Bayesian joint analysis of heterogeneous genomics data.

Published

Journal Article

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. AVAILABILITY AND IMPLEMENTATION: The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/. CONTACT: catherine.ll.zheng@gmail.com SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Full Text

Duke Authors

Cited Authors

  • Ray, P; Zheng, L; Lucas, J; Carin, L

Published Date

  • May 2014

Published In

Volume / Issue

  • 30 / 10

Start / End Page

  • 1370 - 1376

PubMed ID

  • 24489367

Pubmed Central ID

  • 24489367

Electronic International Standard Serial Number (EISSN)

  • 1367-4811

International Standard Serial Number (ISSN)

  • 1367-4803

Digital Object Identifier (DOI)

  • 10.1093/bioinformatics/btu064

Language

  • eng