Knowledge-Guided Biomarker Identification for Label-Free Single-Cell RNA-Seq Data: A Reinforcement Learning Perspective

TL;DR

This paper introduces a reinforcement learning-based method for gene panel selection in label-free single-cell RNA-Seq data, leveraging existing algorithms and expert feedback to improve biomarker discovery accuracy and efficiency.

Contribution

It proposes a novel iterative gene selection framework that combines ensemble knowledge and reinforcement learning to reduce biases and enhance biomarker identification.

Findings

Improved precision in biomarker discovery.

Enhanced efficiency over traditional methods.

Effective integration of prior knowledge and RL.

Abstract

Gene panel selection aims to identify the most informative genomic biomarkers in label-free genomic datasets. Traditional approaches, which rely on domain expertise, embedded machine learning models, or heuristic-based iterative optimization, often introduce biases and inefficiencies, potentially obscuring critical biological signals. To address these challenges, we present an iterative gene panel selection strategy that harnesses ensemble knowledge from existing gene selection algorithms to establish preliminary boundaries or prior knowledge, which guide the initial search space. Subsequently, we incorporate reinforcement learning through a reward function shaped by expert behavior, enabling dynamic refinement and targeted selection of gene panels. This integration mitigates biases stemming from initial boundaries while capitalizing on RL's stochastic adaptability. Comprehensive comparative experiments, case studies, and downstream analyses demonstrate the effectiveness of our method, highlighting its improved precision and efficiency for label-free biomarker discovery. Our results underscore the potential of this approach to advance single-cell genomics data analysis.