Bayesian Consensus Clustering
This work addresses the challenge of integrating heterogeneous biomedical data for more accurate disease subtyping, representing an incremental improvement over prior clustering approaches.
The authors tackled the problem of clustering objects from multiple data sources by proposing a Bayesian integrative model that estimates both consensus and source-specific clusterings, demonstrating improved robustness and power over existing methods in breast cancer subtype identification using TCGA data.
The task of clustering a set of objects based on multiple sources of data arises in several modern applications. We propose an integrative statistical model that permits a separate clustering of the objects for each data source. These separate clusterings adhere loosely to an overall consensus clustering, and hence they are not independent. We describe a computationally scalable Bayesian framework for simultaneous estimation of both the consensus clustering and the source-specific clusterings. We demonstrate that this flexible approach is more robust than joint clustering of all data sources, and is more powerful than clustering each data source separately. This work is motivated by the integrated analysis of heterogeneous biomedical data, and we present an application to subtype identification of breast cancer tumor samples using publicly available data from The Cancer Genome Atlas. Software is available at http://people.duke.edu/~el113/software.html.