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ScDA: A Denoising AutoEncoder Based Dimensionality Reduction for Single-cell RNA-seq Data

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Bioinformatics Research and Applications (ISBRA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13064))

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Abstract

Single-cell RNA-seq (scRNA-seq) data has provided a higher resolution of cellular heterogeneity. However, scRNA-seq data also brings some computational challenges for its high-dimension, high-noise, and high-sparseness. The dimension reduction is a crucial way to denoise and greatly reduce the computational complexity by representing the original data in a low-dimensional space. In this study, to achieve an accurate low-dimension representation, we proposed a denoising AutoEncoder based dimensionality reduction method for scRNA-seq data (ScDA), combining the denoising function with the AutoEncoder. ScDA is a deep unsupervised generative model, which models the dropout events and denoises the scRNA-seq data. Meanwhile, ScDA can reveal the nonlinear feature extraction of the original data through maximum distribution similarity before and after dimensionality reduction. Tested on 16 scRNA-seq datasets, ScDA provides superior average performances, and especially superior performances in large-scale datasets compared with 3 clustering methods.

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Authors’ Contributions

Conceptualization, X.Z., X.P., and J.W.; Methodology, X.Z., Y.L., and X.P.; Software, Y.L., and J.L.; Writing-Original Draft Preparation, X.Z., J.W., and X.P.; Visualization, Y.L.; Funding Acquisition, X.Z., and X.P.

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Funding

This research was supported by the National Natural Science Foundation of China (No. 61762087, 61702555, 61772557), Hunan Provincial Science and Technology Program (2018WK4001), 111 Project (No. B18059), Guangxi Natural Science Foundation (No. 2018JJA170175).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Yulin Normal University, Yulin, 537000, Guangxi, China

    Xiaoshu Zhu, Yongchang Lin & Jian Li

  2. Hunan Provincial Key Lab On Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, 400083, Hunan, China

    Jianxin Wang

  3. Center for Medical Genetics School of Life Sciences, Central South University, Changsha, 400083, Hunan, China

    Xiaoqing Peng

Authors
  1. Xiaoshu Zhu
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  2. Yongchang Lin
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  3. Jian Li
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  4. Jianxin Wang
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  5. Xiaoqing Peng
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Corresponding author

Correspondence to Xiaoqing Peng .

Editor information

Editors and Affiliations

  1. Shenzhen Institutes of Advanced Technology, Shenzhen, China

    Yanjie Wei

  2. Central South University, Changsha, China

    Min Li

  3. Georgia State University, Atlanta, GA, USA

    Pavel Skums

  4. Georgia State University, Atlanta, GA, USA

    Zhipeng Cai

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Zhu, X., Lin, Y., Li, J., Wang, J., Peng, X. (2021). ScDA: A Denoising AutoEncoder Based Dimensionality Reduction for Single-cell RNA-seq Data. In: Wei, Y., Li, M., Skums, P., Cai, Z. (eds) Bioinformatics Research and Applications. ISBRA 2021. Lecture Notes in Computer Science(), vol 13064. Springer, Cham. https://doi.org/10.1007/978-3-030-91415-8_45

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  • DOI: https://doi.org/10.1007/978-3-030-91415-8_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91414-1

  • Online ISBN: 978-3-030-91415-8

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