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:	0.3
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:

Reference manual:	scAnnotate.pdf
Vignettes:	introduction

Downloads:

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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