Rui Yan

This paper introduces a computational framework for true 3D spatial reconstruction of tissues, demonstrating improved fidelity over 2D methods across multiple spatial omics modalities.

H2O predicts spatial transcriptomics and proteomics from H&E images using a contrastive learning framework, outperforming existing models and recovering biologically relevant cell-cell communication.

SpatialZ generates dense 3D cell atlases from planar spatial transcriptomics data by reconstructing virtual slices, validated with real 3D data and enabling new spatial analyses.