MULTIMODAL DATA VISUALIZATION AND DENOISING WITH INTEGRATED DIFFUSION

Area of Science:

• Computational Biology
• Data Science
• Signal Processing

Background:

• Real-world multimodal data often contains both local and global noise.
• Combining information from different sensors presents challenges due to noise and data heterogeneity.

Purpose of the Study:

• To introduce an integrated diffusion method for effectively combining multimodal data.
• To develop an optimal diffusion operator that preserves essential data features despite noise.
• To demonstrate the method's utility in denoising and visualizing complex biological datasets.

Main Methods:

• Constructing an integrated diffusion operator by combining low-frequency eigenvectors from individual modalities.
• Developing mechanisms to optimally calculate the diffusion operator, considering both local and global data properties.
• Applying the integrated diffusion operator to multimodal toy data and multi-omic data (gene expression and chromatin accessibility).

Main Results:

• The integrated diffusion operator effectively denoises multimodal data.
• The method enhances the visualization of integrated data geometry.
• It successfully captures known cross-modality associations in multi-omic blood cell data.

Conclusions:

• Integrated diffusion provides a robust framework for analyzing noisy multimodal data.
• The approach is broadly applicable across various scientific fields utilizing multimodal datasets.
• This method improves the understanding of complex biological systems by integrating diverse data types.