1.2 Relative PAC (rPAC) | 1.3 Frequency vs. Stability Trade-off | 2. SigClust Visualization (PlotSigClust) | 2.1 Hierarchical Dendrogram | 2.2 P-value Heatmap | 3. Consensus Matrix Visualization | 3.1 Understanding the Consensus Matrix | 4. Color Schemes | 5. Publication-Ready Figures | 5.1 Saving Figures | 5.2 Recommended Dimensions | Author

Overview | CNV Heatmaps | Example CNV Heatmap | Basic Heatmap | Customizing Heatmaps | Split by Cell Type | Split by CNV Clusters | Custom Color Scheme | Heatmap Components | Phylogenetic Trees | Example Dendrogram | Building CNV Trees | Visualizing Trees | Annotated Trees | Tree Interpretation | Chromosome Arm-Level Analysis | Example: CNV Fraction per Chromosome Arm | Computing Arm-Level CNVs | Visualizing Arm-Level CNVs | Spatial CNV Visualization | Visium Data | Visium HD Data | Combining Spatial and CNV Information | Custom Visualizations | Extracting CNV Data | Creating Custom Plots | Comparing Clusters | Publication-Ready Figures | High-Resolution Export | Figure Panel Assembly | Color Palettes | Default Palettes | Custom Palettes | Tips and Best Practices | 1. Heatmap Ordering | 2. Color Scaling | 3. Resolution Selection | 4. Figure Legends | Session Info

Theoretical Background | Copy Number Variations in Cancer | Expression-Based CNV Inference | Algorithm Pipeline | Step 1: Gene Ordering | Step 2: Sliding Window Smoothing | Step 3: Reference Normalization | Step 4: Score Computation | Step 5: Quantile-Based Thresholding | Step 6: Hierarchical Clustering | Mathematical Details | Distance Metric | CNV Classification | Performance Optimization | Vectorized Operations | Memory Management | Algorithm Parameters Summary | Validation and Quality Control | Checking Results | References | Session Info

Overview | Example Output: CNV Heatmap | Key Features | Installation | From R-universe (Recommended) | From GitHub | Quick Start | Basic Workflow | Understanding the Parameters | Examining Results | Example Output: Single Cell CNV Profile | Supported Data Types | Single-Cell RNA-seq | 10X Visium | 10X Visium HD | Next Steps | Session Info | Citation | Contact

Overview | Key Features | Installation | From R-universe (Recommended) | From GitHub | Quick Start | Load Required Packages | Prepare Your Data | Create Connectome | Filter Edges | Visualize Results | Network Plot | Circos Plot | Centrality Analysis | Understanding the Output | Workflow Summary | Next Steps | Citation | Session Info

Overview | Example Data Preparation | Network Visualizations | 1. NetworkPlot | 2. CircosPlot | Dot Plot Visualizations | 3. EdgeDotPlot | Centrality Analysis | 4. Centrality | Scatter Plots | 5. SignalScatter | Customization Tips | Color Palettes | Filtering Before Plotting | Saving High-Quality Figures | Visualization Selection Guide | Session Info

Overview | Load Package and Data | Step 1: Calculate MRAS | MRAS Distribution | Step 2: Compute Metabolic Fluxes | Flux Interpretation | Step 3: Key Metabolic Indicators | Central Carbon Metabolism | Biomass Production | Step 4: Pathway Analysis | Step 5: Quality Control | Steady-State Verification | Gene Coverage | Summary | Author | Session Information

Overview | Load Package and Data | Step 1: Calculate MRAS | Step 2: Compute Fluxes | Visualization 1: Flux Distribution | Visualization 2: Key Metabolite Exchange | Visualization 3: Top Variable Reactions | Visualization 4: Pathway Activity | Visualization 5: Sample Correlation | Visualization 6: Exchange Reaction Analysis | Summary Statistics | Session Information

Best Practices | 1. Data Preprocessing | 1.1 Quality Control | 1.2 Recommended Preprocessing | 2. Parameter Selection | 2.1 Number of Repetitions (reps) | 2.2 Subsampling Proportion (pSample) | 2.3 Resolution Range | 2.4 PCA Dimensions (nPC) | 3. Computational Considerations | 3.1 Parallel Processing | 3.2 Memory Management | 3.3 Runtime Estimates | 4. Interpreting Results | 4.1 Clear Optimal K | 4.2 Multiple Candidate K Values | 4.3 Hierarchical Relationships | 5. Troubleshooting | 5.1 "No valid consensus matrices found" | 5.2 High PAC for All K | 5.3 SigClust Returns NA | 5.4 Long Runtime | 6. Validation Strategies | 6.1 Biological Validation | 6.2 Technical Validation | 6.3 Cross-Validation | 7. Reporting Guidelines | Example Methods Text | Author

