StaVia: Spatially and temporally aware cartography with higher order random walks for cell atlases

Abstract

Single-cell atlases are critical for unraveling the cellular basis of health and disease, yet their sheer diversity and vast data landscape in time and space pose daunting computational challenges pertaining to the delineation of multiple simultaneously emerging lineages, the integration of spatial and temporal information, and the visualization of trajectories across large atlases with enough resolution to observe localized transitions. To tackle this intricacy, we introduce StaVia, a computational framework that synergizes multi-faceted single-cell data—spanning time-series data, spatial gene expression patterns, and directional trends from RNA velocity— with higher-order random walks that leverage the memory of cells’ past states. StaVia fuses this method with a cartographic “Atlas View” that offers intuitive graph visualization, simultaneously capturing the nuanced details of cellular development at single-cell resolution as well as the broader connectivity of cell lineages, avoiding common pitfalls of merged distinct trajectories or missed transitional states seen in existing methods which are all memoryless. Notably, we demonstrate that StaVia unlocks new insights into placode development, radial glia pluripotency during neurulation, and the transitions pivotal to these processes in a large-scale Zebrafish developmental atlas. StaVia also allows spatially aware cartography that captures relationships between cell populations based on their spatial location as well as their gene expressions in a MERFISH dataset – underscoring its potential to dissect complex biological landscapes in both spatial and temporal contexts.