ClusterMap for multi-scale clustering analysis of spatial gene expression.

Abstract

Quantifying RNAs in their spatial context is crucial to understanding gene expression and regulation in complex tissues. In situ transcriptomic methods generate spatially resolved RNA profiles in intact tissues. However, there is a lack of a unified computational framework for integrative analysis of in situ transcriptomic data. Here, we introduce an unsupervised and annotation-free framework, termed ClusterMap, which incorporates the physical location and gene identity of RNAs, formulates the task as a point pattern analysis problem, and identifies biologically meaningful structures by density peak clustering (DPC). Specifically, ClusterMap precisely clusters RNAs into subcellular structures, cell bodies, and tissue regions in both two- and three-dimensional space, and performs consistently on diverse tissue types, including mouse brain, placenta, gut, and human cardiac organoids. We demonstrate ClusterMap to be broadly applicable to various in situ transcriptomic measurements to uncover gene expression patterns, cell niche, and tissue organization principles from images with high-dimensional transcriptomic profiles.