scAnnotatR: framework to accurately classify cell types in single-cell RNA-sequencing data

BMC Bioinformatics

Table 1 Structured list of existing tools to automatically classify cell types in scRNA-seq datasets

Tools	Usage language	Level of assignment	Reference source	Prediction score	Ambiguous assignment	Unknown population detection
northstar	Python	Cluster	Dataset	No info	No info	Yes
scmap	R	Both	Dataset	Yes	No	Yes
MARS	Python	Cell	Dataset	Yes	Yes	Yes
scCATCH	R	Cluster	Database	Yes	Yes	No
SingleR	R	Cell	Dataset	Yes	No	Yes
CIPR	R	Cluster	Dataset	Yes	No	Yes
clustifyr	R	Both	Dataset	Yes	No	Yes
scMatch	Python	Cell	Dataset	Yes	No	No info
DigitalCellSorter	Python	Cluster	Markers	Yes	Yes	Yes
CellAssign	R	Cell	Markers	Yes	No	Yes
SciBet	R	Cell	Dataset	Yes, but with additional process	No	Yes, but with additional process
Garnett	R	Cell	Markers and datasets	No	No	Yes
CHETAH	R	Cell	Dataset	Yes	Yes	Yes
SCINA	R	Cell	Markers	Yes	No	Yes
scPred	R	Cell	Dataset	Yes	No	Yes
scID	R	Cell	Dataset	Yes	No	Yes
scClassify	R	Cell	Dataset	No	Yes	Yes
ACTINN	Python	Cell	Dataset	Yes	No	No
Superscan	Python	Cell	Dataset	Yes	No	Yes

ISSN: 1471-2105