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In silico tissue generation and power analysis for spatial omics.

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As spatially resolved multiplex profiling of RNA and proteins becomes more prominent, it is increasingly important to understand the statistical power available to test specific hypotheses when designing and interpreting such experiments. Ideally, it would be possible to create an oracle that predicts sampling requirements for generalized spatial experiments. However, the unknown number of relevant spatial features and the complexity of spatial data analysis make this challenging. Here, we enumerate multiple parameters of interest that should be considered in the design of a properly powered spatial omics study. We introduce a method for tunable in silico tissue (IST) generation and use it with spatial profiling data sets to construct an exploratory computational framework for spatial power analysis. Finally, we demonstrate that our framework can be applied across diverse spatial data modalities and tissues of interest. While we demonstrate ISTs in the context of spatial power analysis, these simulated tissues have other potential use cases, including spatial method benchmarking and optimization.


Funder: Royal Swedish Academy of Sciences (Kungl. Vetenskapsakademien); doi:

Funder: Svenska Läkaresällskapet (Swedish Society of Medicine); doi:

Funder: KMB Foundation


Proteins, RNA, In Vitro Techniques, Multiomics

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Nat Methods

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Springer Science and Business Media LLC
Bundesministerium für Bildung und Forschung (Federal Ministry of Education and Research) (BMBF 01ZZ2004)
Damon Runyon Cancer Research Foundation (Cancer Research Fund of the Damon Runyon-Walter Winchell Foundation) (DRQ-03-20)
Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (Swiss National Science Foundation) (P2ZHP3_181475)
National Science Foundation (NSF) (1745302, DMS-1638352, DMS-1638521)
U.S. Department of Health & Human Services | NIH | National Cancer Institute (NCI) (HHSN261201500003I, HHSN261100039)