The problem of maximizing cell type discovery under budget constraints is a fundamental challenge for the collection and analysis of single-cell RNAsequencing (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.

Camerlenghi, F., Dumitrascu, B., Ferrari, F., Engelhardt, B., Favaro, S. (2020). Nonparametric bayesian multiarmed bandits for single-cell experiment design. THE ANNALS OF APPLIED STATISTICS, 14(4), 2003-2019 [10.1214/20-AOAS1370].

Nonparametric bayesian multiarmed bandits for single-cell experiment design

Camerlenghi F.
Co-primo
;
2020

Abstract

The problem of maximizing cell type discovery under budget constraints is a fundamental challenge for the collection and analysis of single-cell RNAsequencing (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.
Articolo in rivista - Articolo scientifico
Cell type discovery; Experimental sampling design; Hierarchical Pitman–Yor model; Multiarmed bandits; ScRNA-seq; Sequential Monte Carlo; Thompson sampling;
English
19-dic-2020
2020
14
4
2003
2019
none
Camerlenghi, F., Dumitrascu, B., Ferrari, F., Engelhardt, B., Favaro, S. (2020). Nonparametric bayesian multiarmed bandits for single-cell experiment design. THE ANNALS OF APPLIED STATISTICS, 14(4), 2003-2019 [10.1214/20-AOAS1370].
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/298273
Citazioni
  • Scopus 6
  • ???jsp.display-item.citation.isi??? 5
Social impact