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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 https://github.com/fedfer/HPYsinglecell.|
|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|
|Pages:||2003 - 2019|
|Type of Material:||Journal Article|
|Journal/Proceeding Title:||Annals of Applied Statistics|
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