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Wen X, Yang M, Hsu L, Zhang D. Test-retest reliability of modular-relevant analysis in brain functional network. Front Neurosci 2022; 16:1000863. [PMID: 36570835 PMCID: PMC9770801 DOI: 10.3389/fnins.2022.1000863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/22/2022] [Indexed: 12/13/2022] Open
Abstract
Introduction The human brain could be modeled as a complex network via functional magnetic resonance imaging (fMRI), and the architecture of these brain functional networks can be studied from multiple spatial scales with different graph theory tools. Detecting modules is an important mesoscale network measuring approach that has provided crucial insights for uncovering how brain organizes itself among different functional subsystems. Despite its successful application in a wide range of brain network studies, the lack of comprehensive reliability assessment prevents its potential extension to clinical trials. Methods To fill this gap, this paper, using resting-state test-retest fMRI data, systematically explored the reliabilities of five popular network metrics derived from modular structure. Considering the repeatability of network partition depends heavily on network size and module detection algorithm, we constructed three types of brain functional networks for each subject by using a set of coarse-to-fine brain atlases and adopted four methods for single-subject module detection and twelve methods for group-level module detection. Results The results reported moderate-to-good reliability in modularity, intra- and inter-modular functional connectivities, within-modular degree and participation coefficient at both individual and group levels, indicating modular-relevant network metrics can provide robust evaluation results. Further analysis identified the significant influence of module detection algorithm and node definition approach on reliabilities of network partitions and its derived network analysis results. Discussion This paper provides important guidance for choosing reliable modular-relevant network metrics and analysis strategies in future studies.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
| | - Liming Hsu
- Center for Animal MRI, University of North Carolina, Chapel Hill, Chapel Hill, NC, United States
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
- MIIT Key Laboratory of Pattern Analysis and Machine Intelligence, Nanjing, Jiangsu, China
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Community detection in attributed networks considering both structural and attribute similarities: two mathematical programming approaches. Neural Comput Appl 2019. [DOI: 10.1007/s00521-019-04064-5] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
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Guerra OJ, Calderón AJ, Papageorgiou LG, Reklaitis GV. Integrated shale gas supply chain design and water management under uncertainty. AIChE J 2018. [DOI: 10.1002/aic.16476] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- Omar J. Guerra
- School of Chemical EngineeringPurdue University West Lafayette IN, 47907
| | - Andrés J. Calderón
- Dept. of Chemical EngineeringUniversity College London WC1E 7JE, London U.K
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Karimi-Majd AM, Fathian M, Gholamian MR. Behavior-based indices for evaluating communities in online social networks. INTELL DATA ANAL 2017. [DOI: 10.3233/ida-150349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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5
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Community Structure Detection for Directed Networks through Modularity Optimisation. ALGORITHMS 2016. [DOI: 10.3390/a9040073] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Chen YY, Yu YN, Zhang YY, Li B, Liu J, Li DF, Wu P, Wang J, Wang Z, Wang YY. Quantitative Determination of Flexible Pharmacological Mechanisms Based On Topological Variation in Mice Anti-Ischemic Modular Networks. PLoS One 2016; 11:e0158379. [PMID: 27383195 PMCID: PMC4934924 DOI: 10.1371/journal.pone.0158379] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 05/12/2016] [Indexed: 12/29/2022] Open
Abstract
Targeting modules or signalings may open a new path to understanding the complex pharmacological mechanisms of reversing disease processes. However, determining how to quantify the structural alteration of these signalings or modules in pharmacological networks poses a great challenge towards realizing rational drug use in clinical medicine. Here, we explore a novel approach for dynamic comparative and quantitative analysis of the topological structural variation of modules in molecular networks, proposing the concept of allosteric modules (AMs). Based on the ischemic brain of mice, we optimize module distribution in different compound-dependent modular networks by using the minimum entropy criterion and then calculate the variation in similarity values of AMs under various conditions using a novel method of SimiNEF. The diverse pharmacological dynamic stereo-scrolls of AMs with functional gradient alteration, which consist of five types of AMs, may robustly deconstruct modular networks under the same ischemic conditions. The concept of AMs can not only integrate the responsive mechanisms of different compounds based on topological cascading variation but also obtain valuable structural information about disease and pharmacological networks beyond pathway analysis. We thereby provide a new systemic quantitative strategy for rationally determining pharmacological mechanisms of altered modular networks based on topological variation.
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Affiliation(s)
- Yin-ying Chen
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ya-nan Yu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Ying-ying Zhang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Bing Li
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jun Liu
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
| | - