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. © The Author 2014.
CITATION STYLE
Ray, P., Zheng, L., Lucas, J., & Carin, L. (2014). Bayesian joint analysis of heterogeneous genomics data. Bioinformatics, 30(10), 1370–1376. https://doi.org/10.1093/bioinformatics/btu064
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