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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=scAnnotate to link to this page.
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