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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 |
| 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 |
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These binaries (installable software) and packages are in development.
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