A gene regulatory network–aware graph learning method for cell identity annotation in single-cell RNA-seq data

Table 1.

The F1-score of different tools on cross-technology data sets

Data set PBMC1-SM2 PBMC1-CS2 PBMC1-DS PBMC1-D PBMC1-SW PBMC2-10X2 Average
scHPL 0.0000 0.1582 0.3336 0.2100 0.3568 0.5718 0.2717
scGAC 0.7419 0.6670 0.5317 0.5201 0.4620 0.6323 0.5925
scDeepSort 0.8707 0.6881 0.6056 0.6046 0.5388 0.6451 0.6588
scVI 0.9376 0.6965 0.6373 0.6246 0.5548 0.7750 0.7043
ACTINN 0.9482 0.8021 0.6104 0.6362 0.5247 0.8232 0.7241
SCENIC 0.4892 0.2298 0.3189 0.1793 0.2098 0.3564 0.2972
scHGR 0.9436 0.8683 0.7225 0.7309 0.6553 0.8664 0.7978

  • Bold indicates the maximum value in that column.

This Article

  1. Published in Advance August 12, 2024, doi: 10.1101/gr.278439.123 Genome Res. 34: 1036-1051

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