spaCraft: calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics

Abstract

Comparative spatial transcriptomics is now routine, yet the number of tissue sections per group is rarely determined by formal power analysis. Power depends jointly on between sample variation and domains recovered by clustering, a combination not represented by existing tools. We present spaCraft, which converts a replicated pilot into endpoint-specific per-group sample-size recommendations. It fits models of spatial expression, domain geometry and composition, then estimates power through a generate, recover, test loop that reestimates domains in every synthetic sample, allowing clustering uncertainty to enter the recommendation. Its differential-expression and composition endpoints are tested on recovered rather than assumed domains, yielding calibrated power rather than detection rates. We applied spaCraft to four cohorts spanning Visium, Stereo-seq, and Visium HD. In held-out validation, three-sample pilots predicted sample-size requirements in independent real samples, supporting the full chain from pilot fitting through domain recovery to endpoint testing. Sample size thereby becomes an explicit, reproducible property of the planned analysis rather than an informal guess.