Nonparametric bayesian multiarmed bandits for single-cell experiment design

Federico Camerlenghi, Bianca Dumitrascu, Federico Ferrari, Barbara E. Engelhardt, Stefano Favaro

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)2003-2019
Number of pages17
JournalAnnals of Applied Statistics
Volume14
Issue number4
DOIs
StatePublished - 2020

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty

Keywords

  • Cell type discovery
  • Experimental sampling design
  • Hierarchical Pitman–Yor model
  • Multiarmed bandits
  • ScRNA-seq
  • Sequential Monte Carlo
  • Thompson sampling

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