Enhanced Gene Selection in Single-Cell Genomics: Pre-Filtering Synergy and Reinforced Optimization

TL;DR

This paper presents a novel iterative gene selection method for single-cell genomics that combines pre-filtering with reinforcement learning to improve the accuracy and efficiency of identifying informative genes for clustering tasks.

Contribution

The study introduces an integrated approach that leverages prior gene selection results and reinforcement learning to enhance gene panel selection in single-cell data analysis.

Findings

Improved gene selection accuracy demonstrated in experiments

Enhanced clustering performance with the proposed method

Effective reduction of bias in gene panel selection

Abstract

Recent advancements in single-cell genomics necessitate precision in gene panel selection to interpret complex biological data effectively. Those methods aim to streamline the analysis of scRNA-seq data by focusing on the most informative genes that contribute significantly to the specific analysis task. Traditional selection methods, which often rely on expert domain knowledge, embedded machine learning models, or heuristic-based iterative optimization, are prone to biases and inefficiencies that may obscure critical genomic signals. Recognizing the limitations of traditional methods, we aim to transcend these constraints with a refined strategy. In this study, we introduce an iterative gene panel selection strategy that is applicable to clustering tasks in single-cell genomics. Our method uniquely integrates results from other gene selection algorithms, providing valuable preliminary boundaries or prior knowledge as initial guides in the search space to enhance the efficiency of our framework. Furthermore, we incorporate the stochastic nature of the exploration process in reinforcement learning (RL) and its capability for continuous optimization through reward-based feedback. This combination mitigates the biases inherent in the initial boundaries and harnesses RL's adaptability to refine and target gene panel selection dynamically. To illustrate the effectiveness of our method, we conducted detailed comparative experiments, case studies, and visualization analysis.