Table 2.
Performance of the crossNN pan-cancer model in several cohorts
| Cohort | Number of cases | Metrics | crossNN |
|---|---|---|---|
| 450K | 3,871 | Accuracy | 0.859 |
| Precision | 0.975 | ||
| Sensitivity | 0.654 | ||
| AUC | 0.901 | ||
| EPICv1 | 554 | Accuracy | 0.949 |
| Precision | 0.962 | ||
| Sensitivity | 0.926 | ||
| AUC | 0.926 | ||
| EPICv2 | 133 | Accuracy | 0.992 |
| Precision | 0.992 | ||
| Sensitivity | 0.977 | ||
| AUC | 0.939 | ||
|
Nanopore R9 |
443 | Accuracy | 0.928 |
| Precision | 0.997 | ||
| Sensitivity | 0.772 | ||
| AUC | 0.947 | ||
|
Nanopore R10 |
129 | Accuracy | 0.860 |
| Precision | 0.989 | ||
| Sensitivity | 0.713 | ||
| AUC | 0.949 | ||
| Targeted sequencing | 124 | Accuracy | 0.847 |
| Precision | 0.935 | ||
| Sensitivity | 0.806 | ||
| AUC | 0.895 | ||
| WGBS | 125 | Accuracy | 0.848 |
| Precision | 0.941 | ||
| Sensitivity | 0.640 | ||
| AUC | 0.860 | ||
| Overall | 5,379 | Accuracy | 0.877 |
| Precision | 0.978 | ||
| Sensitivity | 0.691 | ||
| AUC | 0.897 |
MCF-level raw accuracy before the application of cutoffs, precision with platform-specific cutoffs and AUC of the ROC curve for the classification score to predict the correct classification are given. For crossNN, the following cutoffs, as derived above, were used: microarray > 0.3; nanopore/targeted methyl-seq/WGBS > 0.15.