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December 2020 Nonparametric Bayesian multiarmed bandits for single-cell experiment design
Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, Stefano Favaro
Ann. Appl. Stat. 14(4): 2003-2019 (December 2020). DOI: 10.1214/20-AOAS1370


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


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Federico Camerlenghi. Bianca Dumitrascu. Federico Ferrari. Barbara E. Engelhardt. Stefano Favaro. "Nonparametric Bayesian multiarmed bandits for single-cell experiment design." Ann. Appl. Stat. 14 (4) 2003 - 2019, December 2020.


Received: 1 October 2019; Revised: 1 July 2020; Published: December 2020
First available in Project Euclid: 19 December 2020

MathSciNet: MR4194258
Digital Object Identifier: 10.1214/20-AOAS1370

Rights: Copyright © 2020 Institute of Mathematical Statistics


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Vol.14 • No. 4 • December 2020
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