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scAnnotate  

An Automated Cell Type Annotation Tool for Single-Cell RNA-Sequencing Data
View on CRAN: Click here


Download and install scAnnotate package within the R console
Install from CRAN:
install.packages("scAnnotate")

Install from Github:
library("remotes")
install_github("cran/scAnnotate")

Install by package version:
library("remotes")
install_version("scAnnotate", "0.3")



Attach the package and use:
library("scAnnotate")
Maintained by
Xuekui Zhang
[Scholar Profile | Author Map]
First Published: 2022-07-01
Latest Update: 2023-11-20
Description:
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) for more details.
How to cite:
Xuekui Zhang (2022). scAnnotate: An Automated Cell Type Annotation Tool for Single-Cell RNA-Sequencing Data. R package version 0.3, https://cran.r-project.org/web/packages/scAnnotate. Accessed 22 Apr. 2025.
Previous versions and publish date:
0.0.1 (2022-07-01 13:00), 0.0.2 (2022-08-08 08:20), 0.0.3 (2022-08-09 16:50), 0.0.4 (2022-08-16 07:50), 0.1.1 (2022-11-24 18:50)
Other packages that cited scAnnotate R package
View scAnnotate citation profile
Other R packages that scAnnotate depends, imports, suggests or enhances
Complete documentation for scAnnotate
Functions, R codes and Examples using the scAnnotate R package
Some associated functions: eva_cal . pbmc1 . pbmc2 . predict_label . scAnnotate . 
Some associated R codes: br_harmony.R . br_seurat.R . data.R . depthf.R . eva_cal.R . gs_t.R . gs_wilcox.R . mix_dep.R . mix_ln.R . mix_ln_pt.R . pred_addtiny.R . probability_ln.R . qjm.R . scAnnotate.R .  Full scAnnotate package functions and examples
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