scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data
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Published:2023-10-03
Issue:12
Volume:6
Page:e202302103
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ISSN:2575-1077
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Container-title:Life Science Alliance
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language:en
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Short-container-title:Life Sci. Alliance
Author:
Wang Zongqin1,
Xie Xiaojun12,
Liu Shouyang3,
Ji Zhiwei12ORCID
Affiliation:
1. College of Artificial Intelligence, Nanjing Agricultural University
2. Center for Data Science and Intelligent Computing, Nanjing Agricultural University
3. Academy for Advanced Interdisciplinary Studies, Nanjing Agricultural University
Abstract
Single-cell RNA sequencing (scRNA-seq) enables researchers to reveal previously unknown cell heterogeneity and functional diversity, which is impossible with bulk RNA sequencing. Clustering approaches are widely used for analyzing scRNA-seq data and identifying cell types and states. In the past few years, various advanced computational strategies emerged. However, the low generalization and high computational cost are the main bottlenecks of existing methods. In this study, we established a novel computational framework, scFseCluster, for scRNA-seq clustering analysis. scFseCluster incorporates a metaheuristic algorithm (Feature Selection based on Quantum Squirrel Search Algorithm) to extract the optimal gene set, which largely guarantees the performance of cell clustering. We conducted simulation experiments in several aspects to verify the performance of the proposed approach. scFseCluster performed very well on eight benchmark scRNA-seq datasets because of the optimal gene sets obtained using the Feature Selection based on Quantum Squirrel Search Algorithm. The comparative study demonstrated the significant advantages of scFseCluster over seven State-of-the-Art algorithms. In addition, our analysis shows that feature selection on high-variable genes can significantly improve clustering performance. In conclusion, our study demonstrates that scFseCluster is a highly versatile tool for enhancing scRNA-seq data clustering analysis.
Funder
JST | Natural Science Foundation of Jiangsu Province
Fundamental Research Funds for the Central Universities
Agricultural Science and Technology Innovation Foundation of Jiangsu Province
Nanjing Agricultural University
Publisher
Life Science Alliance, LLC
Subject
Health, Toxicology and Mutagenesis,Plant Science,Biochemistry, Genetics and Molecular Biology (miscellaneous),Ecology