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Reference Vector-guided Evolutionary Algorithm for cluster analysis of single-cell transcriptomes
Fernando M Rodríguez-Bejarano1, Miguel A Vega-Rodríguez1, Sergio Santander-Jiménez1
1Escuela Politécnica, Universidad de Extremadura(1), Campus Universitario s/n, 10003 Cáceres, Spain.
Computer Methods and Programs in Biomedicine
|June 11, 2025
Summary
This study introduces RVEA-CAST, a novel multi-objective optimization algorithm for clustering single-cell RNA sequencing (scRNA-seq) data. RVEA-CAST effectively identifies distinct cell populations, outperforming existing methods in accuracy and biological relevance.
Area of Science:
- Computational biology
- Genomics
- Bioinformatics
Background:
- Single-cell RNA sequencing (scRNA-seq) provides high-resolution transcriptomic data.
- Clustering scRNA-seq data is essential for identifying cell populations.
- Existing clustering methods face challenges due to conflicting optimization objectives.
Purpose of the Study:
- To develop a multi-objective optimization approach for scRNA-seq data clustering.
- To address the challenge of clustering scRNA-seq data by considering multiple conflicting objectives.
Main Methods:
- Proposes Reference Vector-guided Evolutionary Algorithm for Cluster Analysis of Single-cell Transcriptomes (RVEA-CAST).
- Optimizes clustering deviation, compactness, and Davies-Bouldin index using problem-aware mutation operators.
- Employs a multi-objective search engine guided by reference vectors.
Main Results:
- RVEA-CAST demonstrates superior performance and robustness on ten real scRNA-seq datasets.
- Achieved statistically significant improvements in Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI) by up to 66.7% and 261.5%, respectively.
- Showcased high agreement between predicted and actual cell populations, confirming biological relevance.
Conclusions:
- RVEA-CAST is an effective and versatile tool for scRNA-seq data clustering.
- Outperforms existing methods in both standard evaluation metrics and biological relevance.
- Applicable across diverse biological scenarios for accurate cell population identification.
