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A Link-Based Ensemble Cluster Approach for Identification of Cell Types

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Intelligent Computing Theories and Application (ICIC 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12837))

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Abstract

Clustering is a necessary step in analyzing single-cell RNA-seq (sRNA-seq) data to illuminate the complexity of the tissue, including the number of cell types and transcriptome characteristics of each cell type. However, the clustering results obtained from different single-cell clustering methods are often different, and sometimes even contradictory conclusions are drawn. Biologists often cannot obtain the correct clustering results. To overcome this challenge, researchers have developed an integrated learning strategy that can effectively solve this problem. Here, we propose a new unsupervised ensemble clustering method LE2CT. First, we obtained five clustering results that have been used in the scRNA-seq data clustering method. Second, we construct a similarity consensus matrix based on multiple clustering solutions. Finally, hierarchical clustering is used as a consensus function to generate final data partitions. We identified cell clusters on twelve scRNA-seq benchmark data sets and used the adjusted RAND index (ARI) and normalized mutual information (NMI) to measure the accuracy of clustering. The experimental results are encouraging. Compared with the classic single clustering method, LE2CT has higher clustering accuracy and stronger robustness in various data sets, which shows that LE2CT has a competitive advantage compared with existing methods.

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Acknowledgements

This work was supported by Natural Science Foundation of China (Grant No. 61972141) and Natural Science Foundation of Hunan Province, China (Grant No. 2018JJ2053).

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Authors and Affiliations

  1. College of Computer Science and Electronic Engineering, Hunan University, Changsha, China

    Xinguo Lu, Yan Gao, Daoxu Tang & Yue Yuan

Authors
  1. Xinguo Lu
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  2. Yan Gao
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  3. Daoxu Tang
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  4. Yue Yuan
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Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Shenzhen University, Shenzhen, China

    Jianqiang Li

  4. Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russia

    Valeriya Gribova

  5. Department of Computer Science, Liverpool John Moores University, Liverpool, UK

    Abir Hussain

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Lu, X., Gao, Y., Tang, D., Yuan, Y. (2021). A Link-Based Ensemble Cluster Approach for Identification of Cell Types. In: Huang, DS., Jo, KH., Li, J., Gribova, V., Hussain, A. (eds) Intelligent Computing Theories and Application. ICIC 2021. Lecture Notes in Computer Science(), vol 12837. Springer, Cham. https://doi.org/10.1007/978-3-030-84529-2_54

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  • DOI: https://doi.org/10.1007/978-3-030-84529-2_54

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84528-5

  • Online ISBN: 978-3-030-84529-2

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