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 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.

Automated summary

As the spatially resolved multiplex profiling of RNA and proteins becomes more prominent, understanding the statistical power for testing specific hypotheses in such experiments is crucial. This study introduces a method for generating tunable in silico tissues and constructs a computational framework for spatial power analysis using spatial profiling data sets. The framework can be applied to diverse spatial data modalities and tissues of interest, providing insights for spatial omics studies.