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.
Show 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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