Abstract
Reconstructing a gene network from high-throughput molecular data is an important but challenging task, as the number of parameters to estimate easily is much larger than the sample size. A conventional remedy is to regularize or penalize the model likelihood. In network models, this is often done locally in the neighborhood of each node or gene. However, estimation of the many regularization parameters is often difficult and can result in large statistical uncertainties. In this paper we propose to combine local regularization with global shrinkage of the regularization parameters to borrow strength between genes and improve inference. We employ a simple Bayesian model with nonsparse, conjugate priors to facilitate the use of fast variational approximations to posteriors. We discuss empirical Bayes estimation of hyperparameters of the priors, and propose a novel approach to rank-based posterior thresholding. Using extensive model- and data-based simulations, we demonstrate that the proposed inference strategy outperforms popular (sparse) methods, yields more stable edges, and is more reproducible. The proposed method, termed ShrinkNet, is then applied to Glioblastoma to investigate the interactions between genes associated with patient survival.
Original language | English |
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Pages (from-to) | 41-68 |
Number of pages | 28 |
Journal | The Annals of Applied Statistics |
Volume | 11 |
Issue number | 1 |
DOIs | |
Publication status | Published - Mar 2017 |
Funding
Supported in part by the Center for Medical Systems Biology (CMSB), established by the Netherlands Genomics Initiative/Netherlands Organization for Scientific Research (NGI/NWO), and the European Union Grant \u201CEpiRadBio,\u201D nr. FP7-269553.
Funders | Funder number |
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Centre for Medical Systems Biology | |
Medical Research Council | MC_UP_0801/1, MC_UU_00002/10 |
European Commission | 320637, FP7-269553 |