Gene network reconstruction using global-local shrinkage priors

Leday, Gwenaël G. R.; Gunst, Mathisca C. M. de; Kpogbezan, Gino B.; Vaart, Aad van der; Wieringen, Wessel N. van; Wiel, Mark A. van de

doi:10.48550/arxiv.1510.03771

Cited by 2 publications

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“…We also acknowledge that, like numerous other node-wise regression methods in the literature (Leday et al , 2015;Meinshausen & Bühlmann, 2006;Peng et al , 2012;Kolar et al , 2010;Ha et al , 2020), our model does not explicitly constrain positive definiteness for all possible covariate levels. One possible route is to construct a joint prior (and hence a generative model) on the entire precision matrix elements, Ω(X), ∀X, such that it lies in the cone of positive definite matrices.…”

Section: Discussionmentioning

confidence: 99%

“…To deal with potential high dimensionality, we use global-local priors to effectively induce sparsity into the underlying graphs, and we use posterior probabilities to infer important graph edges for a given set of covariates. Based on node-wise regressions, that have been shown with good performance for graph reconstruction (Leday et al , 2015;Meinshausen & Bühlmann, 2006;Ha et al , 2020), our method is primarily focused and recommended for applied settings in which the focus is on edge detection rather than estimation of the full precision or covariance matrix. This modeling framework allows researchers to study how clinical and biological factors lead to heterogeneous genomic or proteomic networks varying across patients.…”

Section: Discussionmentioning

confidence: 99%

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Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

Wang¹,

Baladandayuthapan²,

Kaseb³

et al. 2021

Preprint

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Graphical models are commonly used to discover associations within gene or protein networks for complex diseases such as cancer. Most existing methods estimate a single graph for a population, while in many cases, researchers are interested in characterizing the heterogeneity of individual networks across subjects with respect to subject-level covariates. Examples include assessments of how the network varies with patient-specific prognostic scores or comparisons of tumor and normal graphs while accounting for tumor purity as a continuous predictor. In this paper, we propose a novel edge regression model for undirected graphs, which estimates conditional dependencies as a function of subject-level covariates. Bayesian shrinkage algorithms are used to induce sparsity in the underlying graphical models. We assess our model performance through simulation studies focused on comparing tumor and normal graphs while adjusting for tumor purity and a case study assessing how blood protein networks in hepatocellular carcinoma patients vary with severity of disease, measured by HepatoScore, a novel biomarker signature measuring disease severity.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

Wang¹,

Baladandayuthapan²,

Kaseb³

et al. 2021

Preprint

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“…Variational Bayes method and Gibbs sampling In this section we develop a variational Bayes approach to approximate the (marginal) posterior distributions of the parameters β i,r , τ 2 i,0 , τ 2 i,1 , σ 2 i in model ( 4). The algorithm is similar, but still significantly different, from the algorithm developed in Leday et al (2015) for the model (3). In the following we can see that, due to (4), the variational parameters have a form which renders the implementation of (4) much more challenging.…”

Section: Modelmentioning

confidence: 99%

“…Previously, we proposed a Bayesian formulation of the SEM (Leday et al, 2015). In this Bayesian SEM (henceforth BSEM) the structural model ( 2) is endowed with the following prior:…”

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confidence: 99%

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An empirical Bayes approach to network recovery using external knowledge

et al. 2017

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Reconstruction of a high-dimensional network may benefit substantially from the inclusion of prior knowledge on the network topology. In the case of gene interaction networks such knowledge may come for instance from pathway repositories like KEGG, or be inferred from data of a pilot study. The Bayesian framework provides a natural means of including such prior knowledge. Based on a Bayesian Simultaneous Equation Model, we develop an appealing Empirical Bayes (EB) procedure that automatically assesses the agreement of the used prior knowledge with the data at hand. We use variational Bayes method for posterior densities approximation and compare its accuracy with that of Gibbs sampling strategy. Our method is computationally fast, and can outperform known competitors. In a simulation study, we show that accurate prior data can greatly improve the reconstruction of the network, but need not harm the reconstruction if wrong. We demonstrate the benefits of the method in an analysis of gene expression data from GEO. In particular, the edges of the recovered network have superior reproducibility (compared to that of competitors) over resampled versions of the data.

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Gene network reconstruction using global-local shrinkage priors

Cited by 2 publications

References 0 publications

Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer

An empirical Bayes approach to network recovery using external knowledge

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