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cff-version: 1.2.0
message: 'To cite package "SVG" in publications use:'
type: software
license: MIT
title: 'SVG: Spatially Variable Genes Detection Methods for Spatial Transcriptomics'
version: 1.0.0
doi: 10.32614/CRAN.package.SVG
abstract: 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) <https://doi.org/10.1101/gr.271288.120>,
  Dries et al. (2021) <https://doi.org/10.1186/s13059-021-02286-2>, Zhu et al. (2021)
  <https://doi.org/10.1186/s13059-021-02404-0>, and Weber et al. (2023) <https://doi.org/10.1038/s41467-023-39748-z>.
authors:
- family-names: Liu
  given-names: Zaoqu
  email: liuzaoqu@163.com
  orcid: https://orcid.org/0000-0002-0452-742X
repository: https://zaoqu-liu.r-universe.dev
repository-code: https://github.com/Zaoqu-Liu/SVG
commit: 2fb606b28dcf4d96a1a0f212341449a633ea37b9
url: https://zaoqu-liu.github.io/SVG/
date-released: '2026-01-23'
contact:
- family-names: Liu
  given-names: Zaoqu
  email: liuzaoqu@163.com
  orcid: https://orcid.org/0000-0002-0452-742X