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:	1.0.0
Imports:	Matrix, sparseMatrixStats, fitdistrplus, RANN, spam
Suggests:	knitr, rmarkdown
Published:	2024-05-03
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
CRAN checks:	scBSP results

Documentation:

Reference manual:

scBSP.pdf

Downloads:

Package source:	scBSP_1.0.0.tar.gz
Windows binaries:	r-devel: scBSP_1.0.0.zip, r-release: scBSP_1.0.0.zip, r-oldrel: scBSP_1.0.0.zip
macOS binaries:	r-release (arm64): scBSP_1.0.0.tgz, r-oldrel (arm64): scBSP_1.0.0.tgz, r-release (x86_64): scBSP_1.0.0.tgz, r-oldrel (x86_64): scBSP_1.0.0.tgz
Old sources:	scBSP archive

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