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Nonparametric Bayesian multiarmed bandits for single-cell experiment design

Author(s): Camerlenghi, Federico; Dumitrascu, Bianca; Ferrari, Federico; Engelhardt, Barbara E; Favaro, Stefano

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Abstract: 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
Publication Date: 2020
Citation: Camerlenghi, Federico, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, and Stefano Favaro. "Nonparametric Bayesian multiarmed bandits for single-cell experiment design." The Annals of Applied Statistics 14, no. 4 (2020): pp. 2003-2019. doi:10.1214/20-AOAS1370
DOI: 10.1214/20-AOAS1370
ISSN: 1932-6157
EISSN: 1941-7330
Pages: 2003 - 2019
Type of Material: Journal Article
Journal/Proceeding Title: Annals of Applied Statistics
Version: Author's manuscript

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