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Computational biology

Designing spatial transcriptomic experiments

Nature Methods volume 20, pages 355–356 (2023)Cite this article

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Optimal design of spatial transcriptomic experiments allows statistical evaluation of the impact of various biological and technological features on the discovery of cell phenotypes.

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Fig. 1: Workflow of spatial transcriptomics power analysis.

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Acknowledgements

This work was supported in part by CZF2019-002443 from the Chan Zuckerberg Initiative DAF, an advised fund of Silicon Valley Community Foundation. The authors are supported by the National Cancer Institute of the US National Institutes of Health (U24CA180996). A.S. is also supported by the project of excellence “Statistical methods and models for complex data” awarded to the Department of Statistical Sciences, University of Padova by the Italian Ministry for Education and University Research.

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  1. These authors contributed equally: Dario Righelli, Andrea Sottosanti.

Authors and Affiliations

  1. Department of Statistical Sciences, University of Padova, Padova, Italy

    Dario Righelli, Andrea Sottosanti & Davide Risso

  2. Padua Center for Network Medicine, University of Padova, Padova, Italy

    Davide Risso

Authors
  1. Dario Righelli
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  2. Andrea Sottosanti
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  3. Davide Risso
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Correspondence to Davide Risso.

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Righelli, D., Sottosanti, A. & Risso, D. Designing spatial transcriptomic experiments. Nat Methods 20, 355–356 (2023). https://doi.org/10.1038/s41592-023-01801-6

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