Theoretical Foundation | 1. Ligand-Receptor Database | FANTOM5 Database | Evidence Levels | 2. Edge Weight Computation | Expression Metrics | Edge Weight Functions | 3. Statistical Testing | Wilcoxon Rank-Sum Test | Multiple Testing Correction | 4. Diagnostic Odds Ratio (DOR) | Standard DOR | Haldane-Anscombe Correction | 5. Network Centrality Analysis | Graph Construction | Kleinberg's Hub and Authority Scores | 6. Differential Connectivity Analysis | Fold Change Computation | Perturbation Score | 7. Implementation Details | Computational Complexity | Memory Efficiency | References | Session Info

Data Preparation | Input Requirements | Preprocessing Checklist | Cell Type Considerations | Parameter Optimization | CreateConnectome Parameters | FilterConnectome Parameters | Performance Optimization | Large Datasets | Parallel Processing | Common Issues and Solutions | Issue 1: No edges after filtering | Issue 2: Missing cell types in visualization | Issue 3: Memory errors | Issue 4: Species mismatch | Issue 5: Custom ligand-receptor database | Quality Control | Sanity Checks | Biological Validation | Reproducibility | Setting Seeds | Saving Results | Session Documentation | Session Info

Overview | Workflow | Step 1: Prepare Data | Split by Condition | Why EvenSplit? | Step 2: Create Individual Connectomes | Step 3: Compute Differential Connectome | Output Columns | Step 4: Interpret Results | Perturbation Score | Example Interpretation | Step 5: Visualization | Circos Diagram | Edge Dot Plot | Scoring Heatmap | Advanced Analysis | Mode-Specific Changes | Cell Type-Specific Changes | Export Results | Best Practices | Sample Size | Filtering Strategy | Handling Infinite Values | Common Pitfalls | Session Info

Overview | Single-Cell Resolution Platforms | STARmap Example (Built-in Data) | Other Single-Cell Platforms | Spot-Based Platforms | 10x Visium Workflow | Slide-seq / Slide-seqV2 | Workflow Comparison | Single-cell Workflow (STARmap, MERFISH, Xenium) | Spot-based Workflow (Visium, Slide-seq) | Parameter Guidelines by Platform | Best Practices | For Spot-based Data | For Single-cell Data | Session Info

Introduction | Setup | Spatial Cell Type Visualization | plot_st_celltype_all | plot_st_celltype | plot_st_celltype_density | Gene Expression Visualization | plot_st_gene | Advanced Visualizations (After CCI Analysis) | plot_ccdist | plot_lrpair | Customization Tips | Custom Themes | Summary of Visualization Functions | Session Info

Introduction | Citation | Key Features | Installation | Quick Start with STARmap Data | Step 1: Load Package and Data | Step 2: Create SpaTalk Object | Step 3: Visualize Spatial Distribution | Step 4: Filter LR-Pathway Pairs | Step 5: Infer Cell-Cell Communications | Step 6: Visualize Results | Cell-Cell Distance Distribution | LR Pair Spatial Distribution | Next Steps | Session Info

Introduction | Theoretical Background | Genome-Scale Metabolic Models | Stoichiometric Matrix | Flux Balance Analysis (FBA) | Steady-State Assumption | Optimization Problem | Biomass Objective | Metabolic Reaction Activity Scores (MRAS) | Gene-Protein-Reaction (GPR) Rules | Scoring Algorithm | Isoenzyme Score (OR) | Complex Score (AND) | Mixed Relationships | Normalization | Flux Bounds Construction | Reversible Reactions | Irreversible Reactions | Exchange Reactions | Community Modeling (Single-Cell) | Community Stoichiometric Matrix | Weighted Objective | Solver: OSQP | Algorithm Settings | Convergence | Computational Complexity | References | Author

