Bayesian joint analysis of heterogeneous genomics data.
CONCLUSIONS
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.


METHODS
The source code for this model is written in MATLAB and has been made publicly available at https://sites.google.com/site/jointgenomics/.


BACKGROUND
catherine.ll.zheng@gmail.com


BACKGROUND
Supplementary data are available at Bioinformatics online.
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.
Priyadip
P
Ray
Lingling
L
Zheng
Joseph
J
Lucas
Lawrence
L
Carin
D001499:Bayes Theorem; D056915:DNA Copy Number Variations; D019175:DNA Methylation; D005260:Female; D005786:Gene Expression Regulation; D023281:Genomics / Q000379:methods*; D006801:Humans; D010051:Ovarian Neoplasms / Q000235:genetics; D012984:Software