Shui, Lan, Maitra, Anirban, Yuan, Ying, Lau, Ken S., Kaur, Harsimran, Li, Liang, & Li, Ziyi. (2025). “PoweREST: Statistical power estimation for spatial transcriptomics experiments to detect differentially expressed genes between two conditions.” PLOS Computational Biology, 21(7), e1013293. https://doi.org/10.1371/journal.pcbi.1013293
Recent advances in spatial transcriptomics (ST)—a technique that measures gene activity in tissue while preserving its location—have greatly improved biological research. However, current ST methods are expensive, making large-scale studies difficult. This creates a need to make the most of available data to achieve reliable results.
A key task in ST research is identifying genes that behave differently under different conditions, known as differentially expressed genes (DEGs). While these analyses are common, how to calculate their statistical power—the ability to detect real differences—is rarely discussed.
To address this, we developed PoweREST, a tool that estimates the power of DEG detection using 10X Genomics Visium data. PoweREST can be used before starting experiments or after collecting preliminary data, making it flexible for many study designs. We also created a user-friendly web applicationthat allows researchers to easily calculate and visualize the power of their ST studies without needing to write any code.
Fig 1. Schema of the proposed PoweREST method.
When a preliminary cohort of ST data is available, PoweREST performs the power calculation based on bootstrap and P-splines fitting. When preliminary data are not available, an R Shiny app with power estimation results based on datasets from two cancer studies can be used. Created in BioRender. Shui, L. (2025)