IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-58314-3.html
   My bibliography  Save this article

LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer

Author

Listed:
  • Siyu Han

    (German Research Center for Environmental Health
    Technical University of Munich
    Partner Neuherberg)

  • Shixiang Yu

    (German Research Center for Environmental Health
    Technical University of Munich
    Partner Neuherberg)

  • Mengya Shi

    (German Research Center for Environmental Health
    Technical University of Munich
    Partner Neuherberg)

  • Makoto Harada

    (German Research Center for Environmental Health
    Partner Neuherberg)

  • Jianhong Ge

    (German Research Center for Environmental Health
    Technical University of Munich
    Partner Neuherberg)

  • Jiesheng Lin

    (German Research Center for Environmental Health
    Pettenkofer School of Public Health)

  • Cornelia Prehn

    (German Research Center for Environmental Health)

  • Agnese Petrera

    (German Research Center for Environmental Health)

  • Ying Li

    (Jilin University)

  • Flora Sam

    (Lilly Corporate Center
    Boston University Chobanian & Avedisian School of Medicine)

  • Giuseppe Matullo

    (Turin University)

  • Jerzy Adamski

    (German Research Center for Environmental Health
    National University of Singapore
    University of Ljubljana)

  • Karsten Suhre

    (Education City
    Weill Cornell Medicine)

  • Christian Gieger

    (German Research Center for Environmental Health
    German Research Center for Environmental Health)

  • Stefanie M. Hauck

    (German Research Center for Environmental Health)

  • Christian Herder

    (Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf
    Partner Düsseldorf
    Heinrich-Heine-University Düsseldorf)

  • Michael Roden

    (Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf
    Partner Düsseldorf
    Heinrich-Heine-University Düsseldorf)

  • Francesco Paolo Casale

    (German Research Center for Environmental Health
    German Research Center for Environmental Health
    Technical University of Munich)

  • Na Cai

    (Technical University of Munich
    German Research Center for Environmental Health)

  • Annette Peters

    (Partner Neuherberg
    German Research Center for Environmental Health
    Pettenkofer School of Public Health
    Partner-Site Munich))

  • Rui Wang-Sattler

    (German Research Center for Environmental Health
    Partner Neuherberg
    Pettenkofer School of Public Health)

Abstract

Longitudinal multi-view omics data offer unique insights into the temporal dynamics of individual-level physiology, which provides opportunities to advance personalized healthcare. However, the common occurrence of incomplete views makes extrapolation tasks difficult, and there is a lack of tailored methods for this critical issue. Here, we introduce LEOPARD, an innovative approach specifically designed to complete missing views in multi-timepoint omics data. By disentangling longitudinal omics data into content and temporal representations, LEOPARD transfers the temporal knowledge to the omics-specific content, thereby completing missing views. The effectiveness of LEOPARD is validated on four real-world omics datasets constructed with data from the MGH COVID study and the KORA cohort, spanning periods from 3 days to 14 years. Compared to conventional imputation methods, such as missForest, PMM, GLMM, and cGAN, LEOPARD yields the most robust results across the benchmark datasets. LEOPARD-imputed data also achieve the highest agreement with observed data in our analyses for age-associated metabolites detection, estimated glomerular filtration rate-associated proteins identification, and chronic kidney disease prediction. Our work takes the first step toward a generalized treatment of missing views in longitudinal omics data, enabling comprehensive exploration of temporal dynamics and providing valuable insights into personalized healthcare.