Overview | Key Features | Author | Installation | From R-universe (Recommended) | From GitHub | Quick Start | Load Package | Bulk RNA-seq Analysis | Single-Cell Analysis | Workflow Overview | Data Requirements | Citation | Session Info

Overview | Biological Context | Tumor Microenvironment Metabolism | Community Modeling Concept | Data Preparation | Input Requirements | Load Example Data | Step 1: Bootstrap Aggregation | Bootstrap Algorithm | Step 2: Calculate MRAS | Step 3: Define Cell Type Fractions | Step 4: Community Flux Calculation | Output Structure | Step 5: Analyze Results | Extract Cell Type-Specific Fluxes | Compare Metabolic Phenotypes | External Medium Exchange | Advanced Analysis | Metabolic Competition | Metabolic Cooperation | Performance Tips | Parallel Computing | Memory Management | Biological Interpretation | Warburg Effect | Immunometabolism | Output Files | Next Steps | Author | Session Information

Overview | Preparing Multiple Samples | Loading Samples | Sample List Preparation | Running Multi-Sample Analysis | Basic Multi-Sample Workflow | Pooled Reference Analysis | Handling Results | Accessing Individual Results | Merging Results | Cross-Sample Comparisons | Comparing CNV Patterns | Chromosome Arm Comparison | Identifying Recurrent CNVs | Finding Common Events | Visualizing Recurrent CNVs | Batch Effect Considerations | Detecting Batch Effects | Mitigating Batch Effects | Cohort-Level Visualization | Combined Heatmap | Sample-Wise CNV Tree | Memory Management | Large Cohort Processing | Parallel Processing | Case Study: Tumor Cohort Analysis | Workflow Example | Summary | Session Info

Overview | Ligand-Receptor Database | Database Structure | Database Sources | Statistical Methods for Differential Expression | Method Comparison | Wilcoxon Rank-Sum Test | DESeq2 Model | MAST Hurdle Model | L-R Pair Detection Algorithm | Workflow | Matching Algorithm | Multiple Testing Correction | Communication Strength Metrics | Expression-based Scoring | Fold Change-based Scoring | Network Construction | Computational Complexity | References | Session Info

Introduction | Species Detection | Gene Naming Conventions | Automatic Detection | Detection Algorithm | Ortholog Mapping via BioMart | How It Works | BioMart Query Details | Caching System | Automatic Conversion in FindLR | Seamless Workflow | Disabling Auto-Conversion | Mapping Rates and Considerations | Typical Mapping Rates | One-to-Many Mappings | Unmapped Genes | Advanced Configuration | Using Different Ensembl Versions | Mirror Selection | SSL Configuration | Performance Benchmarks | Troubleshooting | Common Issues | Summary | Session Info

Introduction | Custom Databases | Custom Ligand-Receptor Pairs | Custom Pathway Database | Alternative Deconvolution Methods | Method 1: Built-in NNLM (Default) | Method 2: RCTD (spacexr) | Method 3: Seurat Integration | Method 4: SPOTlight | Method 5: deconvSeq | Method 6: stereoscope (Python) | Method 7: cell2location (Python) | Parallel Processing | Enabling Parallel Processing | Memory Optimization | Platform-Specific Workflows | 10x Visium | Slide-seq | STARmap / MERFISH (Single-cell resolution) | Extracting Results | LR Pair Results | Downstream TF Scores | Full CCI Network | Troubleshooting | Common Issues | Best Practices | Session Info

Overview | Algorithmic Pipeline | 1. Cell-Type Deconvolution | Mathematical Formulation | NNLM Algorithm | 2. Spatial Reconstruction | Monte Carlo Spatial Sampling | k-Nearest Neighbor Mapping | 3. Knowledge Graph Integration | LR-Pathway Database | Pathway Filtering Algorithm | 4. Cell-Cell Communication Inference | Co-expression Score | Permutation Test | 5. Downstream Pathway Scoring | Random Walk Algorithm | C++ Implementation | Computational Complexity | References | Session Info

output: github_document | Read ST data to a SpaCET object | Infer secreted protein activity | Infer cell-cell communication | Visualize cell-cell communication | Visualize secreted signaling velocity

output: github_document | Prepare expression matrix | Infer cell-cell communication | Visualize cell-cell communication | Visualize secreted signaling flow

output: github_document | Prepare expression data | Infer secreted protein activity | Calculate clinical relevance | Visualize risk score | Draw survival plot

