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A Bayesian Nonparametric Model for Integrative Clustering of Omics Data

  • Iliana PenevaEmail author
  • Richard S. Savage
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 296)

Abstract

Cancer is a complex disease, driven by a range of genetic and environmental factors. Many integrative clustering methods aim to provide insight into the mechanisms underlying cancer but few of them are computationally efficient and able to estimate the number of subtypes. We have developed a Bayesian nonparametric model for combined data integration and clustering called BayesCluster, which aims to identify cancer subtypes and addresses many of the issues faced by the existing integrative methods. The proposed method can integrate and use the information from multiple different datasets, and offers better cluster interpretability by using nonlocal priors. We incorporate feature learning because of the large number of predictors, and use a Dirichlet process mixture model approach to produce the patient subgroups. We ensure tractable inference with simulated annealing. We apply the model to datasets from the Cancer Genome Atlas project of glioblastoma multiforme, which contains clinical and biological data about cancer patients with extremely poor prognosis of survival. By combining all available information we are able to be better identify clinically meaningful subtypes of glioblastoma.

Keywords

Bayesian nonparametrics Data integration Glioblastoma Mixture models Non-local priors 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of WarwickWarwickUK
  2. 2.Department of StatisticsUniversity of WarwickWarwickUK

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