Package: SVG 1.0.0
Zaoqu Liu
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>.
Authors:
SVG_1.0.0.tar.gz
SVG_1.0.0.tar.gz(r-4.7-arm64)SVG_1.0.0.tar.gz(r-4.7-x86_64)SVG_1.0.0.tar.gz(r-4.6-arm64)SVG_1.0.0.tar.gz(r-4.6-x86_64)
SVG_1.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
SVG/json (API)
NEWS
| # Install 'SVG' in R: |
| install.packages('SVG', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/zaoqu-liu/svg/issues
Pkgdown/docs site:https://zaoqu-liu.github.io
- example_svg_data - Example Spatial Transcriptomics Data
Last updated from:a3ad09d0b5. Checks:6 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 359 | ||
| linux-devel-x86_64 | OK | 171 | ||
| source / vignettes | OK | 264 | ||
| linux-release-arm64 | OK | 163 | ||
| linux-release-x86_64 | OK | 169 | ||
| wasm-release | OK | 144 |
Exports:ACAT_combinebinarize_expressionbuildSpatialNetworkCalSVGCalSVG_binSpectCalSVG_MarkVarioCalSVG_MERINGUECalSVG_nnSVGCalSVG_SeuratCalSVG_SPARKXgetSpatialNeighbors_DelaunaygetSpatialNeighbors_KNNmoranImoranI_testsimulate_spatial_data
Dependencies:MASSRcppRcppArmadillo
