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>.">

SVG: Spatially Variable Genes Detection Methods for Spatial Transcriptomics (original) (raw)

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
Author: Zaoqu Liu ORCID iD [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
URL: https://github.com/Zaoqu-Liu/SVG, https://zaoqu-liu.github.io/SVG/
NeedsCompilation: yes
Materials: README, NEWS
CRAN checks: SVG results

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