Inferring biologically relevant molecular tissue substructures by agglomerative clustering of digitized spatial transcriptomes with multilayer.

Abstract

Spatially resolved transcriptomics (SrT) can investigate organ or tissue architecture from the angle of gene programs that define their molecular complexity. However, computational methods to analyze SrT data underexploit their spatial signature. Inspired by contextual pixel classification strategies applied to image analysis, we developed MULTILAYER to stratify maps into functionally relevant molecular substructures. MULTILAYER applies agglomerative clustering within contiguous locally defined transcriptomes (gene expression elements or "gexels") combined with community detection methods for graphical partitioning. MULTILAYER resolves molecular tissue substructures within a variety of SrT data with superior performance to commonly used dimensionality reduction strategies and still detects differentially expressed genes on par with existing methods. MULTILAYER can process high-resolution as well as multiple SrT data in a comparative mode, anticipating future needs in the field. MULTILAYER provides a digital image perspective for SrT analysis and opens the door to contextual gexel classification strategies for developing self-supervised molecular diagnosis solutions. A record of this paper's transparent peer review process is included in the supplemental information.