Literature DB >> 24489367

Bayesian joint analysis of heterogeneous genomics data.

Priyadip Ray1, Lingling Zheng, Joseph Lucas, Lawrence Carin.   

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.
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.

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Mesh:

Year:  2014        PMID: 24489367     DOI: 10.1093/bioinformatics/btu064

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  20 in total

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