<p>Parameter Configuration for Simulated Datasets.</p>

Current computational approaches face substantial challenges, such as effective integration of multi-omics molecular information with spatial information and corresponding high-resolution histology images.

To address this challenge, we proposed SpaMOAL (<b>Spa</b>tially <b>M</b>ulti-<b>O</b>mics graph contr<b>A</b>stive <b>L</b>earning), a graph-based contrastive learning approach for spatial domain identification. SpaMOAL learns clustering-friendly representations from spatial multi-omics data by integrating spatial coordinates, histological image features, and molecular profiles, enabling accurate delineation of spatial tissue domains.