scAnnotate: An Automated Cell Type Annotation Tool for Single-CellRNA-Sequencing Data
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 |
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