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SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data

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Abstract

Clustering is a common method to identify cell types in single cell analysis, but the increasing size of scRNA-seq datasets brings challenges to single cell clustering. Therefore, it is an urgent need to design a faster and more accurate clustering method for large-scale scRNA-seq data. In this paper, we proposed a new method for single cell clustering. First, a count matrix is constructed through normalization and gene filtration. Second, the raw data of gene expression matrix are projected to feature space constructed by secondary construction of feature space based on UMAP (Uniform Manifold Approximation and Projection). Third, the low-dimensional matrix on the feature space is randomly divided into two sub-matrices according to a certain proportion for clustering and classifying, respectively. Finally, one subset is clustered by k-means algorithm and then the other subset is classified by k-nearest neighbor algorithm based on clustering results. Experimental results show that our method can cluster the scRNA-seq datasets effectively.

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Acknowledgements

This work was supported by grants from the Xinjiang Autonomous Region University Research Program (No. XJEDU2019Y002) and the National Natural Science Foundation of China (No. U19A2064, 61873001).

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Author notes

  1. Hai-Yun Wang and Jian-ping Zhao authors contributed equally to this work.

Authors and Affiliations

  1. College of Mathematics and System Sciences, Xinjiang University, Urumqi, China

    Hai-Yun Wang, Jian-ping Zhao & Chun-Hou Zheng

  2. Institute of Mathematics and Physics, Xinjiang University, Urumqi, China

    Jian-ping Zhao

  3. College of Computer Science and Technology, Anhui University, Hefei, China

    Chun-Hou Zheng

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  1. Hai-Yun Wang
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Correspondence to Jian-ping Zhao or Chun-Hou Zheng.

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Wang, HY., Zhao, Jp. & Zheng, CH. SUSCC: Secondary Construction of Feature Space based on UMAP for Rapid and Accurate Clustering Large-scale Single Cell RNA-seq Data. Interdiscip Sci Comput Life Sci 13, 83–90 (2021). https://doi.org/10.1007/s12539-020-00411-6

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  • DOI: https://doi.org/10.1007/s12539-020-00411-6

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