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bioRxiv · 10.64898/2026.09.05.749179

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

Liu, Y.·Yi, S.·Yin, H.·Ju, W.
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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.

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Liu, Y., Yi, S., Yin, H., Ju, W.. 2026-09-11. scOLAR: Ontology-Anchored Open-Set Annotation of Single-Cell RNA-seq Data. https://doi.org/10.64898/2026.09.05.749179

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