Bayesian joint analysis of heterogeneous genomics data.

Journal Article (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