# Multimodal Data Visualization and Denoising with Integrated Diffusion

arXiv: https://arxiv.org/abs/2102.06757 · HTML: https://mlsanity.com/paper/2102.06757/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 11 references that are in the map.
- First posted on arXiv 12 Feb 2021.

## Built on by (most cited first, 4 of 4)

- [Diffusion Transport Alignment](https://mlsanity.com/paper/2206.07305/) (2022)
- [Multiscale methods for signal selection in single-cell data](https://mlsanity.com/paper/2206.07760/) (2022)
- [Parametrized Power-Iteration Clustering for Directed Graphs](https://mlsanity.com/paper/2210.00310/) (2022)
- [Multi-view diffusion geometry using intertwined diffusion trajectories](https://mlsanity.com/paper/2512.01484/) (2025)

## Abstract (arXiv)

We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator. As real world data suffers from both local and global noise, we introduce mechanisms to optimally calculate a diffusion operator that reflects the combined information from both modalities. We show the utility of this joint operator in data denoising, visualization and clustering, performing better than other methods to integrate and analyze multimodal data. We apply our method to multi-omic data generated from blood cells, measuring both gene expression and chromatin accessibility. Our approach better visualizes the geometry of the joint data, captures known cross-modality associations and identifies known cellular populations. More generally, integrated diffusion is broadly applicable to multimodal datasets generated in many medical and biological systems.