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The problem of maximizing cell type discovery under budget constraints is afundamental challenge for the collection and analysis of single-cellRNA-sequencing (scRNA-seq) data. In this paper, we introduce a simple,computationally efficient, and scalable Bayesian nonparametric sequentialapproach to optimize the budget allocation when designing a large scaleexperiment for the collection of scRNA-seq data for the purpose of, but notlimited to, creating cell atlases. Our approach relies on the following tools:i) a hierarchical Pitman-Yor prior that recapitulates biological assumptionsregarding cellular differentiation, and ii) a Thompson sampling multi-armedbandit strategy that balances exploitation and exploration to prioritizeexperiments across a sequence of trials. Posterior inference is performed byusing a sequential Monte Carlo approach, which allows us to fully exploit thesequential nature of our species sampling problem. We empirically show that ourapproach outperforms state-of-the-art methods and achieves near-Oracleperformance on simulated and scRNA-seq data alike. HPY-TS code is available athttps://github.com/fedfer/HPYsinglecell.

Nonparametric Bayesian multi-armed bandits for single cell experiment design