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reference vector-guided evolutionary algorithm for cluster analysis of single-cell transcriptomes

Computer Methods and Programs in Biomedicine 2025年 269卷

作者： Fernando M. Rodríguez-Bejarano Miguel A. Vega-Rodríguez Sergio Santander-Jiménez

Background and Objective: Single-cell RNA-sequencing (scRNA-seq) has revolutionized transcriptomic studies by providing detailed insights into gene expression profiles at the single-cell level. This technology allows researchers to capture expression patterns of thousands of genes across hundreds or thousands of individual cells. Clustering is a crucial step in the analysis of scRNA-seq data, since it enables the identification of distinct cell populations based on their transcriptomic profiles and serves as a foundation for downstream analysis. Given that clustering scRNA-seq data is a challenging task that involves different conflicting objectives, our goal is to tackle it from a multi-objective optimization perspective. Methods: This study proposes a reference vector-guided evolutionary algorithm for Cluster Analysis of Single-cell Transcriptomes (RVEA-CAST) to address the clustering task as a multi-objective optimization problem. Our approach considers three objectives to optimize: clustering deviation, clustering compactness, and the Davies–Bouldin index. The algorithmic design of RVEA-CAST incorporates three problem-aware mutation operators specifically designed to improve each objective, which are orchestrated under a multi-objective search engine based on the use of reference vectors. Results: RVEA-CAST is evaluated on ten real scRNA-seq datasets using standard clustering evaluation metrics, such as Normalized Mutual Information (NMI) and Adjusted Rand Index (ARI). The attained results reveal the improved performance and robustness of the proposed approach compared to other previously proposed methods. Specifically, statistically significant improvements of up to 66.7% and 261.5% were achieved for NMI and ARI, respectively. Furthermore, the analysis of differentially expressed genes in the predicted and real clusters showcased greater agreement of our solutions with actual cell populations, underscoring the biological relevance of our approach. Conclusions:

关键词： Multi-objective optimization reference vector-guided evolutionary algorithm Cluster analysis Single-cell transcriptome ScRNA-seq

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Improved reference vector guided Differential Evolution algorithm for Many-Objective Optimization 2020

Improved Reference Vector Guided Differential Evolution Algo...

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5th International Conference on Mathematics and Artificial Intelligence (ICMAI)

作者： Lin, Jie Zheng, Shaoyong Long, Yunliang Sun Yat Sen Univ Sch Elect & Informat Technol Guangzhou 510000 Peoples R China

ISBN: (纸本)9781450377072

Most of the existing evolutionary algorithms to deal with many-objective problems are based on the enhancing of selection strategy. Among them, the reference vector-guided evolutionary algorithm (RVEA) achieves excellent performance. In this paper, a new search engine is combined with RVEA to achieve further performance enhancement of the differential evolutionary (DE) algorithm. In the optimization process of differential evolution algorithm on many-objective problems, improving convergence and maintaining diversity are two different optimization directions, and it is usually difficult to maintain a balance between them. To solve this problem, a new search engine based on DE is proposed. The proposed search engine is implemented based on a cooperative scheme of local and global search strategies. In the local search, the population is divided into several subpopulations, each of which evolves independently using the proposed mutation strategy. The distance between the individuals in each sub-population is relatively close. Therefore, it has a strong exploitation capability, and will not make the population lose diversity. Meanwhile, the selection strategy of RVEA enables the population to maintain diversity, and the DE/rand/1 utilized in global search is sufficient to keep a strong exploration capability. Therefore, the proposed approach can achieve a good balance between exploration and exploitation. The experimental results show that the proposed algorithm performs well in manyobjective optimizations up to more than 10 objectives.

关键词： Differential evolution algorithm local search global search reference vector-guided evolutionary algorithm

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