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Title: AVIDA: Alternating method for Visualizing and Integrating Data
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.  more » « less
Award ID(s):
2031883
NSF-PAR ID:
10344561
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Journal of computational science
Volume:
68
ISSN:
1877-7503
Page Range / eLocation ID:
101998
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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