Nonparametric Bayesian multi-armed bandits for single cell experiment design

TL;DR

This paper presents a scalable Bayesian nonparametric method using hierarchical Pitman-Yor priors and Thompson sampling to optimize budget allocation in large-scale single-cell RNA-sequencing experiments, improving cell type discovery.

Contribution

It introduces a novel, efficient Bayesian approach combining hierarchical Pitman-Yor processes with Thompson sampling for experimental design in single-cell studies.

Findings

Outperforms state-of-the-art methods in simulations and real data.

Achieves near-Oracle performance in cell type discovery.

Provides a scalable solution for large-scale scRNA-seq experiment planning.

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 multi-armed 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.