scMODAL: a general deep learning framework for comprehensive single-cell multi-omics data alignment with feature links
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Abstract
Abstract Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODAL’s effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.
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AI-driven models applied to triple-negative breast cancer merge bulk and single-cell transcriptomics data to discover drug-responsive subpopulations.
“AI-driven models are also applied to discover drug-responsive subpopulations in triple-negative breast cancer, merging bulk and single-cell transcriptomics data (Wang Y. et al., 2025).”
From Spatial pharmacology of anti-cancer immunomodulators: a comprehensive review (2026) · cites this paper as Wang Y. et al., 2025
The Results sections on multimodal integration describe benchmarking scMODAL with blood and bone-marrow datasets, not an analysis of triple-negative breast cancer. Searches for drug-responsive subpopulations and the combination of bulk and single-cell transcriptomics found no passage addressing the claim. The available passages do not establish a contrary finding either. (LLM, conf=0.75)
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- Authors
- Wang, G., Zhao, J., Lin, Y., Liu, T., Zhao, Y., & Zhao, H.
- Affiliations
- Yale University (US)
- Corresponding author
- Zhao, Hongyu
- Venue
- Nature Communications
- Published
- 2025-05-29
- Volume
- 16
- Issue
- 1
- Pages
- 4994-4994
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC12122792
- OpenAlex
- W4410855348
- Cited by
- 29
- References
- 80
- Citations by year
- 2026: 20 · 2025: 9
- FWCI
- 8.72
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nature Communications (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (R01 GM134005)
- U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute (U24 HG012108)
- U.S. Department of Health & Human Services | NIH | National Cancer Institute (P50 CA196530)
- U.S. Department of Health & Human Services | NIH | National Institute on Aging (R01 AG068191, RF1 AG081413)
- U.S. Department of Health & Human Services | NIH | National Institute of Biomedical Imaging and Bioengineering (R01 EB034720)
- 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
- single-cell multi-omics integration, cell embedding, generative adversarial networks, feature imputation, cell subpopulation identification, transcriptomics, epigenomics, proteomics
- MeSH terms
- Deep Learning, Multiomics, Humans, Neural Networks, Computer, Computational Biology, Gene Expression Profiling, Genomics, Proteomics, Epigenomics, Single-Cell Analysis, Transcriptome
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