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Table 1 Automatic cell identification methods included in this study

From: A comparison of automatic cell identification methods for single-cell RNA sequencing data

Name Version Language Underlying classifier Prior knowledge Rejection option Reference
Garnett 0.1.4 R Generalized linear model Yes Yes [14]
Moana 0.1.1 Python SVM with linear kernel Yes No [15]
DigitalCellSorter GitHub version: e369a34 Python Voting based on cell type markers Yes No [16]
SCINA 1.1.0 R Bimodal distribution fitting for marker genes Yes No [17]
scVI 0.3.0 Python Neural network No No [18]
Cell-BLAST 0.1.2 Python Cell-to-cell similarity No Yes [19]
ACTINN GitHub version: 563bcc1 Python Neural network No No [20]
LAmbDA GitHub version: 3891d72 Python Random forest No No [21]
scmapcluster 1.5.1 R Nearest median classifier No Yes [22]
scmapcell 1.5.1 R kNN No Yes [22]
scPred 0.0.0.9000 R SVM with radial kernel No Yes [23]
CHETAH 0.99.5 R Correlation to training set No Yes [24]
CaSTLe GitHub version: 258b278 R Random forest No No [25]
SingleR 0.2.2 R Correlation to training set No No [26]
scID 0.0.0.9000 R LDA No Yes [27]
singleCellNet 0.1.0 R Random forest No No [28]
LDA 0.19.2 Python LDA No No [29]
NMC 0.19.2 Python NMC No No [29]
RF 0.19.2 Python RF (50 trees) No No [29]
SVM 0.19.2 Python SVM (linear kernel) No No [29]
SVMrejection 0.19.2 Python SVM (linear kernel) No Yes [29]
kNN 0.19.2 Python kNN (k = 9) No No [29]