Bayesian joint analysis of heterogeneous genomics data | Zendy

Priyadip Ray | Zendy; LingLing Zheng | Zendy; Joseph E. Lucas | Zendy; Lawrence Carin | Zendy

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Bayesian joint analysis of heterogeneous genomics data

Author(s) -

Priyadip Ray,

LingLing Zheng,

Joseph E. Lucas,

Lawrence Carin

Publication year - 2014

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btu064

Subject(s) - computer science , component (thermodynamics) , bayesian probability , data mining , genomics , key (lock) , source code , artificial intelligence , biology , gene , genetics , genome , physics , computer security , thermodynamics , operating system

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.

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