Introduction | Basic Usage | 1. Load Example Data | 2. Infer Secreted Protein Activity | 3. Explore Results | Visualization | Activity Heatmap | Bar Plot | Lollipop Plot | Compare Treatment vs Control | R vs GSL Implementation | Workflow Summary | Next Steps | Getting Help | Session Info

output: github_document | Prepare expression data | Infer activity change | Visualize activity change | Run a differential profile

output: github_document | Load scRNA-Seq data | Infer secreted protein activity | Visualize activity

output: github_document | Read ST data to a SpaCET object | Infer secreted protein activity | Estimate signaling pattern | Calculate signaling velocity | Deconvolve ST data | Calculate hallmark score

Overview | Activity Heatmaps | Basic Heatmap | Customized Colors | Divergent Scale | Bar Plots | Basic Bar Plot | With Title | Custom Colors | Lollipop Plots | Basic Lollipop | Combining Plots | Side-by-Side Comparison | Publication-Ready Figures | Customized Theme | Statistical Visualization | P-value Distribution | Volcano Plot | Sample Correlation | Tips for Better Visualizations | 1. Choose Appropriate Color Palettes | 2. Consider Your Audience | 3. Always Include Context | Session Info

Introduction | Biological Question | Clinical Context | Setup | Simulated Immunotherapy Data | Single-Cell Data: Pre-treatment Tumor Biopsies | Bulk Data: Pre-treatment with Response Annotation | Visualize Response Distribution | Run scPAS with Binomial Family | Visualize Results | UMAP Overview | Cell Type Response Association | Detailed Violin Plot | Stacked Bar Plot | Biological Interpretation | Responder-Associated Populations (scPAS+) | Non-Responder-Associated Populations (scPAS-) | Predictive Signature | Extract Response Signature | Signature Visualization | Publication Figure | Clinical Application | Predict Response for New Patients | Key Takeaways | Session Information

Introduction | Biological Question | Analysis Overview | Setup | Simulated Cancer Data | Single-Cell Data: Tumor Microenvironment | Bulk Data: TCGA-like Cohort with Survival | Visualize Survival Data | Run scPAS with Cox Regression | Visualize Results | UMAP Overview | Cell Type Enrichment | Violin Plot by Cell Type | Biological Interpretation | Key Findings | Publication Figure | Model Application to New Data | Key Takeaways | Session Information

Introduction | Installation | Apply scPAS with Cox regression | Load data | Prepare the scRNA-seq data | Prepare the bulk data and phenotype | Run scPAS without imputation | Run scPAS with imputation | Apply the trained model to independent bulk data | Apply the trained model to spatial transcriptomics data | Information about the current R session

Biological Motivation | Mathematical Framework | Signaling Entropy Rate (SR) | Step 1: Transition Probabilities | Step 2: Stationary Distribution | Step 3: Local Entropy | Step 4: Global Entropy Rate | Visual Demonstration | Network Structure | Entropy Computation Example | CCAT: Fast Approximation | Why SR and CCAT Correlate | References | Session Info

Introduction | Performance Comparison | Small Dataset (50 cells) | Medium Dataset (200 cells) | Performance Summary | Scaling Analysis | Extrapolated Performance | Recommendations | Dataset Size Guidelines | Workflow for Large Datasets | Memory Usage | Session Info

Introduction | Installation | Quick Example | Simulate Single-Cell Data | Method 1: CCAT (Fast) | Method 2: SR (Accurate) | Compare Methods | Interpretation | When to Use Each Method | Session Info

Introduction | 1. Distribution Plots | Box Plot with Individual Points | Violin Plot | 2. Scatter Plots | SR vs CCAT Correlation | 3. Density Plots | Overlapping Densities | Ridge Plot Style | 4. Statistical Comparison | Significance Annotation | 5. Local Entropy Heatmap | 6. Summary Statistics Table | Publication Tips | Session Info

Introduction | Building Hierarchical Models | Multi-Level Gating Strategy | Model Conversion Utilities | Multi-Class Classification | Annotating Multiple Cell Types | Visualizing Multi-Class Results | Working with Integrated Data | Using Pre-computed Reductions | Assay Selection | Parameter Optimization | Key Parameters | Threshold Tuning | Performance Evaluation | Using Ground Truth | Interpreting Metrics | Parallel Processing | Multi-core Processing | Gene Blacklisting | Default Blacklist | Custom Blacklist | Troubleshooting | Common Issues | Diagnostic Checks | Best Practices Summary | Session Info

