spaCraft: calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics
- 1Department of Biomedical Informatics, The Ohio State University
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.
Related
- PhenoMapR: scalable mapping of sample phenotypes to single-cell, spatial, and bulk transcriptomics data
- Spatial transcriptomics profiling across tissue boundaries in the developing mouse brain
- SpaCoEx: Sparse Gene Selection for Spatially Varying Co-expression in Spatial Transcriptomics
- NicheCorr: Identifying Niche-Specific Differential Gene Co-Expression in Spatial Transcriptomics
- Spatialsnake: a flexible workflow for spatial transcriptomics analysis across platforms
BibTeX
@misc{shin2026spacraft,
title = {{spaCraft: calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics}},
author = {J. Shin and J. Xie and X. Jin and Q. Ma and D. Chung},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.04.749534},
url = {https://www.biorxiv.org/content/10.64898/2026.09.04.749534v1},
note = {CC BY-ND},
}
RIS
TY - UNPB
TI - spaCraft: calibrated power analysis and sample-size planning for multi-sample spatial transcriptomics
AU - J. Shin
AU - J. Xie
AU - X. Jin
AU - Q. Ma
AU - D. Chung
PY - 2026
DA - 2026/09/04
PB - bioRxiv
DO - 10.64898/2026.09.04.749534
UR - https://www.biorxiv.org/content/10.64898/2026.09.04.749534v1
AB - 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.
ER -