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Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis

Author

Listed:
  • Yanshuo Chen

    (CUHK
    Tsinghua University)

  • Yixuan Wang

    (CUHK
    HIT)

  • Yuelong Chen

    (CUHK
    The Chinese University of Hong Kong)

  • Yuqi Cheng

    (Weill Cornell Medicine)

  • Yumeng Wei

    (CUHK)

  • Yunxiang Li

    (CUHK)

  • Jiuming Wang

    (CUHK)

  • Yingying Wei

    (The Chinese University of Hong Kong)

  • Ting-Fung Chan

    (CUHK
    The Chinese University of Hong Kong)

  • Yu Li

    (CUHK
    The CUHK Shenzhen Research Institute)

Abstract

Single-cell RNA-sequencing has become a powerful tool to study biologically significant characteristics at explicitly high resolution. However, its application on emerging data is currently limited by its intrinsic techniques. Here, we introduce Tissue-AdaPtive autoEncoder (TAPE), a deep learning method connecting bulk RNA-seq and single-cell RNA-seq to achieve precise deconvolution in a short time. By constructing an interpretable decoder and training under a unique scheme, TAPE can predict cell-type fractions and cell-type-specific gene expression tissue-adaptively. Compared with popular methods on several datasets, TAPE has a better overall performance and comparable accuracy at cell type level. Additionally, it is more robust among different cell types, faster, and sensitive to provide biologically meaningful predictions. Moreover, through the analysis of clinical data, TAPE shows its ability to predict cell-type-specific gene expression profiles with biological significance. We believe that TAPE will enable and accelerate the precise analysis of high-throughput clinical data in a wide range.

Suggested Citation

  • Yanshuo Chen & Yixuan Wang & Yuelong Chen & Yuqi Cheng & Yumeng Wei & Yunxiang Li & Jiuming Wang & Yingying Wei & Ting-Fung Chan & Yu Li, 2022. "Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34550-9
    DOI: 10.1038/s41467-022-34550-9
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    References listed on IDEAS

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    1. Ellis Patrick & Mariko Taga & Ayla Ergun & Bernard Ng & William Casazza & Maria Cimpean & Christina Yung & Julie A Schneider & David A Bennett & Chris Gaiteri & Philip L De Jager & Elizabeth M Bradsha, 2020. "Deconvolving the contributions of cell-type heterogeneity on cortical gene expression," PLOS Computational Biology, Public Library of Science, vol. 16(8), pages 1-17, August.
    2. Daphne Tsoucas & Rui Dong & Haide Chen & Qian Zhu & Guoji Guo & Guo-Cheng Yuan, 2019. "Accurate estimation of cell-type composition from gene expression data," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    3. Bárbara Andrade Barbosa & Saskia D. Asten & Ji Won Oh & Arantza Farina-Sarasqueta & Joanne Verheij & Frederike Dijk & Hanneke W. M. Laarhoven & Bauke Ylstra & Juan J. Garcia Vallejo & Mark A. Wiel & Y, 2021. "Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
    4. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    5. Xuran Wang & Jihwan Park & Katalin Susztak & Nancy R. Zhang & Mingyao Li, 2019. "Bulk tissue cell type deconvolution with multi-subject single-cell expression reference," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
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    Cited by:

    1. Zehua Zeng & Yuqing Ma & Lei Hu & Bowen Tan & Peng Liu & Yixuan Wang & Cencan Xing & Yuanyan Xiong & Hongwu Du, 2024. "OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing," Nature Communications, Nature, vol. 15(1), pages 1-15, December.

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