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 multiarmed 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.

中文翻译：

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用于单细胞实验设计的非参数贝叶斯多臂匪

在预算约束下最大化细胞类型发现的问题是单细胞RNA测序（scRNA-seq）数据的收集和分析的一项基本挑战。在本文中，我们引入了一种简单，计算有效且可扩展的贝叶斯非参数顺序方法，以在设计大规模实验以收集scRNA-seq数据的目的（但不限于创建细胞图集）时优化预算分配。我们的方法依赖于以下工具：（i）分级的Pitman-Yor先验，概括了有关细胞分化的生物学假设，以及（ii）汤普森采样多臂土匪策略，其在开发和探索之间取得平衡，从而优先进行一系列试验的实验。通过使用顺序蒙特卡洛方法进行后验推断，这使我们能够充分利用物种采样问题的顺序性质。我们凭经验证明，我们的方法优于最新方法，并且在模拟和scRNA-seq数据上均达到近Oracle性能。HPY-TS代码可从https://github.com/fedfer/HPYsinglecell获得。