Loom: Multi-Region Analysis of Spatial Transcriptomics with Local Neighborhoods and Global Trajectories
TL;DR
The results demonstrate that Loom supports effectively the discovery of cellular transitions and spatiotemporal expression dynamics and the examination of spatiotemporal biological mechanisms.
Abstract
We present Loom, a spatial transcriptomics (ST) visual computing system to support the analysis of pseudo-temporal trajectories, comparative investigation across samples and regions of interest, and the examination of spatially structured processes within local microenvironments. ST is a molecular profiling technology that measures gene expression directly within a thin tissue section while preserving its spatial organization. For practical application-driven analyses, the ST local microenvironment data needs to be integrated with cell reference datasets and temporal simulations of cell behavior. This integration is challenging due to multi-modal registration issues and the complexity of the pseudo-temporal patterns, spatial enrichment data, and gene expression dynamics. Loom leverages a novel glyph coupled with a computational backbone to facilitate the detailed pseudo-temporal exploration of local microenvironments, cross-sample comparisons, and investigation of spatiotemporal biological mechanisms. We evaluate Loom through two case studies developed with experts in tissue pathology and oncologists and through an external usability study. The results demonstrate that Loom supports effectively the discovery of cellular transitions and spatiotemporal expression dynamics.