{ "_id": "6a115f20acfb0bcc41cecf52", "Package": "SVG", "Type": "Package", "Title": "Spatially Variable Genes Detection Methods for Spatial\nTranscriptomics", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Zaoqu\", \"Liu\", email = \"liuzaoqu@163.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-0452-742X\")),\nperson(\"SVGbench\", \"Contributors\", role = \"ctb\",\ncomment = \"Original method implementations\"))", "Description": "A unified framework for detecting spatially variable genes\n(SVGs) in spatial transcriptomics data. This package integrates\nmultiple state-of-the-art SVG detection methods including\nMERINGUE (Moran's I based spatial autocorrelation), Giotto\nbinSpect (binary spatial enrichment test), SPARK-X\n(non-parametric kernel-based test), and nnSVG (nearest-neighbor\nGaussian processes). Each method is implemented with optimized\nperformance through vectorization, parallelization, and C++\nacceleration where applicable. Methods are described in Miller\net al. (2021) , Dries et al. (2021)\n, Zhu et al. (2021)\n, and Weber et al. (2023)\n.", "License": "MIT + file LICENSE", "URL": "https://github.com/Zaoqu-Liu/SVG, https://zaoqu-liu.github.io/SVG/", "BugReports": "https://github.com/Zaoqu-Liu/SVG/issues", "Encoding": "UTF-8", "LazyData": "false", "RoxygenNote": "7.3.3", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Repository": "https://zaoqu-liu.r-universe.dev", "Date/Publication": "2026-01-23 07:10:52 UTC", "RemoteUrl": "https://github.com/Zaoqu-Liu/SVG", "RemoteRef": "HEAD", "RemoteSha": "2fb606b28dcf4d96a1a0f212341449a633ea37b9", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-05-23 07:57:09 UTC", "User": "root" }, "Author": "Zaoqu Liu [aut, cre] (ORCID: ),\nSVGbench Contributors [ctb] (Original method implementations)", "Maintainer": "Zaoqu Liu ", "MD5sum": "5b8a9cee483ee15a8dbec35e7888183d", "_user": "zaoqu-liu", "_type": "src", "_file": "SVG_1.0.0.tar.gz", "_fileid": "77c800375d125f5d9bf42534dac0b5c9dba9fd4ed8f6b7cc18f2f19f070a2b01", "_filesize": 2022257, "_sha256": 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"filename": "SVG-introduction.html", "title": "SVG: A Comprehensive R Package for Spatially Variable Gene Detection", "author": "Zaoqu Liu", "engine": "knitr::rmarkdown", "headings": [ "Abstract", "Introduction", "Background and Motivation", "Package Overview", "Mathematical Foundations", "Spatial Autocorrelation: Moran's I Statistic", "Definition and Intuition", "Statistical Inference", "Spatial Weights Specifications", "Kernel-Based Association Tests: SPARK-X", "Variance Component Score Test", "Multiple Kernel Types", "P-value Computation and Combination", "Binary Spatial Enrichment: binSpect", "Methodology", "Nearest-Neighbor Gaussian Processes: nnSVG", "Full Statistical Model", "Covariance Function", "NNGP Approximation", "Likelihood Ratio Test", "Effect Size: Proportion of Spatial Variance", "Installation and Setup", "Data Description and Visualization", "Simulated Spatial Transcriptomics Data", "Spatial Spot Layout", "Gene Expression Distribution", "Spatial Expression Pattern 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