Multi-Objective Genetic Algorithm for Cluster Analysis of Single-Cell Transcriptomes

Cells are the basic building blocks of human organisms, and the identification of their types and states in transcriptomic data is an important and challenging task. Many of the existing approaches to cell-type prediction are based on clustering methods that optimize only one criterion. In this pape...

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Bibliographic Details
Published in:Journal of personalized medicine 2023-01, Vol.13 (2), p.183
Main Authors: Zhao, Konghao, Grayson, Jason M, Khuri, Natalia
Format: Article
Language:English
Subjects:
Algorithms
Chromosomes
Cluster analysis
Computer applications
Datasets
Genetic analysis
Machine learning
Mutation
Precision medicine
Transcriptomes
Transcriptomics
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Summary:Cells are the basic building blocks of human organisms, and the identification of their types and states in transcriptomic data is an important and challenging task. Many of the existing approaches to cell-type prediction are based on clustering methods that optimize only one criterion. In this paper, a multi-objective Genetic Algorithm for cluster analysis is proposed, implemented, and systematically validated on 48 experimental and 60 synthetic datasets. The results demonstrate that the performance and the accuracy of the proposed algorithm are reproducible, stable, and better than those of single-objective clustering methods. Computational run times of multi-objective clustering of large datasets were studied and used in supervised machine learning to accurately predict the execution times of clustering of new single-cell transcriptomes.
ISSN:2075-4426
2075-4426
DOI:10.3390/jpm13020183