A supervised ontology-aware cell annotation method forsingle-cell transcriptomic data

Magre, N.; Alshehri, E.; Grab, F.; Ordabayev, Y.; McCarroll, S. A.; Babadi, M.; Fleming, S. J.

doi:10.64898/2026.01.13.699356

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bioRxiv · 10.64898/2026.01.13.699356

A supervised ontology-aware cell annotation method forsingle-cell transcriptomic data

Magre, N.·Alshehri, E.·Grab, F.·Ordabayev, Y.·McCarroll, S. A.·Babadi, M.·Fleming, S. J.

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Abstract

Many single-cell RNA-seq annotation methods ignore the hierarchical nature of cell type classification. We present a probability propagation strategy that enforces ontological consistency and improves performance when applied to existing models without retraining. Combined with a lightweight logistic regression model trained on 42 million human cells, this yields SOCAM, a fast and interpretable classifier. We also introduce a hop-based F1 score for ontology-aware evaluation. Code and models are available open source.

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Magre, N., Alshehri, E., Grab, F., Ordabayev, Y., McCarroll, S. A., Babadi, M., Fleming, S. J.. 2026-01-14. A supervised ontology-aware cell annotation method forsingle-cell transcriptomic data. https://doi.org/10.64898/2026.01.13.699356

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