SpatialFuser: Universal Framework for Spatial Multi-Omics Data Integrative Analysis
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SpatialFuster is a unified framework for fine-grained single-slice analysis and cross-sample integrative analysis across modalities including epigenomics, transcriptomics, proteomics, and metabolomics.

.. image:: ./_static/images/SpatialFuser.png
   :width: 1400
   :alt: SpatialFuser overview
   :align: right

Key Applications of SpatialFuser
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1. **Fine Spatial Interpretation:**
   
   As the first method designed for universal inference of spatial multi-omics, SpatialFuser leverages the MCGATE module to accurately detect spatial distributions of tissue domains or cell types across diverse modalities and technology platforms, including highly heterogeneous tumor tissues.

2. **Accurate Cross-Slice Alignment:** 
   
   SpatialFuser provides multiple coordinate registration strategies tailored to different alignment scenarios：
   
      - **ICP algorithm** for slices with well-defined structural grids or regular anatomical boundaries.

      - **NDT algorithm** for sparse, noisy, or irregular spot distributions.

   By coupling alignment and integration, SpatialFuser jointly addresses these two tasks through an iteratively trained dual-layer architecture consisting of a matching layer based on the Sinkhorn algorithm and a fusion layer grounded in contrastive learning. This design enables:
   
      - **Continuous slices alignment**: ensuring smooth reconstruction of three-dimensional tissue structures.
      
      - **Align slices across differnt developmental stages**: capturing dynamic reorganization of tissue morphology during growth or regeneration.
      
      - **Cross-resolution alignment**: harmonizing datasets acquired from different platform.
      
      - **Cross-modality alignment**: registering tissue slices across different molecular layers.

3. **Spatial Muti-Omics Data Integration:** 
   
   SpatialFuser can effectively capture shared spatial molecular patterns across the different omics layers to correct modality bias. Its design offers several key advantages:
   
      - **Broad modality coverage**: supports diverse data types including epigenomics, transcriptomics, proteomics, and metabolomics.
      
      - **Robust cross-modality integration**: applicable to both strongly correlated omics(e.g., genome–transcriptome with near one-to-one correspondence) and weakly correlated modalities(e.g., transcriptome–proteome).
      
      - **Prevention of over-integration**: achieved through strict anchor selection and reconstruction supervision, ensuring that modality-specific features are preserved and biologically meaningful variation is not lost.

Application Examples
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1. **Fine Spatial Interpretation**
   
   - Detection of spatial domains in the **10X Visium** DLPFC dataset.
   
   - Detection of spatial domains in the **osmFISH** mouse somatosensory cortex dataset.
   
   - Detection of main celltype distribution in the **CODEX** human muscle-invasive bladder cancer tumor dataset.


2. **Integration and Alignment**
   
   - Integration and alignment of adjacent slices from the **BaristaSeq** mouse visual cortex dataset.
   
   - Integration and alignment of **Stereo-seq** data representing axolotl regenerative telencephalon at developmental stages 54 and 57.
   
   - Integration of homologous **Spatial ATAC–RNA-seq** slices.
   
   - Integration and alignment of transcriptomic (**MAGIC-seq**), proteomic (**PLATO**), and metabolomic (**MALDI-MSI**) data obtained from consecutive mouse cerebellum tissue slices.


.. toctree::
   :maxdepth: 3
   :caption: Contents:

   Installation/contents
   Tutorials/index
   API


Citation
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