Foundation models hold great promise for analyzing single-cell omics data, yet various challenges remain that require further advancements. In this Comment, we discuss the progress, limitations and best practices in applying foundation models to interrogate data and improve downstream tasks in single-cell omics.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
References
Bommasani, R. et al. Picking on the same person: Does algorithmic monoculture lead to outcome homogenization? Adv. Neural Inf. Process. Syst. 35, 3663–3678 (2022).
Baysoy, A. et al. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).
Ma, Q. & Xu, D. Deep learning shapes single-cell data analysis. Nat. Rev. Mol. Cell Biol. 23, 303–304 (2022).
Cui, H. et al. scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nat. Methods https://doi.org/10.1038/s41592-024-02201-0 (2024).
Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).
Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).
Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).
Wang, W. et al. A survey of zero-shot learning: Settings, methods, and applications. ACM Trans. Intell. Syst. Technol. 10, 1–37 (2019).
Liu, T. et al. Evaluating the utilities of large language models in single-cell data analysis. Preprint at bioRxiv https://doi.org/10.1101/2023.09.08.555192 (2023).
Rosen, Y. et al. Toward universal cell embeddings: integrating single-cell RNA-seq datasets across species with SATURN. Nat. Methods https://doi.org/10.1038/s41592-024-02191-z (2024).
Janizek, J. D. et al. Uncovering expression signatures of synergistic drug responses via ensembles of explainable machine-learning models. Nat. Biomed. Eng. 7, 811–829 (2023).
Van de Sande, B. et al. Applications of single-cell RNA sequencing in drug discovery and development. Nat. Rev. Drug Discov. 22, 496–520 (2023).
Wang, X. et al. MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer. Nat. Commun. 15, 338 (2024).
Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458–1466 (2022).
Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Reviews Molecular Cell Biology thanks the anonymous reviewer(s) for their contribution to the peer review of this work.
Additional information
Related links
OpenAI: https://openai.com/
Supplementary information
Rights and permissions
About this article
Cite this article
Ma, Q., Jiang, Y., Cheng, H. et al. Harnessing the deep learning power of foundation models in single-cell omics. Nat Rev Mol Cell Biol 25, 593–594 (2024). https://doi.org/10.1038/s41580-024-00756-6
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41580-024-00756-6