An empirical Bayes approach to inferring large-scale gene association networks

Schäfer, Juliane; Strimmer, Korbinian

doi:10.1093/bioinformatics/bti062

Cited by 688 publications

(610 citation statements)

References 47 publications

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“…A major concern when evaluating relationships between genes based on their expression is that transcriptional co-regulation among many genes can give rise to indirect interaction effects in expression data, 36 and regular correlation networks cannot distinguish direct from indirect relationships. 37,38 To control for the indirect effects, we employed the approach developed by Schafer and Strimmer 36 based on the graphical Gaussian model (GGM) 39 to reconstruct a GGM network.…”

Section: Coordinated Expression Of Mtdna Genesmentioning

confidence: 99%

Population-level expression variability of mitochondrial DNA-encoded genes in humans

et al. 2014

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Human mitochondria contain multiple copies of a circular genome made up of double-stranded DNA (mtDNA) that encodes proteins involved in cellular respiration. Transcript abundance of mtDNA-encoded genes varies between human individuals, yet the level of variation in the general population has not been systematically assessed. In the present study, we revisited large-scale RNA sequencing data generated from lymphoblastoid cell lines of HapMap samples of European and African ancestry to estimate transcript abundance and quantify expression variation for mtDNA-encoded genes. In both populations, we detected up to over 100-fold difference in mtDNA gene expression between individuals. The marked variation was not due to differences in mtDNA copy number between individuals, but was shaped by the transcription of hundreds of nuclear genes. Many of these nuclear genes were co-expressed with one another, resulting in a module-enriched co-expression network. Significant correlations in expression between genes of the mtDNA and nuclear genomes were used to identify factors involved with the regulation of mitochondrial functions. In conclusion, we determined the baseline amount of variability in mtDNA gene expression in general human populations and cataloged a complete set of nuclear genes whose expression levels are correlated with those of mtDNA-encoded genes. Our findings will enable the integration of information from both mtDNA and nuclear genetic systems, and facilitate the discovery of novel regulatory pathways involving mitochondrial functions.

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Section: Coordinated Expression Of Mtdna Genesmentioning

confidence: 99%

Population-level expression variability of mitochondrial DNA-encoded genes in humans

et al. 2014

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“…Based on these large-scale expression data sets, we compare the resultant networks from graphical Gaussian models (GGMs) [11] and the proposed algorithm. The synthetic expression data are generated by SynTReN [16], which is well suited for the purpose of testing network learning strategies [17].…”

Section: Methodsmentioning

confidence: 99%

“…In Section 3 in the supplement file, we evaluate two widely used methods for inference from gene expression data and as a result choose partial correlation of gene expression data for the proposed framework. Partial correlation (PCOR) is indicative of direct interactions between a pair of variables/genes by eliminating the effects from the rest of variables/genes and therefore has been popular in the research towards the inference of gene regulatory relationship [11]. In other words, if the residuals of two time series, after removing the effect from the rest of the data by regression, are still correlated, there exists direct interaction between them.…”

Section: Bayes Random Fields (Brfs) Integrationmentioning

confidence: 99%

“…Such gene expression dynamics are important since they directly reveal the active components within the cell over time, thereby indicating certain interacting relationships. Regarding to how the time series data is used in network reconstruction, two types of methods, Dynamic Bayesian networks (DBNs) [1] and Graphical Gaussian models (GGMs) [11], account for a major part of current research.…”

Section: Supplement: Microarry Time Series Network Inferencementioning

confidence: 99%

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A Bayes Random Fields Approach for Integrative Large-Scale Regulatory Network Analysis

Yuan

2008

Journal of Integrative Bioinformatics

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SummaryWe present a Bayes-Random Fields framework which is capable of integrating unlimited data sources for discovering relevant network architecture of large-scale networks. The random field potential function is designed to impose a cluster constraint, teamed with a full Bayesian approach for incorporating heterogenous data sets. The probabilistic nature of our framework facilitates robust analysis in order to minimize the influence of noise inherent in the data on the inferred structure in a seamless and coherent manner. This is later proved in its applications to both large-scale synthetic data sets and Saccharomyces Cerevisiae data sets. The analytical and experimental results reveal the varied characteristic of different types of data and refelct their discriminative ability in terms of identifying direct gene interactions.

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“…Methods were assessed according to a scoring system (AUROC) that evaluates the number of false positives and correct answers as a smooth function of the acceptance threshold. The methods considered in the paper all infer causal networks, and include most of the well-known methodologies, such as Graphical Gaussian Models (GGM, [21], [7]), Sparse Regression (Lasso [22] and Elastic Net [24]), Time-varying Sparse Regression (Tesla [2]), Hierarchical Bayesian Regression models (HBR, [1]), Non-Homogeneous Hierarchical Bayesian models ( [11]), Automatic Relevance Determination in the context of Sparse Bayesian Regression (ARD-SBR, [20]), Bayesian Spline Autoregression (BSA, [17]), State Space Models (SSM, [3]), Gaussian processes (GP, [4]), Mutual information methods (ARACNE, [16]), Mixture Bayesian network models (MBN, [14]), and Gaussian Bayesian networks (BGe, [9]). Data were simulated using the computational model proposed by the Millar's group (Millar2010, [19]), in both deterministic and stochastic settings.…”

Section: Introductionmentioning

confidence: 99%

Assessing the effect of unknown widespread perturbations in complex systems using the ν-gap

Carignano

Jin

Webb

2015

2015 54th IEEE Conference on Decision and Control (CDC)

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Abstract-Pinpointing the exact locations of perturbations can help to detect and correct faults in large-scale systems, such as power grids (lost of transmission lines), internet (loss of servers) and biological systems (diseases). This paper outlines a mathematical framework to study the effect of perturbations on large-scale systems, with particular emphasis to biological applications. In particular, it focuses on wide-spread perturbations that target unknown components of the system. These problems are usually studied with genome-wide assays, which are becoming increasingly popular and accessible. However, analysis of the data sets produced by this technology remains challenging: genome-wide experiments are often inherently noisy, with a small number of measurements, and a low sampling rate. The paper first develops a simple yet powerful network inference tool based on LTIs. We compare this tool with the current state of the art. Then, as its major contribution, the paper develops a method for network differentiation, where it detects the effects of perturbations in large scale-systems. The method is based on the ν-gap, a control engineering tool that measures the distance between linear models. A major difference between this work and others, is that we look at changes in dynamics in links, as opposed to the standard differential expression analysis that focuses on changes in node concentrations. Through an illustrative model, the paper shows how perturbations impact certain links in the network, which can then be captured by differences between LTIs with the ν-gap.

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An empirical Bayes approach to inferring large-scale gene association networks

Cited by 688 publications

References 47 publications

Population-level expression variability of mitochondrial DNA-encoded genes in humans

Population-level expression variability of mitochondrial DNA-encoded genes in humans

A Bayes Random Fields Approach for Integrative Large-Scale Regulatory Network Analysis

Assessing the effect of unknown widespread perturbations in complex systems using the ν-gap

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