Advances in spatially-resolved transcriptomics (SRT) technologies have propelled the development of new computational analysis methods to unlock biological insights. As the cost of generating these data decreases, these technologies provide an exciting opportunity to create large-scale atlases that integrate SRT data across multiple tissues, individuals, species, or phenotypes to perform population-level analyses. Here, we describe unique challenges of varying spatial resolutions in SRT data, as well as highlight the opportunities for standardized preprocessing methods along with computational algorithms amenable to atlas-scale datasets leading to improved sensitivity and reproducibility in the future.
@article{arxiv.2408.00367,
title = {Integrating spatially-resolved transcriptomics data across tissues and individuals: challenges and opportunities},
author = {Boyi Guo and Wodan Ling and Sang Ho Kwon and Pratibha Panwar and Shila Ghazanfar and Keri Martinowich and Stephanie C. Hicks},
journal= {arXiv preprint arXiv:2408.00367},
year = {2024}
}