Bayesian joint analysis of heterogeneous genomics data

Priyadip Ray, Lingling Zheng, Joseph Lucas, Lawrence Carin

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

Summary: A non-parametric Bayesian factor model is proposed for joint analysis of multi-platform genomics data. The approach is based on factorizing the latent space (feature space) into a shared component and a data-specific component with the dimensionality of these components (spaces) inferred via a beta-Bernoulli process. The proposed approach is demonstrated by jointly analyzing gene expression/copy number variations and gene expression/methylation data for ovarian cancer patients, showing that the proposed model can potentially uncover key drivers related to cancer. © The Author 2014.
Original languageEnglish (US)
Pages (from-to)1370-1376
Number of pages7
JournalBioinformatics
Volume30
Issue number10
DOIs
StatePublished - May 15 2014
Externally publishedYes

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