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In silico tissue generation and power analysis for spatial omics

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 predic...

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Bibliographic Details
Main Authors: Baker, Ethan (Author) , Schapiro, Denis (Author) , Dumitrascu, Bianca (Author) , Vickovic, Sanja (Author) , Regev, Aviv (Author)
Format: Article (Journal)
Language:English
Published: March 2023
In: Nature methods
Year: 2023, Volume: 20, Issue: 3, Pages: 424-431
ISSN:1548-7105
DOI:10.1038/s41592-023-01766-6
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1038/s41592-023-01766-6
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41592-023-01766-6
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Author Notes:Ethan A.G. Baker, Denis Schapiro, Bianca Dumitrascu, Sanja Vickovic & Aviv Regev
Description
Summary: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.
Item Description:Online veröffentlicht am 2. März 2023
Gesehen am 13.12.2023
Physical Description:Online Resource
ISSN:1548-7105
DOI:10.1038/s41592-023-01766-6

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