Overview | Algorithm Pipeline | Step 1: Signature Scoring with UCell | Mathematical Formulation | Key Properties | Step 2: kNN Smoothing | Effect of Smoothing | Step 3: Hierarchical Decision Trees | Gating Logic | Parameter Decay | Performance Metrics | Matthews Correlation Coefficient (MCC) | Numerical Stability | Computational Considerations | Vectorized Operations | Parallel Processing | Summary | References | Session Info

Introduction | Key Features | Installation | Quick Example | Load Required Packages | Load Example Data | Create a Simple Gating Model | Apply scGate | Visualize Results | Building More Complex Models | Positive and Negative Markers | Using Pre-defined Models | Multi-class Classification | Key Parameters | Session Info

Introduction | Preparing Example Data | Basic Visualizations | Gating Results on UMAP | Signature Score Visualization | Level-by-Level Visualization | Using plot_levels() | Custom Level Visualization | Score Distribution Analysis | Violin Plots | Density Plots by Cell Type | UCell Score Ridge Plots | Using plot_UCell_scores() | Confusion Matrix Visualization | Creating a Confusion Matrix | Publication-Ready Figures | Combined Summary Figure | Color Palettes | Recommended Color Schemes | Exporting Figures | High-Resolution Export | Tips for Effective Visualization | Session Info

Overview | Mathematical Framework | Problem Formulation | Network-Regularized Sparse Regression | Loss Functions by Phenotype Type | Gaussian Family (Continuous) | Binomial Family (Binary) | Cox Family (Survival) | Gene Network Construction | Shared Nearest Neighbor (SNN) Network | Risk Score Calculation | Per-Cell Risk Score | Normalized Risk Score | Statistical Significance Testing | Permutation Test | FDR Correction | Cell Classification | Implementation Details | Sparse Matrix Operations | Parallel Computing | References | Session Information

Introduction | Key Features | Package Installation | Quick Example | Load Required Packages | Simulate Example Data | Preprocess Single-Cell Data | Run scPAS Analysis | Examine Results | Basic Visualization | Output Structure | Three Phenotype Types | 1. Continuous Phenotype (Gaussian) | 2. Binary Phenotype (Binomial) | 3. Survival Phenotype (Cox) | Next Steps | Session Information

Introduction | Setup and Simulated Data | Create Simulated scPAS Result | Basic UMAP Plots | Cell Type Overview | Risk Score Visualization | Cell Classification | Combined Multi-Panel Plot | Cell Type Enrichment Analysis | Proportion Bar Plot | Enrichment Heatmap | Volcano-Style Plot | Violin Plots | Risk Score by Cell Type | Split Violin by Classification | Box Plots with Statistical Tests | Density Plots | Pie Chart Summary | Publication-Ready Figure | Saving Plots | Session Information

Introduction | Parameter Reference | Main Function Parameters | Using Individual Functions | 1. Quality Control | 2. Network Construction | 3. Multiple Networks | 4. Tensor Decomposition | 5. Manifold Alignment | 6. Differential Regulation | Parameter Tuning Guidelines | Network Construction Parameters | Quantile Threshold Effect | Tensor Rank Selection | Comparing Multiple Knockouts | Performance Considerations | Memory Usage | Parallel Processing | Best Practices | Recommended Workflow | Troubleshooting | Session Info

Overview | Step 1: Quality Control | Mathematical Formulation | Step 2: Network Construction (Principal Component Regression) | Algorithm | Mathematical Derivation | Step 3: Tensor Decomposition (CP-ALS) | CANDECOMP/PARAFAC Decomposition | Alternating Least Squares (ALS) Algorithm | Reconstruction | Step 4: Virtual Knockout | Implementation | Step 5: Manifold Alignment | Non-linear Manifold Alignment (NLMA) | Spectral Embedding | Step 6: Differential Regulation Analysis | Distance-Based Statistics | Fold Change | Statistical Testing | Multiple Testing Correction | Summary | References | Session Info

Introduction | Key Features | Installation | Quick Example | Load Package and Data | Run Virtual Knockout Analysis | View Results | Visualize Results | Output Structure | Differential Regulation Table | Next Steps | Session Info

