Bayesian joint analysis of heterogeneous genomics data

Priyadip Ray; Lingling Zheng; Joseph Lucas; Lawrence Carin

doi:10.1093/bioinformatics/btu064

Bayesian joint analysis of heterogeneous genomics data

Priyadip Ray, Lingling Zheng, Joseph Lucas, Lawrence Carin

Research output: Contribution to journal › Article › peer-review

63 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 language	English (US)
Pages (from-to)	1370-1376
Number of pages	7
Journal	Bioinformatics
Volume	30
Issue number	10
DOIs	https://doi.org/10.1093/bioinformatics/btu064
State	Published - May 15 2014
Externally published	Yes

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AU - Zheng, Lingling

AU - Lucas, Joseph

AU - Carin, Lawrence

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AB - 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.

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Bayesian joint analysis of heterogeneous genomics data

Abstract

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