Shui, Lan, Maitra, Anirban, Yuan, Ying, Lau, Ken S., Kaur, Harsimran, Li, Liang, & Li, Ziyi. (2025). 鈥�.鈥� PLOS Computational Biology, 21(7), e1013293.
Recent advances in听spatial transcriptomics (ST)鈥攁 technique that measures gene activity in tissue while preserving its location鈥攈ave greatly improved biological research. However, current ST methods are expensive, making large-scale studies difficult. This creates a need to听make the most of available data听to achieve reliable results.
A key task in ST research is identifying听genes that behave differently under different conditions, known as听differentially expressed genes (DEGs). While these analyses are common, how to calculate their听statistical power鈥攖he ability to detect real differences鈥攊s rarely discussed.
To address this, we developed听PoweREST, a tool that estimates the power of DEG detection using听10X Genomics Visium data. PoweREST can be used听before starting experiments听or听after collecting preliminary data, making it flexible for many study designs. We also created a听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)