Introduction | Run Analysis | Output Structure | Understanding the Differential Regulation Table | Column Descriptions | Key Metrics Explained | Distance | Fold Change (FC) | Statistical Significance | Interpreting the Gene Regulatory Networks | Network Comparison | Knockout Effect on Network | Biological Interpretation Guidelines | Categories of Affected Genes | Potential Interpretations | Quality Assessment | Check Knockout Gene Rank | Network Sparsity Check | Significance Distribution | Exporting Results | Summary | Session Info

Introduction | Run Analysis | 1. Volcano Plot | 2. Distance Distribution Plot | 3. Ranking Plot | 4. Network Heatmap | 5. Manifold Alignment Plot | 6. P-value Distribution | 7. Summary Statistics Table | Session Info

Abstract | Introduction | Background and Motivation | Package Overview | Mathematical Foundations | Spatial Autocorrelation: Moran's I Statistic | Definition and Intuition | Statistical Inference | Spatial Weights Specifications | Kernel-Based Association Tests: SPARK-X | Variance Component Score Test | Multiple Kernel Types | P-value Computation and Combination | Binary Spatial Enrichment: binSpect | Methodology | Nearest-Neighbor Gaussian Processes: nnSVG | Full Statistical Model | Covariance Function | NNGP Approximation | Likelihood Ratio Test | Effect Size: Proportion of Spatial Variance | Installation and Setup | Data Description and Visualization | Simulated Spatial Transcriptomics Data | Spatial Spot Layout | Gene Expression Distribution | Spatial Expression Pattern Visualization | SVG Detection: Method-by-Method Tutorial | Method 1: MERINGUE (Moran's I with Spatial Networks) | Algorithm Overview | Running MERINGUE | Visualizing MERINGUE Results | Method 2: binSpect (Binary Spatial Enrichment) | Running binSpect | Visualizing binSpect Results | Method 3: SPARK-X (Kernel-Based Association) | Running SPARK-X | Visualizing SPARK-X Results | Method 4: Seurat (Moran's I with Distance Weights) | Running Seurat Method | Unified Interface: CalSVG() | Comprehensive Method Comparison | Performance Metrics | Visual Performance Comparison | ROC Curve Analysis | Overlap Analysis | Advanced Analysis | Data Simulation for Custom Benchmarking | Parallelization for Large Datasets | Gene Filtering Strategies | Practical Guidelines | Method Selection Framework | Parameter Tuning Guidelines | Network Construction | Statistical Testing | Computational Considerations | Conclusion | Session Information | References

Overview | Architecture Patterns | Pattern 1: The Robust Pipeline | Implementation | Configuration Best Practices | Environment-Based Configuration | Configuration Decision Tree | Error Handling Strategies | The Three-Layer Defense | Error Recovery Workflow | Session Management | Naming Convention | Cleanup Strategy | Performance Optimization | Batch Size Tuning | Memory Management | Monitoring and Logging | Progress Tracking | Production Checklist | Anti-Patterns to Avoid | Summary | Additional Resources

Overview | Architecture Overview | 1. Fingerprinting Mechanism | What is a Fingerprint? | Fingerprint Generation Flow | Feature Selection Rationale | Custom Session IDs | 2. Checkpointing Mechanism | Checkpoint Data Structure | Checkpoint Save Timing | Configuring Batch Size | 3. Auto-Recovery Mechanism | Recovery Flow | Recovery Demo | 4. Error Retry Mechanism | Retry Flow | Configure Retry Attempts | 5. Storage Location | Complete Execution Flow | Design Principles | Next Steps

Overview | Error Handling Architecture | Layer 1: Built-in Fault Tolerance | Automatic Retry | Retry Flow Diagram | Layer 2: Error Wrapper Functions | s_safely() - Capture Errors | Using s_safely with Mapping | s_possibly() - Default on Error | Using s_possibly for Robust Pipelines | s_quietly() - Capture Side Effects | Comparison of Error Handlers | Combining Strategies | Strategy 1: s_safely + Post-Processing | Strategy 2: s_possibly for Clean Pipelines | Strategy 3: Multi-Layer Protection | Decision Guide | Error Handling Patterns | Pattern 1: Fail Fast | Pattern 2: Log and Continue | Pattern 3: Collect Errors for Reporting | Best Practices | Next Steps

