Package: SCEVAN 1.0.6

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