From: Single-cell classification using graph convolutional networks
Methods | Zhengsorted | Zheng68K | BaronHuman | Muraro | Segerstolpe | BaronMouse | Xin |
---|---|---|---|---|---|---|---|
sigGCN | 0.965 | 0.776 | 0.977 | 1 | 1 | 0.969 | 0.995 |
FC | 0.952 | 0.681 | 0.938 | 1 | 0.968 | 0.902 | 0.997 |
scID | 0.66 | 0.535 | 0.22 | 0.578 | 0 | 0 | 1 |
scPred | 0.568 | 0.105 | 0.833 | 0.932 | 0.8 | 0.97 | 0.784 |
CasTLe | 0.834 | 0.667 | 0.956 | 0.967 | 0.965 | 0.848 | 0.997 |
SingleR | 0.678 | 0.335 | 0.946 | 0.984 | 1 | 0.898 | 1 |
scmapcluster | 0.729 | 0.357 | 0.9 | 0.997 | 0.965 | 0.88 | 0.991 |
scmapcell | 0.305 | 0.198 | 0.95 | 0.993 | 0.977 | 0.942 | 0.708 |
ACTINN | 0.892 | 0.753 | 1 | 0.97 | 1 | 1 | 0.997 |
RF | 0.853 | 0.646 | 0.956 | 0.987 | 0.993 | 0.984 | 0.997 |
SVM-linear | 0.868 | 0.663 | 0.362 | 1 | 0.059 | 0.238 | 1 |
SVM-rbf | 0.9 | 0.671 | 0.906 | 1 | 0.772 | 0.695 | 1 |
KNN | 0.795 | 0.613 | 0.928 | 1 | 0.961 | 0.889 | 1 |