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Fast and precise single-cell data analysis using a hierarchical autoencoder

Author

Listed:
  • Duc Tran

    (University of Nevada Reno)

  • Hung Nguyen

    (University of Nevada Reno)

  • Bang Tran

    (University of Nevada Reno)

  • Carlo La Vecchia

    (University of Milan)

  • Hung N. Luu

    (Division of Cancer Control and Population Sciences, Hillman Cancer Center, University of Pittsburgh Medical Center
    University of Pittsburgh Graduate School of Public Health)

  • Tin Nguyen

    (University of Nevada Reno)

Abstract

A primary challenge in single-cell RNA sequencing (scRNA-seq) studies comes from the massive amount of data and the excess noise level. To address this challenge, we introduce an analysis framework, named single-cell Decomposition using Hierarchical Autoencoder (scDHA), that reliably extracts representative information of each cell. The scDHA pipeline consists of two core modules. The first module is a non-negative kernel autoencoder able to remove genes or components that have insignificant contributions to the part-based representation of the data. The second module is a stacked Bayesian autoencoder that projects the data onto a low-dimensional space (compressed). To diminish the tendency to overfit of neural networks, we repeatedly perturb the compressed space to learn a more generalized representation of the data. In an extensive analysis, we demonstrate that scDHA outperforms state-of-the-art techniques in many research sub-fields of scRNA-seq analysis, including cell segregation through unsupervised learning, visualization of transcriptome landscape, cell classification, and pseudo-time inference.

Suggested Citation

  • Duc Tran & Hung Nguyen & Bang Tran & Carlo La Vecchia & Hung N. Luu & Tin Nguyen, 2021. "Fast and precise single-cell data analysis using a hierarchical autoencoder," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-21312-2
    DOI: 10.1038/s41467-021-21312-2
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    Cited by:

    1. Scott R. Tyler & Daniel Lozano-Ojalvo & Ernesto Guccione & Eric E. Schadt, 2024. "Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    2. Thomas Hu & Mayar Allam & Shuangyi Cai & Walter Henderson & Brian Yueh & Aybuke Garipcan & Anton V. Ievlev & Maryam Afkarian & Semir Beyaz & Ahmet F. Coskun, 2023. "Single-cell spatial metabolomics with cell-type specific protein profiling for tissue systems biology," Nature Communications, Nature, vol. 14(1), pages 1-20, December.
    3. Yasa Baig & Helena R. Ma & Helen Xu & Lingchong You, 2023. "Autoencoder neural networks enable low dimensional structure analyses of microbial growth dynamics," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    4. Zhuohan Yu & Yanchi Su & Yifu Lu & Yuning Yang & Fuzhou Wang & Shixiong Zhang & Yi Chang & Ka-Chun Wong & Xiangtao Li, 2023. "Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    5. Hyun Kim & Won Chang & Seok Joo Chae & Jong-Eun Park & Minseok Seo & Jae Kyoung Kim, 2024. "scLENS: data-driven signal detection for unbiased scRNA-seq data analysis," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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