SpaDesign2: A Statistical Framework for the Power Analysis of a Multi-Sample Spatial Transcriptomics

Thomas M. Menino Convention & Exhibition Center 

High-throughput spatial transcriptomics (HST) captures high-dimensional gene expression profiles in tissue samples while preserving spatial coordinates and has gained broad attention across biomedical research fields. Although many statistical methods exist for HST analysis, experimental design remains under-studied despite the high cost of data generation. To fill this gap, we propose spaDesign2, a simulation-based framework for the power analysis in multi-sample HST. To generate biologically realistic synthetic data, spaDesign2 learns generative models from pilot data, using Beta–Binomial regression for domain composition, a Fisher–Gaussian kernel mixture model for domain morphology, and Gaussian-processes for gene expression patterns, accounting for between-sample variability. It then estimates power across varying candidate sample sizes via simulation and identifies the minimum sample size required to achieve a target power for detecting changes in domain composition, geometry, and gene expression. Across multiple HST datasets, spaDesign2 yields robust sample size recommendations across experimental settings and platforms.

Spatial transcriptomics

Multi-sample study

Experimental design

Power analysis

Spatial statistics 

Section on Statistics in Genomics and Genetics