vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer

Autor: Jiening Zhu, Jung Hun Oh, Joseph O. Deasy, Allen R. Tannenbaum
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: PLoS ONE, Vol 17, Iss 3 (2022)
Druh dokumentu: article
ISSN: 1932-6203
Popis: 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.
Databáze: Directory of Open Access Journals
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