scAnnotate: An Automated Cell Type Annotation Tool for Single-Cell
RNA-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: |
0.0.4 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
glmnet, stats, MTPS, Seurat (≥ 4.0.5), harmony |
| Suggests: |
knitr, testthat (≥ 3.0.0), rmarkdown |
| Published: |
2022-08-16 |
| 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 |
Documentation:
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