Package: SVG 1.0.0

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