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  • Review Article
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Principles and challenges of modeling temporal and spatial omics data

Nature Methods volume 20, pages 1462–1474 (2023)Cite this article

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Abstract

Studies with temporal or spatial resolution are crucial to understand the molecular dynamics and spatial dependencies underlying a biological process or system. With advances in high-throughput omic technologies, time- and space-resolved molecular measurements at scale are increasingly accessible, providing new opportunities to study the role of timing or structure in a wide range of biological questions. At the same time, analyses of the data being generated in the context of spatiotemporal studies entail new challenges that need to be considered, including the need to account for temporal and spatial dependencies and compare them across different scales, biological samples or conditions. In this Review, we provide an overview of common principles and challenges in the analysis of temporal and spatial omics data. We discuss statistical concepts to model temporal and spatial dependencies and highlight opportunities for adapting existing analysis methods to data with temporal and spatial dimensions.

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Fig. 1: Experimental designs with or without temporal or spatial side information.
Fig. 2: Alternative strategies to model spatial and temporal dependencies at the sample level.
Fig. 3: Comparative analysis of temporal or spatial data across multiple subjects.
Fig. 4: Obtaining mechanistic insights and causal relationships from temporal and spatial data.

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Acknowledgements

We thank S. Ghazanfar, R. Argelaguet, L. Marconato and I. Kats for providing feedback on the manuscript. The work was supported by the BMBF (COMPLS project MOFA no. 031L0171B), the European Commission (ERC project DECODE, 810296) and core support from the European Molecular Biology Laboratory and the German Cancer Research Center.

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Authors and Affiliations

  1. Division of Computational Genomics and Systems Genetics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Britta Velten & Oliver Stegle

  2. Cellular Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge, UK

    Britta Velten & Oliver Stegle

  3. Centre for Organismal Studies (COS) and Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany

    Britta Velten

  4. Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany

    Oliver Stegle

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  1. Britta Velten
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B.V. and O.S. conceived and jointly wrote the review.

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Correspondence to Britta Velten or Oliver Stegle.

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O.S. is a paid advisor of Insitro. The remaining author declares no competing interests.

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Nature Methods thanks Stephanie Hicks and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Lin Tang, in collaboration with the Nature Methods team.

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Velten, B., Stegle, O. Principles and challenges of modeling temporal and spatial omics data. Nat Methods 20, 1462–1474 (2023). https://doi.org/10.1038/s41592-023-01992-y

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