Exploring Spatial Multi-Omics Integration: An Interactive Infographic

This infographic translates complex scientific concepts into an easily digestible visual narrative, covering everything from data acquisition challenges to the latest deep learning integration methods and their applications in disease understanding.

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About the Research

This infographic is based on our comprehensive review of computational strategies for integrating spatial metabolomics and spatial transcriptomics data. The integration of these modalities allows us to connect the genetic blueprint with its functional output in their native tissue environment, creating a holistic understanding of biological processes. We delve into how different ‘omics’ technologies capture data at varying scales, the challenges of aligning disparate resolutions, and how advanced deep learning models are forging a shared latent space to uncover non-linear relationships.

Key areas explored include:

• Resolution Mismatch and Alignment: Techniques for handling diverse spatial resolutions and tissue deformations.
• Data Sparsity and Noise: Strategies for imputation and accounting for signal-to-noise differences.
• Latent Space Learning: The role of Variational Autoencoders (VAEs), Graph Neural Networks (GNNs), and Transformers in integration.
• Biological Interpretation: Methods for linking integrated data back to specific biological processes.
• Applications: Insights gained in disease contexts like oncology and neurodegenerative diseases.
• Future Directions: Emerging algorithmic innovations and the path towards clinical translation.

We hope this visual summary provides a clear and engaging overview of this exciting field!