AVIDA: Alternating method for Visualizing and Integrating Data
This addresses the problem of integrating multimodal data for researchers in fields like single-cell analysis, though it appears incremental as it combines existing methods like optimal transport and t-SNE.
The paper tackles the challenge of aligning high-dimensional multimodal datasets without known correspondences by introducing AVIDA, a framework that simultaneously performs data alignment and dimension reduction, demonstrating that it better preserves local structures in joint visualizations while achieving comparable alignment performance on synthetic and real single-cell datasets.
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.