PHD-MS: Multiscale Domain Identification for Spatial Transcriptomics via Persistent Homology

Spatial transcriptomics (ST) measures gene expression at a set of spatial locations in a tissue. Communities of nearby cells that express similar genes form spatial domains. Specialized ST clustering algorithms have been developed to identify these spatial domains. These methods often identify spatial domains at a single morphological scale, and interactions across multiple scales are often overlooked. For example, large cellular communities often contain smaller substructures, and heterogeneous frontier regions often lie between homogeneous domains. Topological data analysis (TDA) is an emerging mathematical toolkit that studies the underlying features of data at various geometric scales. It is especially useful for analyzing complex biological datasets with multiscale characteristics. Using TDA, we develop Persistent Homology for Domains at Multiple Scales (PHD-MS) to locate tissue structures that persist across morphological scales. We apply PHD-MS to highlight multiscale spatial domains in several tissue types and ST technologies. We also compare PHD-MS domains against ground-truth domains in expert-annotated tissues, where PHD-MS outperforms traditional clustering approaches. PHD-MS is available as an open-source software package with an interactive graphical user interface for exploring the identified multiscale domains.