SpatialCOC: an integrative framework for spatial continuous mapping and cross-omics correction in spatial multi-omics...

Area of Science:

• Computational Biology
• Genomics
• Spatial Transcriptomics

Background:

• Integrating spatial multi-omics data is challenging due to difficulties in uncovering spatial cell patterns and regulatory mechanisms.
• Biotechnological biases can lead to unstable results, hindering the full potential of multi-omics data analysis.
• Existing methods struggle with identifying continuous spatial domains and maintaining batch consistency.

Purpose of the Study:

• To introduce SpatialCOC, a novel framework for integrating spatial information with multi-omics data.
• To leverage spatial information as prior knowledge for learning omics-specific spatial distributions.
• To discover nonlinear correlations among different omics modalities while minimizing biotechnological biases.

Main Methods:

• SpatialCOC treats spatial information as prior knowledge to learn omics-specific spatial distributions.
• The framework identifies nonlinear correlations among multi-omics modalities.
• It is validated on diverse real-world datasets from various tissue sections and experimental techniques.

Main Results:

• SpatialCOC effectively identifies region-specific continuous spatial domains.
• The framework demonstrates robustness and batch-consistency across trajectory inferences.
• It successfully integrates multi-omics data with arbitrary dimensions, accommodating diverse data types.

Conclusions:

• SpatialCOC offers a robust and flexible framework for spatial multi-omics data integration.
• It provides novel insights into the interplay between spatial information and multi-omics modalities.
• The approach enhances the discovery of biological mechanisms by addressing data biases and improving analytical accuracy.