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SCALE: unsupervised multiscale domain identification in spatial omics data
Behnam Yousefi1,2, Darius P Schaub1,3, Robin Khatri1,4
1Institute of Medical Systems Bioinformatics, Center for Biomedical AI (bAIome), Center for Molecular Neurobiology (ZMNH), University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany.
We developed SCALE, a new algorithm for identifying hierarchical functional domains in spatial transcriptomics data. This tool reveals multiscale tissue organization, advancing our understanding of biological systems in health and disease.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Single-cell spatial transcriptomics maps cellular states within tissues.
- Biological systems exhibit hierarchical organization with multiscale functional domains.
- Identifying these domain hierarchies computationally is challenging.
Purpose of the Study:
- Introduce SCALE, an unsupervised algorithm for multiscale domain identification in spatial transcriptomics.
- Enable the discovery of hierarchical functional domains at various spatial scales.
- Provide a robust and scalable tool for analyzing complex tissue architectures.
Main Methods:
- SCALE employs deep learning-based graph representation learning.
- An entropy-based search algorithm is integrated for scale detection.
- The algorithm is validated on simulated and real-world spatial transcriptomics datasets.
Main Results:
- SCALE effectively identifies multiscale functional domains across diverse tissues (murine brain, kidney).
- Demonstrated robustness and scalability on Xenium and MERFISH data.
- Outperformed state-of-the-art methods by up to 191.1 percentage points.
Conclusions:
- SCALE is a user-friendly tool for uncovering hierarchical tissue organization.
- Facilitates deeper insights into tissue function and cellular interactions.
- Advances the study of biological systems in both health and disease contexts.
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