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
Published inNature methods Vol. 20; no. 3; pp. 424 - 431
Main Authors Baker, Ethan A. G., Schapiro, Denis, Dumitrascu, Bianca, Vickovic, Sanja, Regev, Aviv
Format Journal Article
LanguageEnglish
Published New York Nature Publishing Group US 01.03.2023
Nature Publishing Group
Subjects
631/114/2415
631/208/212/2019
631/553/794
631/61/212/2019
Bioinformatics
Biological Microscopy
Biological Techniques
Biomedical and Life Sciences
Biomedical Engineering/Biotechnology
Computer applications
Data analysis
In Vitro Techniques
Life Sciences
Multiomics
Optimization
Proteins - chemistry
Proteomics
RNA - chemistry
Spatial analysis
Spatial data
Statistics
Tissues
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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. This paper presents a statistical framework for power analysis of spatial omics studies, facilitated by an in silico tissue-generation method.
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ISSN:1548-7091
1548-7105
1548-7105
DOI:10.1038/s41592-023-01766-6