scBSP: A Fast Tool for Single-Cell Spatially Variable Genes Identifications on Large-Scale Data

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>).

Version:	1.1.0
Imports:	Matrix, sparseMatrixStats, fitdistrplus, RANN, spam
Suggests:	knitr, rmarkdown
Published:	2025-09-01
DOI:	10.32614/CRAN.package.scBSP
Author:	Jinpu Li [aut, cre], Yiqing Wang [aut]
Maintainer:	Jinpu Li <castle.lee.f at gmail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation:	no
Materials:	README
CRAN checks:	scBSP results

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