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 method 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: 0.0.1
Imports: Matrix, sparseMatrixStats, fitdistrplus, RANN, spam
Suggests: knitr, rmarkdown
Published: 2024-02-09
Author: Jinpu Li ORCID iD [aut, cre]
Maintainer: Jinpu Li <castle.lee.f at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: scBSP results


Reference manual: scBSP.pdf


Package source: scBSP_0.0.1.tar.gz
Windows binaries: r-prerel:, r-release:, r-oldrel:
macOS binaries: r-prerel (arm64): scBSP_0.0.1.tgz, r-release (arm64): scBSP_0.0.1.tgz, r-oldrel (arm64): scBSP_0.0.1.tgz, r-prerel (x86_64): scBSP_0.0.1.tgz, r-release (x86_64): scBSP_0.0.1.tgz


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