ArticlePloS one2022
vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- A sub-Riemannian model of the motor cortex with Wasserstein distance.Frontiers in computational neuroscience · 2026Article
- HABiC: an algorithm based on the exact computation of the Kantorovich-Rubinstein optimizer for binary classification in transcriptomics.Bioinformatics (Oxford, England) · 2025Article
- Biological correlates associated with high-risk breast cancer patients identified using a computational method.NPJ breast cancer · 2025Article
- Wasserstein HOG: Local Directionality Extraction via Optimal Transport.IEEE transactions on medical imaging · 2024Article
- Cancer Informatics for Cancer Centers: Sharing Ideas on How to Build an Artificial Intelligence-Ready Informatics Ecosystem for Radiation Oncology.JCO clinical cancer informatics · 2023Article
- Geometric graph neural networks on multi-omics data to predict cancer survival outcomes.Computers in biology and medicine · 2023Article
- The maximum entropy principle for compositional data.BMC bioinformatics · 2022Article
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Abstract
In this paper, we present a network-based clustering method, called vector Wasserstein clustering (vWCluster), based on the vector-valued Wasserstein distance derived from optimal mass transport (OMT) theory. This approach allows for the natural integration of multi-layer representations of data in a given network from which one derives clusters via a hierarchical clustering approach. In this study, we applied the methodology to multi-omics data from the two largest breast cancer studies. The resultant clusters showed significantly different survival rates in Kaplan-Meier analysis in both datasets. CIBERSORT scores were compared among the identified clusters. Out of the 22 CIBERSORT immune cell types, 9 were commonly significantly different in both datasets, suggesting the difference of tumor immune microenvironment in the clusters. vWCluster can aggregate multi-omics data represented as a vectorial form in a network with multiple layers, taking into account the concordant effect of heterogeneous data, and further identify subgroups of tumors in terms of mortality.
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