Testing Differential Gene Networks under Nonparanormal Graphical Models with False Discovery Rate Control

Zhang, Qingyang

doi:10.3390/genes11020167

Cited by 3 publications

(3 citation statements)

References 24 publications

(42 reference statements)

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“…This feature makes a key advantage of GGM that it avoids spurious correlations. The nonparanormal graphical model, one derivative of GGM, is a semiparametric generalization for continuous variables and has emerged as an important tool for modeling dependency structure between items ( Mulgrave and Ghosal, 2020 ; Xue and Zou, 2012 ; Zhang, 2019 , 2020 ). These models can be incorporated to precisely infer the dependency structures of biomolecules ( Liu et al., 2012 ; Yin and Li, 2011 ; Zhang et al., 2016 ).…”

Section: Resultsmentioning

confidence: 99%

Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin

Zheng

et al. 2021

iScience

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Summary Context-specific activities of transcription regulators (TRs) in the nucleus modulate spatiotemporal gene expression precisely. Using the largest ChIP-seq data and chromatin loops in the human K562 cell line, we initially interrogated TR cooperation in 3D chromatin via a graphical model and revealed many known and novel TRs manipulating context-specific pathways. To explore TR cooperation across broad tissue/cell types, we systematically leveraged large-scale open chromatin profiles, computational footprinting, and high-resolution chromatin interactions to investigate tissue/cell type-specific TR cooperation. We first delineated a landscape of TR cooperation across 40 human tissue/cell types. Network modularity analyses uncovered the commonality and specificity of TR cooperation in different conditions. We also demonstrated that TR cooperation information can better interpret the disease-causal variants identified by genome-wide association studies and recapitulate cell states during neural development. Our study characterizes shared and unique patterns of TR cooperation associated with the cell type specificity of gene regulation in 3D chromatin.

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Section: Resultsmentioning

confidence: 99%

Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin

Zheng

et al. 2021

iScience

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“…These extensions differ from the Gaussian copula graphical model [14,25,33,43] in that the nonparanormal graphical model concurrently estimates the transformation functions and the precision matrices. Nonparanormal graphical model approaches have been applied to discrete data models of interactions between genes [38] and to test differential gene networks [57].…”

Section: Introductionmentioning

confidence: 99%

Regression‐based Bayesian estimation and structure learning for nonparanormal graphical models

Mulgrave

Ghosal

2022

Statistical Analysis

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A nonparanormal graphical model is a semiparametric generalization of a Gaussian graphical model for continuous variables in which it is assumed that the variables follow a Gaussian graphical model only after some unknown smooth monotone transformations. We consider a Bayesian approach to inference in a nonparanormal graphical model in which we put priors on the unknown transformations through a random series based on B‐splines. We use a regression formulation to construct the likelihood through the Cholesky decomposition on the underlying precision matrix of the transformed variables and put shrinkage priors on the regression coefficients. We apply a plug‐in variational Bayesian algorithm for learning the sparse precision matrix and compare the performance to a posterior Gibbs sampling scheme in a simulation study. We finally apply the proposed methods to a microarray dataset. The proposed methods have better performance as the dimension increases, and in particular, the variational Bayesian approach has the potential to speed up the estimation in the Bayesian nonparanormal graphical model without the Gaussianity assumption while retaining the information to construct the graph.

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“…To study the different roles of the cell cycle pathway in the two subtypes of breast cancer, including luminal A subtype and basal-like subtype using a TCGA (The Cancer Genome Atlas) gene expression dataset, Zhang [10] considers a computational pipeline of detecting differential substructure between two nonparanormal graphical models with false discovery rate control. The proposed approach extends the hierarchical testing method introduced by Liu [11] to a more flexible semiparametric framework and provides a convenient tool for modeling the dependency structure between non-Gaussian data while maintaining the good interpretability and computational convenience of Gaussian graphical models.…”

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confidence: 99%

Statistics in the Genomic Era

Jiang

2020

Genes

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Testing Differential Gene Networks under Nonparanormal Graphical Models with False Discovery Rate Control

Cited by 3 publications

References 24 publications

Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin

Interrogating cell type-specific cooperation of transcriptional regulators in 3D chromatin

Regression‐based Bayesian estimation and structure learning for nonparanormal graphical models

Statistics in the Genomic Era

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