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Bayesian joint analysis of heterogeneous genomics data.

Publication ,  Journal Article
Ray, P; Zheng, L; Lucas, J; Carin, L
Published in: Bioinformatics (Oxford, England)
May 2014

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 source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/.catherine.ll.zheng@gmail.comSupplementary data are available at Bioinformatics online.

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Published In

Bioinformatics (Oxford, England)

DOI

EISSN

1367-4811

ISSN

1367-4803

Publication Date

May 2014

Volume

30

Issue

10

Start / End Page

1370 / 1376

Related Subject Headings

  • Software
  • Ovarian Neoplasms
  • Humans
  • Genomics
  • Gene Expression Regulation
  • Female
  • DNA Methylation
  • DNA Copy Number Variations
  • Bioinformatics
  • Bayes Theorem
 

Citation

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Ray, P., Zheng, L., Lucas, J., & Carin, L. (2014). Bayesian joint analysis of heterogeneous genomics data. Bioinformatics (Oxford, England), 30(10), 1370–1376. https://doi.org/10.1093/bioinformatics/btu064
Ray, Priyadip, Lingling Zheng, Joseph Lucas, and Lawrence Carin. “Bayesian joint analysis of heterogeneous genomics data.Bioinformatics (Oxford, England) 30, no. 10 (May 2014): 1370–76. https://doi.org/10.1093/bioinformatics/btu064.
Ray P, Zheng L, Lucas J, Carin L. Bayesian joint analysis of heterogeneous genomics data. Bioinformatics (Oxford, England). 2014 May;30(10):1370–6.
Ray, Priyadip, et al. “Bayesian joint analysis of heterogeneous genomics data.Bioinformatics (Oxford, England), vol. 30, no. 10, May 2014, pp. 1370–76. Epmc, doi:10.1093/bioinformatics/btu064.
Ray P, Zheng L, Lucas J, Carin L. Bayesian joint analysis of heterogeneous genomics data. Bioinformatics (Oxford, England). 2014 May;30(10):1370–1376.

Published In

Bioinformatics (Oxford, England)

DOI

EISSN

1367-4811

ISSN

1367-4803

Publication Date

May 2014

Volume

30

Issue

10

Start / End Page

1370 / 1376

Related Subject Headings

  • Software
  • Ovarian Neoplasms
  • Humans
  • Genomics
  • Gene Expression Regulation
  • Female
  • DNA Methylation
  • DNA Copy Number Variations
  • Bioinformatics
  • Bayes Theorem