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Gene selection and clustering of single-cell data based on Fisher score and genetic algorithm

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Abstract

Huge amounts of genes in single-cell RNA sequencing (scRNA-seq) data may influence the performance of data clustering. To obtain high-quality genes for data clustering, the study proposes a novel gene selection algorithm based on Fisher score and genetic algorithms with dynamic crossover (abbreviated as FDCGA). To reduce time and space complexity, FDCGA first employs Fisher score to gain the preliminary candidate genes and then utilizes genetic algorithms with dynamic crossover to select beneficial genes to data clustering and analysis. The experimental results conducted on several publicly real-world scRNA-seq datasets demonstrate that FDCGA outperforms the other several competitors in terms of both NMI and ARI metrics and possesses significant optimization performances. The experimental convergence shows that the fitness of FDCGA can increase and converge to a fixed state versus the number of iterations. The statistical analysis demonstrates that FDCGA statistically significantly outperforms the other competing methods.

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Acknowledgements

We would like to thank anonymous reviewers and all authors of the cited references.

Funding

This research was supported in part by the Natural Science Foundation of Guangxi Province grant number 2021GXNSFAA220076, National Natural Science Foundation of China Grant No. 62141207, and Key Fields Project of Universities in Guangdong Province Grant Nos. 2021ZDZX4109, 2020ZDZX3119.

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Author notes

  1. Junhong Feng and Jie Zhang have contributed equally to this work.

Authors and Affiliations

  1. School of Computer and Information Engineering, Guangdong Songshan Polytechnic, Shaoguan, 512126, Guangdong, People’s Republic of China

    Junhong Feng & Jie Zhang

  2. School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Jie Zhang & Xiaoshu Zhu

  3. Department of Computer Science and Information Engineering, National Chin-Yi University of Technology, Taichung, Taiwan

    Jian-Hong Wang

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  1. Junhong Feng
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Contributions

JHF and JZ designed the study; XSZ implemented the experiments; JHW addressed the data analysis; JHF and JZ wrote and revised the manuscript. All authors reviewed the manuscript.

Corresponding authors

Correspondence to Jie Zhang or Xiaoshu Zhu.

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Conflict of interest

The authors declare no potential conflicts of interest with respect to the study, authorship, and publication of this article.

Data availability

The datasets supporting this study are publicly available and can be obtained from EMBL-EBI (https://www.ebi.ac.uk/) or the NCBI Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/geo/).

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Feng, J., Zhang, J., Zhu, X. et al. Gene selection and clustering of single-cell data based on Fisher score and genetic algorithm. J Supercomput 79, 7067–7093 (2023). https://doi.org/10.1007/s11227-022-04920-7

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