Priyadip Ray1, Lingling Zheng, Joseph Lucas, Lawrence Carin. 1. G.S.Sanyal School of Telecommunications, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal 721302, India, Computational Biology & Bioinformatics, Duke University, Durham, NC 27705, USA, Quintiles, Durham, NC 27703, USA and Electrical & Computer Engineering Department, Duke University, Durham, NC 27705, USA.
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.
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 cancerpatients, 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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