ArticleScientific reports2019
A Statistical Test for Differential Network Analysis Based on Inference of Gaussian Graphical Model.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 24 citations in OpenAlex.
- Mechanisms underlying the protective effects of San-Bai decoction against UV radiation on the skin.International journal of cosmetic science · 2026Article
- BDDN: bayesian dynamic differential network analysis in cancer proteomics.BMC bioinformatics · 2026Article
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- ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.Briefings in bioinformatics · 2024Article
- Exploring the Early Molecular Pathogenesis of Osteoarthritis Using Differential Network Analysis of Human Synovial Fluid.Molecular & cellular proteomics : MCP · 2024Article
- Sparse spectral graph analysis and its application to gastric cancer drug resistance-specific molecular interplays identification.PloS one · 2024Article
- Limitation of permutation-based differential correlation analysis.Genetic epidemiology · 2023Article
- Machine learning endometrial cancer risk prediction model: integrating guidelines of European Society for Medical Oncology with the tumor immune framework.International journal of gynecological cancer : official journal of the International Gynecological Cancer Society · 2023Article
- Article
- Path analysis: A method to estimate altered pathways in time-varying graphs of neuroimaging data.Network neuroscience (Cambridge, Mass.) · 2022Article
- Covariate-Adjusted Inference for Differential Analysis of High-Dimensional Networks.Sankhya. Series A. (2008) · 2022Article
- Identifying cancer pathway dysregulations using differential causal effects.Bioinformatics (Oxford, England) · 2022Article
- The Interplay between Public Health, Well-Being and Population Aging in Europe: An Advanced Structural Equation Modelling and Gaussian Network Approach.International journal of environmental research and public health · 2021Article
- Differential Network Analysis: A Statistical Perspective.Wiley interdisciplinary reviews. Computational statisticsArticle
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Authors and funding
6 authors at 1 institution in 1 country.
Funding
Abstract
Differential network analysis investigates how the network of connected genes changes from one condition to another and has become a prevalent tool to provide a deeper and more comprehensive understanding of the molecular etiology of complex diseases. Based on the asymptotically normal estimation of large Gaussian graphical model (GGM) in the high-dimensional setting, we developed a computationally efficient test for differential network analysis through testing the equality of two precision matrices, which summarize the conditional dependence network structures of the genes. Additionally, we applied a multiple testing procedure to infer the differential network structure with false discovery rate (FDR) control. Through extensive simulation studies with different combinations of parameters including sample size, number of vertices, level of heterogeneity and graph structure, we demonstrated that our method performed much better than the current available methods in terms of accuracy and computational time. In real data analysis on lung adenocarcinoma, we revealed a differential network with 3503 nodes and 2550 edges, which consisted of 50 clusters with an FDR threshold at 0.05. Many of the top gene pairs in the differential network have been reported relevant to human cancers. Our method represents a powerful tool of network analysis for high-dimensional biological data.
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