Spatially Variable Genes Detection Methods for Spatial Transcriptomics
ACAT: Aggregated Cauchy Association Test
Convert Adjacency Matrix to Edge List
Binarize Gene Expression
Fast Binarization using K-means (k=2)
Build Spatial Neighborhood Network
binSpect: Binary Spatial Enrichment Test for SVG Detection
Detect SVGs using Mark Variogram Method
MERINGUE: Moran's I based Spatially Variable Gene Detection
nnSVG: Nearest-Neighbor Gaussian Process SVG Detection
Seurat-style SVG Detection Methods
SPARK-X: Non-parametric Kernel-based SVG Detection
Unified Interface for SVG Detection
Simulate Spatial Transcriptomics Data with Known SVGs
Davies' Method for Quadratic Form P-values
Fast Distance Matrix Computation
Check if C++ Functions are Available
Compute Fisher's Exact Test for Spatial Enrichment
Generate Hexagonal Grid Coordinates
Generate Random Coordinates
Generate Spatial Expression Pattern
Generate Square Grid Coordinates
Build Spatial Network via Delaunay Triangulation
Build Spatial Network via K-Nearest Neighbors
Build KNN Adjacency Matrix
Local Indicators of Spatial Association (LISA)
Liu's Method for Approximating P-values
Fast Row-wise Moran's I Calculation
Fast Moran's I with Full Statistics
Moran's I Test for Spatial Autocorrelation
Calculate Moran's I Statistic
Row Standardize Adjacency Matrix
Simulate Spatial Transcriptomics Data
SVG: Spatially Variable Genes Detection Methods for Spatial Transcript...
Spatial Network Utilities
Statistical Utilities for SVG Detection
Package Startup Messages
A unified framework for detecting spatially variable genes (SVGs) in spatial transcriptomics data. This package integrates multiple state-of-the-art SVG detection methods including 'MERINGUE' (Moran's I based spatial autocorrelation), 'Giotto' binSpect (binary spatial enrichment test), 'SPARK-X' (non-parametric kernel-based test), and 'nnSVG' (nearest-neighbor Gaussian processes). Each method is implemented with optimized performance through vectorization, parallelization, and 'C++' acceleration where applicable. Methods are described in Miller et al. (2021) <doi:10.1101/gr.271288.120>, Dries et al. (2021) <doi:10.1186/s13059-021-02286-2>, Zhu et al. (2021) <doi:10.1186/s13059-021-02404-0>, and Weber et al. (2023) <doi:10.1038/s41467-023-39748-z>.
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