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In recent biomedical scientific problems, it is a fundamental issue tointegratively cluster a set of objects from multiple sources of datasets. Suchproblems are mostly encountered in genomics, where data is collected fromvarious sources, and typically represent distinct yet complementaryinformation. Integrating these data sources for multi-source clustering ischallenging due to their complex dependence structure including directionaldependency. Particularly in genomics studies, it is known that there is certaindirectional dependence between DNA expression, DNA methylation, and RNAexpression, widely called The Central Dogma.  Most of the existing multi-view clustering methods either assume anindependent structure or pair-wise (non-directional) dependency, therebyignoring the directional relationship. Motivated by this, we propose acopula-based multi-view clustering model where a copula enables the model toaccommodate the directional dependence existing in the datasets. We conduct asimulation experiment where the simulated datasets exhibiting inherentdirectional dependence: it turns out that ignoring the directional dependencenegatively affects the clustering performance. As a real application, weapplied our model to the breast cancer tumor samples collected from The CancerGenome Altas (TCGA).

Directionally Dependent Multi-View Clustering Using Copula Model