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PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.

Lan Shui·Anirban Maitra·Ying Yuan·Ken Lau·Harsimran Kaur·Liang Li·Ziyi Li·Translational and Basic Science Research in Early Lesions Research Consortia
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Abstract

Recent advancements in spatial transcriptomics (ST) have significantly enhanced biological research in various domains. However, the high cost for current ST data generation techniques restricts the large-scale application of ST. Consequently, maximization of the use of available resources to achieve robust statistical power for ST data is a pressing need. One fundamental question in ST analysis is detection of differentially expressed genes (DEGs) under different conditions using ST data. Such DEG analyses are performed frequently, but their power calculations are rarely discussed in the literature. To address this gap, we developed PoweREST, a power estimation tool designed to support the power calculation for DEG detection with 10X Genomics Visium data. PoweREST enables power estimation both before any ST experiments and after preliminary data are collected, making it suitable for a wide variety of power analyses in ST studies. We also provide a user-friendly, program-free web application that allows users to interactively calculate and visualize study power along with relevant parameters.

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Gene Expression ProfilingTranscriptomeComputational BiologyHumansSoftwareAlgorithmsDatabases, GeneticJournal ArticleResearch Support, N.I.H., Extramural

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Lan Shui, Anirban Maitra, Ying Yuan, Ken Lau, Harsimran Kaur, Liang Li, Ziyi Li, Translational and Basic Science Research in Early Lesions Research Consortia. 2025-07-29. PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.. https://doi.org/10.1371/journal.pcbi.1013293

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