vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer.
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...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0265150 |
| _version_ | 1797818569513238528 |
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| author | Jiening Zhu Jung Hun Oh Joseph O Deasy Allen R Tannenbaum |
| author_facet | Jiening Zhu Jung Hun Oh Joseph O Deasy Allen R Tannenbaum |
| author_sort | Jiening Zhu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-13T09:09:50Z |
| format | Article |
| id | doaj.art-69159ca81f3549e49fd5f8ab16aa0189 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-03-13T09:09:50Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-69159ca81f3549e49fd5f8ab16aa01892023-05-27T05:31:36ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01173e026515010.1371/journal.pone.0265150vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer.Jiening ZhuJung Hun OhJoseph O DeasyAllen R TannenbaumIn 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.https://doi.org/10.1371/journal.pone.0265150 |
| spellingShingle | Jiening Zhu Jung Hun Oh Joseph O Deasy Allen R Tannenbaum vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer. PLoS ONE |
| title | vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer. |
| title_full | vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer. |
| title_fullStr | vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer. |
| title_full_unstemmed | vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer. |
| title_short | vWCluster: Vector-valued optimal transport for network based clustering using multi-omics data in breast cancer. |
| title_sort | vwcluster vector valued optimal transport for network based clustering using multi omics data in breast cancer |
| url | https://doi.org/10.1371/journal.pone.0265150 |
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