Limitations of cell embedding metrics assessed using drifting islands.
basic_science · Level V
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- Record sourced from PubMed, PMID 40500472.
- Also identified by DOI 10.1038/s41587-025-02702-z.
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Abstract
Biological studies rely on embeddings of single-cell profiles but assessing the quality of these embeddings is challenging. Here we show that current evaluation metrics are incomplete by training a three-layer perceptron, Islander. Islander outperforms all leading embedding methods on a diverse set of cell atlases but it distorts biological structures, limiting its use for biological discovery. We then present a new metric, scGraph, to help flag such distortions.