AVIDA: Alternating method for Visualizing and Integrating Data

TL;DR

AVIDA is a novel framework that simultaneously aligns and reduces the dimensionality of high-dimensional multimodal data, effectively preserving dataset structures and enabling better visualization without requiring known sample or feature correspondences.

Contribution

It introduces a flexible, joint alignment and dimension reduction approach using optimal transport and t-SNE, improving structure preservation in multimodal data visualization.

Findings

AVIDA accurately aligns datasets without common features.

It better preserves local structures compared to existing methods.

It achieves comparable alignment performance with enhanced visualization quality.

Abstract

High-dimensional multimodal data arises in many scientific fields. The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. To tackle this challenge, we introduce AVIDA, a framework for simultaneously performing data alignment and dimension reduction. In the numerical experiments, Gromov-Wasserstein optimal transport and t-distributed stochastic neighbor embedding are used as the alignment and dimension reduction modules respectively. We show that AVIDA correctly aligns high-dimensional datasets without common features with four synthesized datasets and two real multimodal single-cell datasets. Compared to several existing methods, we demonstrate that AVIDA better preserves structures of individual datasets, especially distinct local structures in the joint low-dimensional visualization, while achieving comparable alignment performance. Such a property is important in multimodal single-cell data analysis as some biological processes are uniquely captured by one of the datasets. In general applications, other methods can be used for the alignment and dimension reduction modules.