Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
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Abstract
Despite the emergence of experimental methods for simultaneous measurement of multiple omics modalities in single cells, most single-cell datasets include only one modality. A major obstacle in integrating omics data from multiple modalities is that different omics layers typically have distinct feature spaces. Here, we propose a computational framework called GLUE (graph-linked unified embedding), which bridges the gap by modeling regulatory interactions across omics layers explicitly. Systematic benchmarking demonstrated that GLUE is more accurate, robust and scalable than state-of-the-art tools for heterogeneous single-cell multi-omics data. We applied GLUE to various challenging tasks, including triple-omics integration, integrative regulatory inference and multi-omics human cell atlas construction over millions of cells, where GLUE was able to correct previous annotations. GLUE features a modular design that can be flexibly extended and enhanced for new analysis tasks. The full package is available online at https://github.com/gao-lab/GLUE .
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Various methods have been developed to integrate single cells with different omics, uncover regulatory networks of single-cell datasets, or combine gene expression profiles with spatial information.
“Various methods have been developed to integrate single cells with different omics [22], uncover the regulatory networks of single-cell datasets [23], or combine gene expression profiles with spatial information [24].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [22]
Cao et al. introduced the GLUE framework for integrating unpaired single-cell multi-omics datasets guided by a linked graph representation of omics features and inferred regulatory interactions across modalities.
“Cao et al. introduced the graph-linked unified embedding (GLUE) framework for integrating unpaired single-cell multi-omics datasets guided by a linked graph representation of omics features and inferred the regulatory interactions across modalities [22].”
From Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis (2025) · cites this paper as [22]
In GLUE, a graph variational autoencoder learns feature embeddings from prior biological knowledge, used to reconstruct and link omics data through an inner product with cell embeddings aligned in subsequent steps.
“In GLUE, a graph variational autoencoder learns feature embeddings from previous biological knowledge, which is then used to reconstruct and link omics data through an inner product with cell embeddings that are aligned further in subsequent steps169.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [169]
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scGPT: Towards Building a Foundation Model for Single-Cell Multi-omics Using Generative AI
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BackgroundMethodInfluentialBest practices for single-cell analysis across modalities
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Challenges in AI-driven Biomedical Multimodal Data Fusion and Analysis
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Advancing biological understanding of cellular senescence with computational multiomics
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- Authors
- Cao, Z.-J., & Gao, G.
- Affiliations
- Peking University (CN)
- Corresponding author
- Gao, Ge
- ORCID
- Venue
- Nature Biotechnology
- Published
- 2022-05-02
- Volume
- 40
- Issue
- 10
- Pages
- 1458-1466
- ISSN
- 0733-222X (ISSN-L)
- 1087-0156
- 1546-1696
- PMC
- PMC9546775
- OpenAlex
- W4225278475
- Cited by
- 616
- References
- 86
- Citations by year
- 2026: 174 · 2025: 170 · 2024: 143 · 2023: 109 · 2022: 19 · 2021: 1
- FWCI
- 36.25
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Nature Biotechnology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Peking University
- Servier (CC BY 3.0)
- National High-tech Research and Development Program (2015AA020108)
- National Key Research and Development Program of China (2016YFC0901603, 2015AA020108)
- National Program for Support of Top-notch Young Professionals
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- multi-omics integration, single-cell data, Human Cell Atlas, adhesives
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