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• W4360616051 abstract "High-dimensional multimodal data arise 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 by alternating dimension reduction and alignment, AVIDA aligns the representations of 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 representation, 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." @default.
• W4360616051 created "2023-03-24" @default.
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• W4360616051 date "2023-04-01" @default.
• W4360616051 modified "2023-10-18" @default.
• W4360616051 title "AVIDA: An alternating method for visualizing and integrating data" @default.
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• W4360616051 doi "https://doi.org/10.1016/j.jocs.2023.101998" @default.
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