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PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed

Lan Shui¹, Anirban Maitra², Ying Yuan¹

¹Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas, United States of America.

Plos Computational Biology

|July 29, 2025

Summary

This summary is machine-generated.

High costs limit spatial transcriptomics (ST) applications. We developed PoweREST, a tool for power calculations in differential gene expression analysis using 10X Genomics Visium data, enhancing ST study design.

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Area of Science:

Genomics and Molecular Biology
Bioinformatics and Computational Biology

Background:

Spatial transcriptomics (ST) advancements offer powerful biological insights.
High costs of ST data generation limit widespread research application.
Robust statistical power is crucial for maximizing the utility of limited ST data.

Purpose of the Study:

To address the lack of power calculation methods for differential gene expression (DEG) analysis in ST.
To introduce PoweREST, a tool for estimating statistical power in ST studies.
To facilitate informed experimental design and resource allocation for ST research.

Main Methods:

Development of PoweREST, a power estimation tool specifically for 10X Genomics Visium ST data.
PoweREST supports power calculations both pre-experimentally and post-preliminary data collection.
A user-friendly, program-free web application for interactive power analysis and visualization.

Main Results:

PoweREST provides a method to calculate statistical power for DEG detection in ST data.
The tool enables researchers to assess the feasibility and optimize parameters for ST experiments.
Interactive visualization aids in understanding the relationship between power and study parameters.

Conclusions:

PoweREST addresses a critical need for power analysis in spatial transcriptomics DEG studies.
The tool enhances the statistical robustness and cost-effectiveness of ST research.
Facilitates better planning and interpretation of spatial transcriptomics experiments.