Welcome to the SpatialEx tutorial!
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.. SpatialEx tutorial master file, created by
   sphinx-quickstart on Thu Sep 16 19:43:51 2021.
   You can adapt this file completely to your liking, but it should at least
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High-Parameter Spatial Multi-Omics through Histology-Anchored Integration
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.. toctree::
   :maxdepth: 1
   :caption: Tutorials

   Installation
   API
   Tutorial 1 SpatialEx Translates Histology to Omics at Single-Cell Resolution_v2
   Tutorial 2 SpatialEx+ Enables Larger Panel Spatial Analysis through Panel Diagonal Integration
   Tutorial 3 Scalability on Million-Cell Tissue Sections
   Tutorial 4 SpatialEx+ Enables Spatial Multi-omics through Omics Diagonal Integration (transcriptomics-proteomics)
   Tutorial 5 SpatialEx+ Enables Spatial Multi-omics through Omics Diagonal Integration (transcriptomics-metabolomics)
   Tutorial 6 SpatialEx+ is Robust Even When There is Weak or No Overlap between Slices

.. image:: ../Figures/figure.jpg
   :width: 1600 

Overview
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Recent advances in spatial omics technologies enable in situ molecular profiling while preserving spatial context but face fundamental challenges in achieving high-parameter and multi-omics co-profiling. Spatially resolving complementary panels or distinct omics layers across serial tissue sections circumvents technical trade-offs but introduces the spatial diagonal integration problem: reconstructing unified multi-omics states when datasets lack shared molecular features. To address this critical challenge, we present SpatialEx and its extension SpatialEx+, computational frameworks that leverage histology as a universal anchor to integrate spatial molecular data across tissue sections. The foundational SpatialEx model combines a pre-trained H&E foundation model with hypergraph learning and contrastive learning to predict single-cell omics profiles from histology, encoding multi-neighborhood spatial dependencies and global tissue context. Building upon SpatialEx, SpatialEx+ introduces an omics cycle module that encourages cross-omics consistency across adjacent sections via slice-invariant mapping functions, achieving seamless diagonal integration without requiring co-measured multi-omics data for training. Through rigorous validation across three key applications, we demonstrate: (1) H&E-to-omics prediction at single-cell resolution, characterizing tumor microenvironments beyond sequencing borders in breast cancer; (2) panel diagonal integration, merging non-overlapping spatial gene panels from different slices to resolve immune-stromal boundaries lost by individual panels; and (3) omics diagonal integration, revealing Parkinson’s disease anatomical domains and context-specific tissue pathologies through integrated transcriptomic-metabolic analysis. The framework scales to datasets exceeding one million cells, maintains robustness with non-overlapping or heterogeneous sections, and supports unlimited omics layers in principle. By transforming highly feasible spatial single-omics assays with histology into a holistic spatial multi-omics map, our work democratizes systems-level tissue analysis, bridging fundamental spatial biology and scalable multi-omics research with minimal experimental overhead. 

Citation
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::

  @article{liu2025high,
    title={High-Parameter Spatial Multi-Omics through Histology-Anchored Integration},
    author={Liu, Yonghao and Wang, Chuyao and Wang, Zhikang and Chen, Liang and Li, Zhi and Song, Jiangning and Zou, Qi and Gao, Rui and Qian, Binzhi and Feng, Xiaoyue and Guan, Renchu and Yuan, Zhiyuan},
    journal={Nature Methods},
    year={2025}
  }