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Small data methods in omics: the power of one

Nature Methods volume 21, pages 1597–1602 (2024)Cite this article

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Abstract

Over the last decade, biology has begun utilizing ‘big data’ approaches, resulting in large, comprehensive atlases in modalities ranging from transcriptomics to neural connectomics. However, these approaches must be complemented and integrated with ‘small data’ approaches to efficiently utilize data from individual labs. Integration of smaller datasets with major reference atlases is critical to provide context to individual experiments, and approaches toward integration of large and small data have been a major focus in many fields in recent years. Here we discuss progress in integration of small data with consortium-sized atlases across multiple modalities, and its potential applications. We then examine promising future directions for utilizing the power of small data to maximize the information garnered from small-scale experiments. We envision that, in the near future, international consortia comprising many laboratories will work together to collaboratively build reference atlases and foundation models using small data methods.

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Fig. 1: Constructing an updateable integrated cell atlas.
Fig. 2: Applications of single-cell integrative foundational models.

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Acknowledgements

This work was supported by National Institutes of Health (NIH) grants UM1MH130994, U01AG076791, U01DA052769, R01AG067153, R01AG082127 and RF1AG065675 to X.X. and the Knights Templar Eye Foundation grant KTEF-5646361 to S.F.G. F.J.T. acknowledges support from the German Federal Ministry of Education and Research (BMBF; 031L0210A) and from the Helmholtz Association’s Initiative and Networking Fund through Helmholtz AI (ZT-I-PF-5-01). Q.N. acknowledges support from National Science Foundation grants DMS1763272, MCB202842 and CBET2134916, and NIH grants R01AR079150, R01ED030565 and U01AR073159. K.G.J. acknowledges support from NIH grant T32 DC010775-14.

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Author notes

  1. These authors contributed equally: Kevin G. Johnston, Steven F. Grieco.

Authors and Affiliations

  1. Department of Mathematics, University of California, Irvine, Irvine, CA, USA

    Kevin G. Johnston & Qing Nie

  2. Department of Anatomy and Neurobiology, School of Medicine, University of California, Irvine, Irvine, CA, USA

    Kevin G. Johnston, Steven F. Grieco & Xiangmin Xu

  3. Center for Neural Circuit Mapping, University of California, Irvine, Irvine, CA, USA

    Steven F. Grieco & Xiangmin Xu

  4. Department of Developmental and Cell Biology, University of California, Irvine, Irvine, CA, USA

    Qing Nie

  5. Helmholtz Center Munich—German Research Center for Environmental Health, Institute of Computational Biology, Neuherberg, Germany

    Fabian J. Theis

  6. School of Life Sciences Weihenstephan, Technical University of Munich, Munich, Germany

    Fabian J. Theis

  7. Department of Mathematics, Technical University of Munich, Munich, Germany

    Fabian J. Theis

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K.G.J. and S.F.G. wrote the paper and created the figures. Q.N. and F.J.T. and oversaw the writing. X.X. oversaw and supported the work.

Corresponding authors

Correspondence to Qing Nie, Fabian J. Theis or Xiangmin Xu.

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F.J.T. consults for Immunai, Singularity Bio B.V., CytoReason and Omniscope, and has ownership interest in Dermagnostix GmbH and Cellarity.

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Johnston, K.G., Grieco, S.F., Nie, Q. et al. Small data methods in omics: the power of one. Nat Methods 21, 1597–1602 (2024). https://doi.org/10.1038/s41592-024-02390-8

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