scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data

Authors

Liu, Y., Yi, S., Yin, H., Ju, W.

Abstract

Single-cell RNA sequencing profiles cellular heterogeneity at atlas scale, making automated annotation essential. However, target datasets often contain novel cell types missing from incomplete references. We present scOLAR, an ontology-guided open-set framework that learns prototypes over the Cell Ontology and uses both reference and target expression to annotate known classes while detecting unfamiliar populations. Guided by ontology hierarchies and decision-boundary regularization, scOLAR penalizes coarse-lineage misclassification and groups novel cells without requiring predefined cluster counts. Across benchmarks, scOLAR achieves a novelty-detection AUROC of 0.9726 and an average precision of 0.9871, enabling structured post-hoc lineage-level interpretation of populations absent from the reference.

Preprint server: bioRxiv
The authors list and abstract were imported from bioRxiv on 12 Sep 2026.

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