SVG: Spatially Variable Genes Detection Methods for Spatial
Transcriptomics
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>.
| Version: |
1.0.0 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
parallel, stats, utils, methods, MASS, Rcpp (≥ 1.0.0) |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
BRISC, geometry, RANN, CompQuadForm, BiocParallel, SpatialExperiment, SingleCellExperiment, SummarizedExperiment, spatstat.geom, spatstat.explore, testthat (≥ 3.0.0), knitr, rmarkdown, covr |
| Published: |
2026-02-01 |
| DOI: |
10.32614/CRAN.package.SVG (may not be active yet) |
| Author: |
Zaoqu Liu  [aut,
cre],
SVGbench Contributors [ctb] (Original method implementations) |
| Maintainer: |
Zaoqu Liu <liuzaoqu at 163.com> |
| BugReports: |
https://github.com/Zaoqu-Liu/SVG/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/Zaoqu-Liu/SVG, https://zaoqu-liu.github.io/SVG/ |
| NeedsCompilation: |
yes |
| Materials: |
README, NEWS |
| CRAN checks: |
SVG results |
Documentation:
Downloads:
Linking:
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