{ "_id": "6a1d44ab1d7bb097a0a41e11", "Package": "scBSP", "Version": "1.1.0", "Date": "2025-09-01", "Title": "A Fast Tool for Single-Cell Spatially Variable Genes\nIdentifications on Large-Scale Data", "Authors@R": "c(\nperson(\"Jinpu\", \"Li\", email = \"castle.lee.f@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-6656-2896\")),\nperson(\"Yiqing\", \"Wang\", email = \"yiqing@wangemail.com\", role = \"aut\")\n)", "Description": "Identifying spatially variable genes is critical in\nlinking molecular cell functions with tissue phenotypes. This\npackage utilizes a granularity-based dimension-agnostic tool,\nsingle-cell big-small patch (scBSP), implementing sparse matrix\noperation and KD tree methods for distance calculation, for the\nidentification of spatially variable genes on large-scale data.\nThe detailed description of this method is available at Wang,\nJ. and Li, J. et al. 2023 (Wang, J. and Li, J. 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