Direct estimation of differential networks under high‐dimensional nonparanormal graphical models

2019 ◽  
Vol 48 (2) ◽  
pp. 187-203 ◽  
Author(s):  
Qingyang Zhang
Keyword(s):  
Graphical Models ◽  
High Dimensional ◽  
Direct Estimation ◽  
Differential Networks

Joint estimation of multiple high-dimensional Gaussian copula graphical models

2017 ◽  
Vol 59 (3) ◽  
pp. 289-310 ◽  
Author(s):  
Yong He ◽  
Xinsheng Zhang ◽  
Jiadong Ji ◽  
Bin Liu
Keyword(s):  
Graphical Models ◽  
Joint Estimation ◽  
Gaussian Copula ◽  
High Dimensional

scholarly journals On the Letac-Massam conjecture and existence of high dimensional Bayes estimators for graphical models

2020 ◽  
Vol 14 (1) ◽  
pp. 580-604
Author(s):  
Emanuel Ben-David ◽  
Bala Rajaratnam
Keyword(s):  
Graphical Models ◽  
High Dimensional ◽  
Bayes Estimators

scholarly journals The ‘Un-Shrunk’ Partial Correlation in Gaussian Graphical Models

2020 ◽  
Author(s):  
Victor Bernal ◽  
Rainer Bischoff ◽  
Peter Horvatovich ◽  
Victor Guryev ◽  
Marco Grzegorczyk
Keyword(s):  
Graphical Models ◽  
Regulatory Networks ◽  
Partial Correlation ◽  
High Dimensional ◽  
Dimensional Problem ◽  
Gaussian Graphical Models ◽  
High Dimensional Problem ◽  
Non Linear ◽  
Partial Correlations ◽  
Molecular Profiles

Abstract Background: In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes (‘high dimensional problem’). Shrinkage methods address this issue by learning a regularized GGM. However, it is an open question how the shrinkage affects the final result and its interpretation.Results: We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as the ‘un-shrunk’ partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. We apply the ‘un-shrunk’ method to two gene expression datasets from Escherichia coli and Mus musculus.Conclusions: GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the “high-dimensional” problem. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results.

scholarly journals The ‘un-shrunk’ partial correlation in Gaussian graphical models

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Victor Bernal ◽  
Rainer Bischoff ◽  
Peter Horvatovich ◽  
Victor Guryev ◽  
Marco Grzegorczyk
Keyword(s):  
Graphical Models ◽  
Regulatory Networks ◽  
Partial Correlation ◽  
High Dimensional ◽  
Dimensional Problem ◽  
Gaussian Graphical Models ◽  
High Dimensional Problem ◽  
Non Linear ◽  
Partial Correlations ◽  
Molecular Profiles

Abstract Background In systems biology, it is important to reconstruct regulatory networks from quantitative molecular profiles. Gaussian graphical models (GGMs) are one of the most popular methods to this end. A GGM consists of nodes (representing the transcripts, metabolites or proteins) inter-connected by edges (reflecting their partial correlations). Learning the edges from quantitative molecular profiles is statistically challenging, as there are usually fewer samples than nodes (‘high dimensional problem’). Shrinkage methods address this issue by learning a regularized GGM. However, it remains open to study how the shrinkage affects the final result and its interpretation. Results We show that the shrinkage biases the partial correlation in a non-linear way. This bias does not only change the magnitudes of the partial correlations but also affects their order. Furthermore, it makes networks obtained from different experiments incomparable and hinders their biological interpretation. We propose a method, referred to as ‘un-shrinking’ the partial correlation, which corrects for this non-linear bias. Unlike traditional methods, which use a fixed shrinkage value, the new approach provides partial correlations that are closer to the actual (population) values and that are easier to interpret. This is demonstrated on two gene expression datasets from Escherichia coli and Mus musculus. Conclusions GGMs are popular undirected graphical models based on partial correlations. The application of GGMs to reconstruct regulatory networks is commonly performed using shrinkage to overcome the ‘high-dimensional problem’. Besides it advantages, we have identified that the shrinkage introduces a non-linear bias in the partial correlations. Ignoring this type of effects caused by the shrinkage can obscure the interpretation of the network, and impede the validation of earlier reported results.

scholarly journals A Focused Information Criterion for Graphical Models in fMRI Connectivity with High-Dimensional Data

2015 ◽  
Author(s):  
Eugen Pircalabelu ◽  
Gerda Claeskens ◽  
Sara Jahfari ◽  
Lourens Waldorp
Keyword(s):  
Graphical Models ◽  
High Dimensional Data ◽  
Information Criterion ◽  
High Dimensional ◽  
Focused Information Criterion

A two-step method for estimating high-dimensional Gaussian graphical models

2020 ◽  
Vol 63 (6) ◽  
pp. 1203-1218
Author(s):  
Yuehan Yang ◽  
Ji Zhu
Keyword(s):  
Graphical Models ◽  
High Dimensional ◽  
Gaussian Graphical Models ◽  
Step Method

scholarly journals Regularized rank-based estimation of high-dimensional nonparanormal graphical models

2012 ◽  
Vol 40 (5) ◽  
pp. 2541-2571 ◽  
Author(s):  
Lingzhou Xue ◽  
Hui Zou
Keyword(s):  
Graphical Models ◽  
High Dimensional

scholarly journals Inference in High-Dimensional Graphical Models

2018 ◽  
pp. 325-350 ◽  
Author(s):  
Jana Janková ◽  
Sara van de Geer
Keyword(s):  
Graphical Models ◽  
High Dimensional
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