A1812
Title: Automated landmark discovery method for integration of spatial omics data
Authors: Xingjie Shi - East China Normal University (China) [presenting]
Abstract: Integrating multiple spatial omics datasets is essential for building spatially coherent atlases and performing comparative analyses, but is challenged by variability between tissue slices. Landmark-based alignment is a robust strategy, yet it relies on laborious manual annotation or inaccurate image-based methods dependent on prior knowledge. ARIEL, a framework for robust alignment and information transfer powered by a fast automated omics-driven landmark discovery method, is presented. ARIEL significantly outperforms existing methods in accuracy and efficiency, especially in complex cross-platform scenarios. The utility of ARIEL is demonstrated through construction of a reusable landmark library and spatial atlas of the human liver lobule, enabling automatic annotation of anatomical structures in new samples and leading to identification of disease-associated spatial gene programs missed by conventional analyses. ARIEL provides a versatile solution for spatial omics integration, repurposing landmarks as tools for biological discovery.