Suggested Citation

  • Siyu Han & Shixiang Yu & Mengya Shi & Makoto Harada & Jianhong Ge & Jiesheng Lin & Cornelia Prehn & Agnese Petrera & Ying Li & Flora Sam & Giuseppe Matullo & Jerzy Adamski & Karsten Suhre & Christian , 2025. "LEOPARD: missing view completion for multi-timepoint omics data via representation disentanglement and temporal knowledge transfer," Nature Communications, Nature, vol. 16(1), pages 1-20, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58314-3
    DOI: 10.1038/s41467-025-58314-3
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-58314-3
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-025-58314-3?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. Suhas V. Vasaikar & Adam K. Savage & Qiuyu Gong & Elliott Swanson & Aarthi Talla & Cara Lord & Alexander T. Heubeck & Julian Reading & Lucas T. Graybuck & Paul Meijer & Troy R. Torgerson & Peter J. Sk, 2023. "A comprehensive platform for analyzing longitudinal multi-omics data," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    2. van Buuren, Stef & Groothuis-Oudshoorn, Karin, 2011. "mice: Multivariate Imputation by Chained Equations in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 45(i03).
    3. Kuhn, Max, 2008. "Building Predictive Models in R Using the caret Package," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 28(i05).
    4. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 287-296, July.
    5. Little, Roderick J A, 1988. "Missing-Data Adjustments in Large Surveys: Reply," Journal of Business & Economic Statistics, American Statistical Association, vol. 6(3), pages 300-301, July.
    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. Gerko Vink & Laurence E. Frank & Jeroen Pannekoek & Stef Buuren, 2014. "Predictive mean matching imputation of semicontinuous variables," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 68(1), pages 61-90, February.
    2. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    3. Saeideh Kamgar & Florian Meinfelder & Ralf Münnich & Hamidreza Navvabpour, 2020. "Estimation within the new integrated system of household surveys in Germany," Statistical Papers, Springer, vol. 61(5), pages 2091-2117, October.
    4. Marco Geraci & Alexander McLain, 2018. "Multiple Imputation for Bounded Variables," Psychometrika, Springer;The Psychometric Society, vol. 83(4), pages 919-940, December.
    5. Jensen, Are & Clausen, Tommy H., 2017. "Origins and emergence of exploration and exploitation capabilities in new technology-based firms," Technological Forecasting and Social Change, Elsevier, vol. 120(C), pages 163-175.
    6. Ann-Marie Küchler & Dana Schultchen & Tim Dretzler & Morten Moshagen & David D. Ebert & Harald Baumeister, 2023. "A Three-Armed Randomized Controlled Trial to Evaluate the Effectiveness, Acceptance, and Negative Effects of StudiCare Mindfulness, an Internet- and Mobile-Based Intervention for College Students with," IJERPH, MDPI, vol. 20(4), pages 1-23, February.
    7. Jürgen Kampf & Iryna Dykun & Tienush Rassaf & Amir Abbas Mahabadi, 2025. "A comparison of various imputation algorithms for missing data," PLOS ONE, Public Library of Science, vol. 20(5), pages 1-16, May.
    8. Gerko Vink & Stef van Buuren, 2013. "Multiple Imputation of Squared Terms," Sociological Methods & Research, , vol. 42(4), pages 598-607, November.
    9. Renate S M Buisman & Katharina Pittner & Marieke S Tollenaar & Jolanda Lindenberg & Lisa J M van den Berg & Laura H C G Compier-de Block & Joost R van Ginkel & Lenneke R A Alink & Marian J Bakermans-K, 2020. "Intergenerational transmission of child maltreatment using a multi-informant multi-generation family design," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-23, March.
    10. Adel Bosch & Steven F. Koch, 2021. "Individual and Household Debt: Does Imputation Choice Matter?," Working Papers 202141, University of Pretoria, Department of Economics.
    11. Williams, Randi M. & Zhang, Jing & Woodard, Nathaniel & Slade, Jimmie & Santos, Sherie Lou Zara & Knott, Cheryl L., 2020. "Development and validation of an instrument to assess institutionalization of health promotion in faith-based organizations," Evaluation and Program Planning, Elsevier, vol. 79(C).
    12. Mingyang Cai & Gerko Vink, 2022. "A note on imputing squares via polynomial combination approach," Computational Statistics, Springer, vol. 37(5), pages 2185-2201, November.
    13. Ahfock, Daniel & Pyne, Saumyadipta & McLachlan, Geoffrey J., 2022. "Statistical file-matching of non-Gaussian data: A game theoretic approach," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    14. Ralf Münnich & Siegfried Gabler & Christian Bruch & Jan Pablo Burgard & Tobias Enderle & Jan-Philipp Kolb & Thomas Zimmermann, 2015. "Tabellenauswertungen im Zensus unter Berücksichtigung fehlender Werte," AStA Wirtschafts- und Sozialstatistisches Archiv, Springer;Deutsche Statistische Gesellschaft - German Statistical Society, vol. 9(3), pages 269-304, December.
    15. Michael D. Teter & Johannes O. Royset & Alexandra M. Newman, 2019. "Modeling uncertainty of expert elicitation for use in risk-based optimization," Annals of Operations Research, Springer, vol. 280(1), pages 189-210, September.
    16. Jana Emmenegger & Ralf Münnich & Jannik Schaller, 2022. "Evaluating Data Fusion Methods to Improve Income Modelling," Research Papers in Economics 2022-03, University of Trier, Department of Economics.
    17. Kristian Kleinke & Jost Reinecke, 2013. "Multiple imputation of incomplete zero-inflated count data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(3), pages 311-336, August.
    18. Rabea Aschenbruck & Gero Szepannek & Adalbert F. X. Wilhelm, 2023. "Imputation Strategies for Clustering Mixed-Type Data with Missing Values," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 2-24, April.
    19. Joost Ginkel & Pieter Kroonenberg, 2014. "Using Generalized Procrustes Analysis for Multiple Imputation in Principal Component Analysis," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 242-269, July.
    20. Verbeek, M.J.C.M. & Nijman, T.E., 1992. "Incomplete panels and selection bias : A survey," Discussion Paper 1992-7, Tilburg University, Center for Economic Research.

    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:16:y:2025:i:1:d:10.1038_s41467-025-58314-3. 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.