  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.
