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) tools:::Rd_expr_doi("10.1101/gr.271288.120"), Dries et al. (2021) tools:::Rd_expr_doi("10.1186/s13059-021-02286-2"), Zhu et al. (2021) tools:::Rd_expr_doi("10.1186/s13059-021-02404-0"), and Weber et al. (2023) tools:::Rd_expr_doi("10.1038/s41467-023-39748-z").
A unified framework for detecting spatially variable genes (SVGs) in spatial transcriptomics data. This package integrates multiple state-of-the-art SVG detection methods:
MERINGUE: Moran's I with binary adjacency network
Seurat: Moran's I with inverse distance weights
binSpect: Binary spatial enrichment test (from Giotto)
SPARK-X: Non-parametric kernel-based test
nnSVG: Nearest-neighbor Gaussian processes
MarkVario: Mark variogram (from spatstat)
CalSVG: Unified interface for all SVG methods
CalSVG_MERINGUE: MERINGUE method (Moran's I with network)
CalSVG_Seurat: Seurat method (Moran's I with 1/d^2 weights)
CalSVG_binSpect: Giotto binSpect method
CalSVG_SPARKX: SPARK-X method
CalSVG_nnSVG: nnSVG method (requires BRISC)
CalSVG_MarkVario: Mark variogram method
buildSpatialNetwork: Build spatial neighborhood network
moranI: Calculate Moran's I statistic
binarize_expression: Binarize gene expression
Maintainer: Zaoqu Liu liuzaoqu@163.com (ORCID)
Other contributors:
SVGbench Contributors (Original method implementations) [contributor]
Zaoqu Liu liuzaoqu@163.com
Miller, B.F. et al. (2022) nnSVG for spatial transcriptomics. Nature Communications.
Sun, S. et al. (2020) Statistical analysis of spatial expression patterns. Nature Methods.
Dries, R. et al. (2021) Giotto: a toolbox for spatial transcriptomics. Genome Biology.
Miller, J.A. et al. (2021) MERINGUE: characterizing spatial gene expression. Genome Research.
Useful links: