IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v13y2022i1d10.1038_s41467-022-34550-9.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-022-34550-9
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-022-34550-9?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhenzhen Xun & Xinyu Ding & Yao Zhang & Benyan Zhang & Shujing Lai & Duowu Zou & Junke Zheng & Guoqiang Chen & Bing Su & Leng Han & Youqiong Ye, 2023. "Reconstruction of the tumor spatial microenvironment along the malignant-boundary-nonmalignant axis," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. Khoa A. Tran & Venkateswar Addala & Rebecca L. Johnston & David Lovell & Andrew Bradley & Lambros T. Koufariotis & Scott Wood & Sunny Z. Wu & Daniel Roden & Ghamdan Al-Eryani & Alexander Swarbrick & E, 2023. "Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures," Nature Communications, Nature, vol. 14(1), pages 1-17, December.
    3. Gavin J. Sutton & Daniel Poppe & Rebecca K. Simmons & Kieran Walsh & Urwah Nawaz & Ryan Lister & Johann A. Gagnon-Bartsch & Irina Voineagu, 2022. "Comprehensive evaluation of deconvolution methods for human brain gene expression," Nature Communications, Nature, vol. 13(1), pages 1-18, December.
    4. Xiaoyu Song & Jiayi Ji & Joseph H. Rothstein & Stacey E. Alexeeff & Lori C. Sakoda & Adriana Sistig & Ninah Achacoso & Eric Jorgenson & Alice S. Whittemore & Robert J. Klein & Laurel A. Habel & Pei Wa, 2023. "MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    5. Tan Wang & L. Jeff Hong, 2023. "Large-Scale Inventory Optimization: A Recurrent Neural Networks–Inspired Simulation Approach," INFORMS Journal on Computing, INFORMS, vol. 35(1), pages 196-215, January.
    6. Léon Faure & Bastien Mollet & Wolfram Liebermeister & Jean-Loup Faulon, 2023. "A neural-mechanistic hybrid approach improving the predictive power of genome-scale metabolic models," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    7. Claudia Quinteros-Cartaya & Guillermo Solorio-Magaña & Francisco Javier Núñez-Cornú & Felipe de Jesús Escalona-Alcázar & Diana Núñez, 2023. "Microearthquakes in the Guadalajara Metropolitan Zone, Mexico: evidence from buried active faults in Tesistán Valley, Zapopan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 116(3), pages 2797-2818, April.
    8. Jakob Hartmann & Thomas Bajaj & Joy Otten & Claudia Klengel & Tim Ebert & Anne-Kathrin Gellner & Ellen Junglas & Kathrin Hafner & Elmira A. Anderzhanova & Fiona Tang & Galen Missig & Lindsay Rexrode &, 2024. "SKA2 regulated hyperactive secretory autophagy drives neuroinflammation-induced neurodegeneration," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    9. López Pérez, Mario & Mansilla Corona, Ricardo, 2022. "Ordinal synchronization and typical states in high-frequency digital markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    10. Jessica M. Vanslambrouck & Sean B. Wilson & Ker Sin Tan & Ella Groenewegen & Rajeev Rudraraju & Jessica Neil & Kynan T. Lawlor & Sophia Mah & Michelle Scurr & Sara E. Howden & Kanta Subbarao & Melissa, 2022. "Enhanced metanephric specification to functional proximal tubule enables toxicity screening and infectious disease modelling in kidney organoids," Nature Communications, Nature, vol. 13(1), pages 1-23, December.
    11. Kiran Krishnamachari & Dylan Lu & Alexander Swift-Scott & Anuar Yeraliyev & Kayla Lee & Weitai Huang & Sim Ngak Leng & Anders Jacobsen Skanderup, 2022. "Accurate somatic variant detection using weakly supervised deep learning," Nature Communications, Nature, vol. 13(1), pages 1-8, December.
    12. Lauren L. Porter & Allen K. Kim & Swechha Rimal & Loren L. Looger & Ananya Majumdar & Brett D. Mensh & Mary R. Starich & Marie-Paule Strub, 2022. "Many dissimilar NusG protein domains switch between α-helix and β-sheet folds," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    13. Matthew Rosenblatt & Link Tejavibulya & Rongtao Jiang & Stephanie Noble & Dustin Scheinost, 2024. "Data leakage inflates prediction performance in connectome-based machine learning models," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    14. Jackie Grant & Mark Hindmarsh & Sergey E. Koposov, 2022. "The distribution of loss to future USS pensions due to the UUK cuts of April 2022," Papers 2206.06201, arXiv.org.
    15. Sayedali Shetab Boushehri & Katharina Essig & Nikolaos-Kosmas Chlis & Sylvia Herter & Marina Bacac & Fabian J. Theis & Elke Glasmacher & Carsten Marr & Fabian Schmich, 2023. "Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    16. 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.
    17. Keyong Sun & Runda Xu & Fuhai Ma & Naixue Yang & Yang Li & Xiaofeng Sun & Peng Jin & Wenzhe Kang & Lemei Jia & Jianping Xiong & Haitao Hu & Yantao Tian & Xun Lan, 2022. "scRNA-seq of gastric tumor shows complex intercellular interaction with an alternative T cell exhaustion trajectory," Nature Communications, Nature, vol. 13(1), pages 1-19, December.
    18. Shukla, Mohak & Thakur, Ajay D., 2022. "An Enquiry on similarities between Renormalization Group and Auto-Encoders using Transfer Learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    19. Khaled Akkad & David He, 2023. "A dynamic mode decomposition based deep learning technique for prognostics," Journal of Intelligent Manufacturing, Springer, vol. 34(5), pages 2207-2224, June.
    20. Romain Fournier & Zoi Tsangalidou & David Reich & Pier Francesco Palamara, 2023. "Haplotype-based inference of recent effective population size in modern and ancient DNA samples," Nature Communications, Nature, vol. 14(1), pages 1-13, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-34550-9. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.