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Table 3 Accuracy of the eight scRNAseq data classifier and the four conventional classifiers tools on the four experiments (N = 1000)

From: Single-cell classification using graph convolutional networks

Training dataset BaronHuman + Muraro + Segerstolpe Xin + Muraro + Segerstolpe Xin + BaronHuman + Segerstolpe Xin + BaronHuman + Muraro
Testing dataset Xin BaronHuman Muraro Segerstolpe
sigGCN 0.997 0.987 0.974 0.993
FC 0.992 0.977 0.968 0.993
scID 0.989 0.747 0.97 0.979
scPred 0.945 0.467 0.92 0.814
CasTLe 0.99 0.944 0.977 0.992
SingleR 0.995 0.984 0.977 0.996
scmapcluster 0.196 0.003 0.051 0.568
scmapcell 0.756 0.421 0.64 0.367
ACTINN 0.993 0.984 0.974 0.992
RF 0.982 0.941 0.947 0.938
SVM-linear 0.994 0.979 0.972 0.992
SVM-rbf 0.986 0.983 0.973 0.97
KNN 0.934 0.864 0.788 0.817