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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 |
Maintainer: | Jinpu Li <castle.lee.f at gmail.com> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | no |
CRAN checks: | scBSP results |
Reference manual: | scBSP.pdf |
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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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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