A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data R package scBSP version 1.1.0 Identifying spatially variable genes is critical in linking molecular cell functions with tissue phenotypes. This package utilizes a granularity-based dimension-agnostic tool, single-cell big-small patch (scBSP), implementing sparse matrix operation and KD tree methods for distance calculation, for the identification of spatially variable genes on large-scale data. The detailed description of this method is available at Wang, J. and Li, J. et al. 2023 (Wang, J. and Li, J. (2023), <doi:10.1038/s41467-023-43256-5>). Software Imports: Matrix, sparseMatrixStats, fitdistrplus, RANN, spam Suggests: knitr, rmarkdown Jinpu Li <castle.lee.f@gmail.com> Comprehensive R Archive Network (CRAN) Jinpu Li [aut, cre] (ORCID: <https://orcid.org/0000-0002-6656-2896>), Yiqing Wang [aut] GPL (>= 2) 2025-09-01 application/tgz https://CRAN.R-project.org/package=scBSP doi:10.32614/CRAN.package.scBSP