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

Weiliang Zhang, Zhen Meng, Dongjie Wang, Min Wu, Kunpeng Liu, Yuanchun Zhou, Meng Xiao

TL;DR

RiGPS tackles gene panel selection for scRNA-seq clustering by combining pre-filtering with reinforcement learning to navigate a high-dimensional gene space. The method uses a three-stage framework: Gene Pre-Filtering, Knowledge Injection, and Reinforced Iteration, with a reward that balances spatial separability and panel compactness. It demonstrates superior performance across 25 datasets and produces substantially smaller gene panels while maintaining or improving clustering accuracy. This approach offers a transferable, scalable strategy for informative gene selection in multi-omics and multi-species contexts.

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.

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

TL;DR

RiGPS tackles gene panel selection for scRNA-seq clustering by combining pre-filtering with reinforcement learning to navigate a high-dimensional gene space. The method uses a three-stage framework: Gene Pre-Filtering, Knowledge Injection, and Reinforced Iteration, with a reward that balances spatial separability and panel compactness. It demonstrates superior performance across 25 datasets and produces substantially smaller gene panels while maintaining or improving clustering accuracy. This approach offers a transferable, scalable strategy for informative gene selection in multi-omics and multi-species contexts.

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.
Paper Structure (30 sections, 11 equations, 14 figures, 2 tables)

This paper contains 30 sections, 11 equations, 14 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Background and Preliminary
  3. RiGPS Framework
  4. Framework Overview
  5. Gene Pre-Filtering Strategy
  6. Cascading Agents for Gene Selection
  7. Reward Estimation:
  8. Iteration and Optmization
  9. Experiments
  10. Overall Comparison
  11. Study of the Impact of Each Technical Component
  12. Visualization Analysis of the Selected Gene Expressions
  13. Study of the Selected Gene Panel Size
  14. Related Work
  15. Conclusion Remarks
  16. ...and 15 more sections

Figures (14)

  • Figure 1: The overview of RiGPS framework. RiGPS consists of three main stages: (a) Gene Pre-Filtering to reduce the gene complexity; (b) Knowledge Injection for better start points; (c) Reinforced Key Gene Select Iteration to find the optimal selection.
  • Figure 2: Overall performance comparison: (a-c) Comparison of RiGPS with seven state-of-the-art gene panel selection methods for single-cell clustering in ARI, NMI, and SI. (d) Performance Rank of the gene panel selection methods in NMI.
  • Figure 3: Ablation studies of RiGPS in terms of NMI.
  • Figure 4: Visualization analysis of the Puram dataset. (a) t-SNE visualization of the original dataset; (b) t-SNE visualization of RiGPS optimized dataset; (c) expression heatmap of genes on the original dataset; (d) expression heatmap of genes selected by RiGPS.
  • Figure 5: Comparison between RiGPS and the runner-up regarding the selected gene panel size.
  • ...and 9 more figures