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AVIDA: Alternating method for Visualizing and Integrating Data
Quantitative Methods · Submitted 31 May 2022
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim 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.
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The abstract, sentence by sentence
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.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | High-dimensional multimodal data arises in many scientific fields. | background 1.00 | 0.04 | 0.02 | asserted 1.00 |
| s1 | The integration of multimodal data becomes challenging when there is no known correspondence between the samples and the features of different datasets. | background 0.75 | 0.05 | 0.02 | asserted 0.99 |
| s2 | To tackle this challenge, we introduce AVIDA, a framework for simultaneously performing data alignment and dimension reduction. | contribution 0.99 | 0.85 | 0.11 | asserted 0.98 |
| s3 | In the numerical experiments, Gromov-Wasserstein optimal transport and t-distributed stochastic neighbor embedding are used as the alignment and dimension reduction modules respectively. | method 1.00 | 0.76 | 0.03 | asserted 1.00 |
| s4 | We show that AVIDA correctly aligns high-dimensional datasets without common features with four synthesized datasets and two real multimodal single-cell datasets. | result 1.00 | 0.97 | 0.40 | asserted 1.00 |
| s5 | 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. | result 1.00 | 0.96 | 0.58 | asserted 1.00 |
| s6 | Such a property is important in multimodal single-cell data analysis as some biological processes are uniquely captured by one of the datasets. | interpretation 1.00 | 0.15 | 0.06 | asserted 0.99 |
| s7 | In general applications, other methods can be used for the alignment and dimension reduction modules. | method 0.66 | 0.07 | 0.03 | hedged possibility 0.59 |
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Original 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.
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Cites as background 2 papers
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When the datasets are represented as graphs with edges annotating pairs of similar data points within each dataset, the integration problem can be addressed using various graph alignment methods [ref] .
Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks · Introduction · 0.89
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Principal Component Analysis (PCA) [ref] is the most traditional linear technique used in dimensionality reduction but there are many popular non-linear techniques, such as Local Linear Embedding [ref] , Isomap [ref] , UMAP [ref] , and t-SNE [ref] .
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction · Introduction · 0.98
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