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Gene Selection in a Single Cell Gene Space Based on D–S Evidence Theory

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A Correction to this article was published on 25 May 2022

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Abstract

If the samples, features and information values in a real-valued information system are cells, genes and gene expression values, respectively, then for convenience, this system is said to be a single cell gene space. In the era of big data, people are faced with high dimensional gene expression data with redundancy and noise causing its strong uncertainty. D–S evidence theory excels at tackling the problem of uncertainty, and its conditions to be met are weaker than Bayesian probability theory. Therefore, this paper studies the gene selection in a single cell gene space to remove noise and redundancy with D–S evidence theory. The distance between two cells in each gene is first defined. Then, the tolerance relation is established according to the defined distance. In addition, the belief and plausibility functions to grasp the uncertainty of a single cell gene space are introduced on the basis of the tolerance classes. Statistical analysis shows that they can effectively measure the uncertainty of a single cell gene space. Furthermore, several gene selection algorithms in a single cell gene space are presented using the proposed belief and plausibility. Finally, the performance of the proposed algorithm is compared to other algorithms on some published single-cell data sets. Experimental results and statistical tests show that the classification and clustering performance of the presented algorithm not only exceeds the other three state-of-the-art algorithms, but also its gene reduction rate is very high.

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Acknowledgements

The authors are very grateful to the reviewers and editors for their valuable comments and suggestions, which have helped us greatly improve the quality of the paper.

Funding

This work is supported by National Natural Science Foundation of China (11971420) and Doctoral research start project (CZ2021YJRC01).

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Authors and Affiliations

  1. Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Zhaowen Li, Pei Wang & Ching-Feng Wen

  2. School of Big Data and Artificial Intelligence, Chizhou University, Chizhou, 247000, Anhui, People’s Republic of China

    Qinli Zhang

  3. School of Mathematics and Information Science, Guangxi University, Nanning, 530004, Guangxi, People’s Republic of China

    Fang Liu

  4. School of Mathematics and Statistics, Yulin Normal University, Yulin, 537000, Guangxi, People’s Republic of China

    Yan Song

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  1. Zhaowen Li
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  4. Fang Liu
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  6. Ching-Feng Wen
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Corresponding authors

Correspondence to Qinli Zhang or Ching-Feng Wen.

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In the Original Publication, The affiliation of the author Ching-Feng Wen has been replaced as “Key Laboratory of Complex System Optimization and Big Data Processing in Department of Guangxi Education, Yulin Normal University, Yulin 537000, Guangxi, People’s Republic of China”.

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Li, Z., Zhang, Q., Wang, P. et al. Gene Selection in a Single Cell Gene Space Based on D–S Evidence Theory. Interdiscip Sci Comput Life Sci 14, 722–744 (2022). https://doi.org/10.1007/s12539-022-00518-y

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