The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
An entirely data-driven cell type annotation tools, which requires training data to learn the classifier, but not biological knowledge to make subjective decisions. It consists of three steps: preprocessing training and test data, model fitting on training data, and cell classification on test data. See Xiangling Ji,Danielle Tsao, Kailun Bai, Min Tsao, Li Xing, Xuekui Zhang.(2022)<doi:10.1101/2022.02.19.481159> for more details.
| Version: | 0.3 |
| Depends: | R (≥ 4.0.0) |
| Imports: | glmnet, stats, Seurat (≥ 5.0.1), harmony, SeuratObject |
| Suggests: | knitr, testthat (≥ 3.0.0), rmarkdown |
| Published: | 2024-03-14 |
| DOI: | 10.32614/CRAN.package.scAnnotate |
| Author: | Xiangling Ji [aut], Danielle Tsao [aut], Kailun Bai [ctb], Min Tsao [aut], Li Xing [aut], Xuekui Zhang [aut, cre] |
| Maintainer: | Xuekui Zhang <xuekui at uvic.ca> |
| License: | GPL-3 |
| URL: | https://doi.org/10.1101/2022.02.19.481159 |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | scAnnotate results |
| Reference manual: | scAnnotate.pdf |
| Vignettes: |
introduction |
| Package source: | scAnnotate_0.3.tar.gz |
| Windows binaries: | r-devel: scAnnotate_0.3.zip, r-release: scAnnotate_0.3.zip, r-oldrel: scAnnotate_0.3.zip |
| macOS binaries: | r-release (arm64): scAnnotate_0.3.tgz, r-oldrel (arm64): scAnnotate_0.3.tgz, r-release (x86_64): scAnnotate_0.3.tgz, r-oldrel (x86_64): scAnnotate_0.3.tgz |
| Old sources: | scAnnotate archive |
Please use the canonical form https://CRAN.R-project.org/package=scAnnotate to link to this page.
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.