  The problem of maximizing cell type discovery under budget constraints is a
fundamental challenge for the collection and analysis of single-cell
RNA-sequencing (scRNA-seq) data. In this paper, we introduce a simple,
computationally efficient, and scalable Bayesian nonparametric sequential
approach to optimize the budget allocation when designing a large scale
experiment for the collection of scRNA-seq data for the purpose of, but not
limited to, creating cell atlases. Our approach relies on the following tools:
i) a hierarchical Pitman-Yor prior that recapitulates biological assumptions
regarding cellular differentiation, and ii) a Thompson sampling multi-armed
bandit strategy that balances exploitation and exploration to prioritize
experiments across a sequence of trials. Posterior inference is performed by
using a sequential Monte Carlo approach, which allows us to fully exploit the
sequential nature of our species sampling problem. We empirically show that our
approach outperforms state-of-the-art methods and achieves near-Oracle
performance on simulated and scRNA-seq data alike. HPY-TS code is available at
https://github.com/fedfer/HPYsinglecell.
