Directionally Dependent Multi-View Clustering Using Copula Model

• URL: http://arxiv.org/abs/2003.07494v2
• Date: Sat, 22 Aug 2020 15:34:44 GMT
• Title: Directionally Dependent Multi-View Clustering Using Copula Model
• Authors: Kahkashan Afrin, Ashif S. Iquebal, Mostafa Karimi, Allyson Souris, Se Yoon Lee, and Bani K. Mallick
• Abstract summary: In genomics studies, there is certain directional dependence between DNA expression, DNA methylation, and RNA expression. Most of the existing multi-view clustering methods either assume an independent structure or pair-wise (non-directional) dependency. We propose a copula-based multi-view clustering model where a copula enables the model to accommodate the directional dependence existing in the datasets.
• Score: 3.5097082077065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent biomedical scientific problems, it is a fundamental issue to integratively cluster a set of objects from multiple sources of datasets. Such problems are mostly encountered in genomics, where data is collected from various sources, and typically represent distinct yet complementary information. Integrating these data sources for multi-source clustering is challenging due to their complex dependence structure including directional dependency. Particularly in genomics studies, it is known that there is certain directional dependence between DNA expression, DNA methylation, and RNA expression, widely called The Central Dogma. Most of the existing multi-view clustering methods either assume an independent structure or pair-wise (non-directional) dependency, thereby ignoring the directional relationship. Motivated by this, we propose a copula-based multi-view clustering model where a copula enables the model to accommodate the directional dependence existing in the datasets. We conduct a simulation experiment where the simulated datasets exhibiting inherent directional dependence: it turns out that ignoring the directional dependence negatively affects the clustering performance. As a real application, we applied our model to the breast cancer tumor samples collected from The Cancer Genome Altas (TCGA).

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