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SAME

Spatial Alignment of Multimodal Experiments

SAME Overview

SAME is a computational framework for aligning and integrating spatial omics data across serial tissue sections and modalities (e.g., proteins, transcripts, metabolites). SAME introduces space-tearing transforms, enabling controlled, localized topological disruptions during cross-sectional alignment.

Key Features

  • Topology-flexible transforms: Unlike rigid registration methods, SAME allows controlled space-tearing events where spatial relationships can break (e.g., when a cell is missing in one section)

  • Mixed Integer Programming: Uses Gurobi MIP solver for optimal cell matching with spatial constraints via Delaunay triangulation. Leverages Gurobi MIP solver's lazy constraint feature to add constraints on-demand instead of upfront.

  • Metacell support: Offers graph simplification for handling large datasets and for large space tears(~100k+ cells) efficiently

  • Lazy constraints: Memory-efficient constraint generation instead of enumerating all O(n×k³) constraints upfront.

  • Sliding window: Offers processing arbitrarily large spatial regions in overlapping windows with automatic merging when regions are too large to be processed in a single step.

Quick Example

from src import run_same, init_optim_params

# Basic matching
matches, var_out = run_same(
    ref_df=reference_data,
    aligned_df=moving_data,
    commonCT=['CellTypeA', 'CellTypeB', 'CellTypeC'],
    outprefix='results/'
)

print(f"Found {len(matches)} matches")

When to Use SAME

SAME is designed for spatial omics integration tasks where:

  1. Serial sections: Aligning adjacent tissue sections with potentially different cells
  2. Multi-modal data: Integrating different spatial technologies (e.g., ISS + IMC)
  3. Missing cells: Handling cases where cells appear/disappear between sections
  4. Tissue deformation: Accounting for non-rigid tissue changes

Citation

If you use SAME in your research, please cite:

Aditya Pratapa, Siavash Mansouri, Nadezhda Nikulina, Bruno Matuck, Marc A. Schneider, Kevin Matthew Byrd, Rajkumar Savai, Purushothama Rao Tata, and Rohit Singh. "SAME: Topology-flexible transforms enable robust integration of multimodal spatial omics." bioRxiv (2025): 2025-07. https://doi.org/10.1101/2025.07.12.664419

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