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To the Editor—Methods for analyzing single-cell data 1, 2, 3, 4 perform a core set of computational tasks. These tasks include dimensionality reduction, cell clustering, cell-state annotation, removal of unwanted variation, analysis of differential expression, identification of spatial patterns of gene expression, and joint analysis of multi-modal omics data. Many of these methods rely on likelihood-based models to represent variation in the data; we refer to these as ‘probabilistic models’. Probabilistic models provide principled ways to capture uncertainty in biological systems and are convenient for decomposing the many sources of variation that give rise to omics data 5.

Despite the appeal of probabilistic models, several obstacles impede their community-wide adoption. The first obstacle, coming from the perspective of the end user, relates to the difficulty of implementing and running such models. Because probabilistic …