Package: scBSP 1.1.0
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>).
Authors:
scBSP_1.1.0.tar.gz
scBSP_1.1.0.zip(r-4.7)scBSP_1.1.0.zip(r-4.6)scBSP_1.1.0.zip(r-4.5)
scBSP_1.1.0.tgz(r-4.6-any)scBSP_1.1.0.tgz(r-4.5-any)
scBSP_1.1.0.tar.gz(r-4.7-any)scBSP_1.1.0.tar.gz(r-4.6-any)
scBSP_1.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
scBSP/json (API)
| # Install 'scBSP' in R: |
| install.packages('scBSP', repos = c('https://castleli.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/castleli/scbsp/issues
Last updated from:94e05397b9. Checks:7 WARNING, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | WARNING | 122 | ||
| source / vignettes | OK | 169 | ||
| linux-release-x86_64 | WARNING | 107 | ||
| macos-release-arm64 | WARNING | 114 | ||
| macos-oldrel-arm64 | WARNING | 164 | ||
| windows-devel | WARNING | 116 | ||
| windows-release | WARNING | 75 | ||
| windows-oldrel | WARNING | 78 | ||
| wasm-release | OK | 108 |
Exports:CombinePvaluesLoadSpatialscBSPSpFilter
Dependencies:dotCall64fitdistrpluslatticeMASSMatrixMatrixGenericsmatrixStatsRANNRcpprlangspamsparseMatrixStatssurvival
