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

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.

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Publication Details

Journal
Nature Communications
Published
2025-04-05
DOI
https://doi.org/10.1038/s41467-025-58314-3
Citations
3
Primary Topic
Health, Environment, Cognitive Aging
Type
article
Field-Weighted Citation Impact
3.60

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article

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

Siyu Han, Mengya Shi, Shixiang Yu, Francesco Paolo Casale et al.
3 citations
Nature Communications
Health, Environment, Cognitive Aging
3.60
article

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

Siyu Han, Mengya Shi, Shixiang Yu, Francesco Paolo Casale, Cornelia Prehn, Michael Roden, Agnese Petrera, Stefanie M. Hauck, Karsten Suhre, Na Cai, Makoto Harada, Flora Sam, Jerzy Adamski, Rui Wang‐Sattler, Christian Herder, Christian Gieger, Annette Peters, Giuseppe Matullo, Ying Li, Jie-sheng Lin, Jianhong Ge
article en
3 citations

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.

Nature CommunicationsVol. 16(1)
Boston University (US), University of Ljubljana (SI), National University of Singapore (SG), Eli Lilly (United States) (US), Zimmer Biomet (Netherlands) (NL), Jilin University (CN), Cornell University (US), Helmholtz Zentrum München (DE), Düsseldorf University Hospital (DE), German Centre for Cardiovascular Research (DE), Deutsches Diabetes-Zentrum e.V. (DE), Weill Cornell Medical College in Qatar (QA), German Center for Diabetes Research (DE), Weill Cornell Medicine (US), Heinrich Heine University Düsseldorf (DE), University of Turin (IT), Technical University of Munich (DE), Ludwig-Maximilians-Universität München (DE)
Qatar Foundation, European Federation of Pharmaceutical Industries and Associations, European Commission, Fonds National de la Recherche Luxembourg, Bundesministerium für Bildung und Forschung, Bundesministerium für Gesundheit, Innovative Medicines Initiative, Weill Cornell Medical College, Qatar National Research Fund, Weill Cornell Medicine - Qatar, Helmholtz Zentrum München
Openalex Percentile: Top 9%
Health, Environment, Cognitive Aging
3.60
DOIOpen Access PDFOpenAlex
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