Discovery and generalization of tissue structures from spatial omics data
Zhenqin Wu, Ayano Kondo, Monee McGrady, Ethan A. G. Baker, Benjamin Chidester, Eric Q. Wu, Maha K. Rahim, Nathan A. Bracey, Vivek Charu, Raymond J. Cho, Jeffrey B. Cheng, Maryam Afkarian, James Zou, Aaron T. Mayer, Alexandro E. Trevino
Abstract
Tissues are organized into anatomical and functional units at different scales. New technologies for high-dimensional molecular profiling in situ have enabled the characterization of structure-function relationships in increasing molecular detail. However, it remains a challenge to consistently identify key functional units across experiments, tissues, and disease contexts, a task that demands extensive manual annotation. Here, we present spatial cellular graph partitioning (SCGP), a flexible method for the unsupervised annotation of tissue structures. We further present a reference-query extension pipeline, SCGP-Extension, that generalizes reference tissue structure labels to previously unseen samples, performing data integration and tissue structure discovery. Our experiments demonstrate reliable, robust partitioning of spatial data in a wide variety of contexts and best-in-class accuracy in identifying expertly annotated structures. Downstream analysis on SCGP-identified tissue structures reveals disease-relevant insights regarding diabetic kidney disease, skin disorder, and neoplastic diseases, underscoring its potential to drive biological insight and discovery from spatial datasets.