Cell type annotation is a fundamental task in single-cell genomics. Although various methods have been developed for automatic annotation, they often function as black-box models lacking explainability, proper uncertainty estimation, and robustness for rare cell types. We introduce ProtoCloud, a self-explanatory deep generative model that embeds cells into a structured, low-dimensional space organized around cell-type-specific prototypes. ProtoCloud matches or outperforms existing methods across 11 large-scale datasets, particularly for rare cell types. Its built-in uncertainty quantification mechanism, based on cell-prototype similarity, identifies and re-annotates misannotated training cells. By backpropagating cell prototype similarities to the gene space, ProtoCloud identifies key genes driving its classifications, facilitating the discovery of both known and novel marker genes. Applied to a time-course dataset of post-injury retinal neurons, ProtoCloud successfully annotates previously unassigned cells; in an esophageal cell atlas, it identifies rare but potentially important cell populations and their marker genes associated with esophageal inflammation.
Guo et al. (2026) studied this question.