NONPARAMETRIC BAYESIAN MULTIARMED BANDITS FOR SINGLE-CELL EXPERIMENT DESIGN

• 成果类型: Article
• 署名作者: Camerlenghi, Federico; Dumitrascu, Bianca; Ferrari, Federico; Engelhardt, Barbara E.; Favaro, Stefano
• 署名单位: University of Milano-Bicocca; Institute for Advanced Study - USA; Duke University; Princeton University; University of Turin
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/20-AOAS1370
• 发表日期: 2020
• 页码: 2003-2019
• 关键词: finite-time analysis; DISCOVERY; number

摘要

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.