vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Mar 14, 2022
- Source ID
- 10.1371/journal.pone.0265150
Entities
People
- Allen Tannenbaum
- Jiening Zhu
- Joseph O. Deasy
- Jung Hun Oh
Organizations
- Air Force Office of Scientific Research
- Foundation for the National Institutes of Health
- National Foundation for Cancer Research