Single-cell classification using graph convolutional networks

BMC Bioinformatics

Table 1 Accuracy of the eight scRNAseq data classifier tools and the four conventional classifiers on the seven datasets (N = 1000)

Methods	Zhengsorted	Zheng68K	BaronHuman	Muraro	Segerstolpe	BaronMouse	Xin
sigGCN	0.922	0.752	0.979	0.991	0.977	0.974	0.993
FC	0.893	0.668	0.967	0.986	0.967	0.958	0.993
scID	0.721	0.484	0.46	0.577	0.285	0.286	0.986
scPred	0.515	0.140	0.86	0.915	0.827	0.862	0.91
CasTLe	0.836	0.736	0.971	0.972	0.953	0.91	0.993
SingleR	0.723	0.388	0.951	0.977	0.953	0.868	1
scmapcluster	0.395	0.409	0.946	0.962	0.949	0.905	0.931
scmapcell	0.727	0.246	0.895	0.972	0.949	0.778	0.952
ACTINN	0.845	0.737	0.977	0.991	0.958	0.984	1
RF	0.835	0.69	0.968	0.981	0.967	0.968	0.993
SVM-linear	0.859	0.652	0.824	0.981	0.383	0.704	1
SVM-rbf	0.884	0.677	0.93	0.981	0.841	0.794	1
KNN	0.824	0.594	0.953	0.991	0.935	0.926	1

ISSN: 1471-2105