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Harnessing the deep learning power of foundation models in single-cell omics

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.

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References

  1. 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).

    Google Scholar 

  2. Baysoy, A. et al. The technological landscape and applications of single-cell multi-omics. Nat. Rev. Mol. Cell Biol. 24, 695–713 (2023).

    Article  CAS  PubMed  Google Scholar 

  3. Ma, Q. & Xu, D. Deep learning shapes single-cell data analysis. Nat. Rev. Mol. Cell Biol. 23, 303–304 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. 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).

  5. Theodoris, C. V. et al. Transfer learning enables predictions in network biology. Nature 618, 616–624 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Stuart, T. & Satija, R. Integrative single-cell analysis. Nat. Rev. Genet. 20, 257–272 (2019).

    Article  CAS  PubMed  Google Scholar 

  7. Thirunavukarasu, A. J. et al. Large language models in medicine. Nat. Med. 29, 1930–1940 (2023).

    Article  CAS  PubMed  Google Scholar 

  8. Wang, W. et al. A survey of zero-shot learning: Settings, methods, and applications. ACM Trans. Intell. Syst. Technol. 10, 1–37 (2019).

    Article  Google Scholar 

  9. 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).

  10. 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).

  11. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. 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).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Wang, X. et al. MarsGT: Multi-omics analysis for rare population inference using single-cell graph transformer. Nat. Commun. 15, 338 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cao, Z. J. & Gao, G. Multi-omics single-cell data integration and regulatory inference with graph-linked embedding. Nat. Biotechnol. 40, 1458–1466 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Moor, M. et al. Foundation models for generalist medical artificial intelligence. Nature 616, 259–265 (2023).

    Article  CAS  PubMed  Google Scholar 

Download references

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Correspondence to Qin Ma.

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Nature Reviews Molecular Cell Biology thanks the anonymous reviewer(s) for their contribution to the peer review of this work.

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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

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