- Bayesian Anal.
- Advance publication (2018), 24 pages.
Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior
Selecting informative nodes over large-scale networks becomes increasingly important in many research areas. Most existing methods focus on the local network structure and incur heavy computational costs for the large-scale problem. In this work, we propose a novel prior model for Bayesian network marker selection in the generalized linear model (GLM) framework: the Thresholded Graph Laplacian Gaussian (TGLG) prior, which adopts the graph Laplacian matrix to characterize the conditional dependence between neighboring markers accounting for the global network structure. Under mild conditions, we show the proposed model enjoys the posterior consistency with a diverging number of edges and nodes in the network. We also develop a Metropolis-adjusted Langevin algorithm (MALA) for efficient posterior computation, which is scalable to large-scale networks. We illustrate the superiorities of the proposed method compared with existing alternatives via extensive simulation studies and an analysis of the breast cancer gene expression dataset in the Cancer Genome Atlas (TCGA).
Bayesian Anal., Advance publication (2018), 24 pages.
First available in Project Euclid: 5 January 2019
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Digital Object Identifier
Cai, Qingpo; Kang, Jian; Yu, Tianwei. Bayesian Network Marker Selection via the Thresholded Graph Laplacian Gaussian Prior. Bayesian Anal., advance publication, 5 January 2019. doi:10.1214/18-BA1142. https://projecteuclid.org/euclid.ba/1546657330
- Supplementary file 1 for “Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior”. Supplementary materials available at Bayesian Analysis online includes proofs of the theoretical results.
- Supplementary file 2 for “Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior”. Supplementary materials available at Bayesian Analysis online includes results for real data analysis.