Overview | Function Family Overview | s_map Family: Single-Input Mapping | s_map - Basic Mapping | s_map_chr - Return Character Vector | s_map_dbl - Return Numeric Vector | s_map_int - Return Integer Vector | s_map_lgl - Return Logical Vector | s_map_dfr - Return Data Frame (Row-Bind) | s_map_dfc - Return Data Frame (Column-Bind) | s_map2 Family: Dual-Input Mapping | s_map2 Basic Usage | Practical Examples | s_pmap: Multi-Input Mapping | s_pmap Basic Usage | Combining with Data Frames | s_imap: Indexed Mapping | s_imap Basic Usage | s_walk Family: Side-Effect Functions | s_walk Basic Usage | s_walk2 Dual-Input Side Effects | Function Parameter Passing | Session ID Usage | Performance Considerations | Common Patterns | Pattern 1: Data Transformation Pipeline | Pattern 2: Conditional Processing | Pattern 3: Safe NULL Handling | Next Steps

Overview | Why Parallel + Fault Tolerance? | Prerequisites | Setting Up Parallel Processing | Step 1: Load Required Packages | Step 2: Configure Workers | Step 3: Use s_future_* Functions | Available Parallel Functions | Basic Usage Examples | s_future_map | s_future_map2 | s_future_pmap | Execution Flow | Configuration Options | Batch Size for Parallel | furrr Options | When to Use Parallel Processing | Good Use Cases for Parallel | Poor Use Cases for Parallel | Handling Progress | Error Handling in Parallel | Best Practices | Complete Example | Next Steps

Why SafeMapper? | Installation | Your First Example: From purrr to SafeMapper | Traditional Approach (using purrr) | The SafeMapper Way | Core Features Demo | 1. Automatic Recovery | 2. Multiple Output Types | 3. Dual-Input Mapping | 4. Multi-Input Mapping | Function Reference Table | Workflow Diagram | Configuration (Optional) | Clean Old Sessions | Next Steps | Summary

Overview | Example 1: Web API Data Collection | Scenario | Implementation | Convert to Data Frame | Example 2: Batch File Processing | Example 3: Machine Learning Cross-Validation | Example 4: Web Scraping Pipeline | Example 5: Parallel Bioinformatics Pipeline | Example 6: Database Migration | Quick Reference: Configuration by Use Case | Next Steps

Overview | Configuration | s_configure() - Customize Behavior | Configuration Options Explained | Configuration Recommendations | Session IDs | Automatic vs Manual Session IDs | When to Use Manual Session IDs | Checkpoint Storage | Storage Location | Checkpoint File Structure | Session Cleanup | s_clean_sessions() - Remove Old Checkpoints | Cleanup Options | Cleanup Workflow | Workflow Examples | Example 1: Long-Running Daily Job | Example 2: Development and Debugging | Example 3: Multiple Related Tasks | Best Practices | Troubleshooting | Checkpoint Not Resuming | Too Many Checkpoint Files | Checkpoint Corrupted | Next Steps

Introduction | Theoretical Background | Installation | From Bioconductor | From GitHub | Quick Start | Prepare Example Data | Run cDNB Analysis | Run tDNB Analysis | Available Methods | Methods Requiring PPI Networks | Example: LcDNB with PPI Network | Interpreting Results | Identifying the Critical State | Performance Optimization | Session Information | References

Introduction | What You'll Learn | Installation | Load Package and Example Data | The 5-Minute Workflow | Step 1: Create scMetaLink Object | Step 2: Infer Metabolite Production | Step 3: Infer Metabolite Sensing | Step 4: Compute Communication | Step 5: Filter Significant Interactions | Quick Visualization | Communication Heatmap | Chord Diagram | One-Line Workflow | Understanding the Output | Key Objects in the Result | Accessing Results | Export Results | Next Steps | Session Info

Overview | The MetalinksDB Knowledge Base | Interaction Types | Mode of Regulation (MOR) | Protein Types | Mathematical Framework | 1. Metabolite Production Potential (MPP) | Gene Expression Scoring | Trimean Option | 2. Metabolite Sensing Capability (MSC) | Affinity Weighting | Hill Function (Optional) | 3. Communication Score | 4. Population Size Correction (Optional) | Statistical Framework | Permutation Test | Multiple Testing Correction | Data Processing Pipeline | Key Assumptions | Comparison with Ligand-Receptor Methods | References | Next