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MULTIMODAL DATA VISUALIZATION AND DENOISING WITH INTEGRATED DIFFUSION.

Manik Kuchroo1,2, Abhinav Godavarthi3, Alexander Tong4

  • 1Yale University, Dept. of Neuro., Mila - Quebec AI Institute.

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Summary
This summary is machine-generated.

We developed integrated diffusion to combine noisy multimodal data. This method optimally denoises and visualizes combined datasets, revealing underlying data structures and associations.

Keywords:
data denoisingdata diffusiondimensionality reductionmanifold learningmultimodal data

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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.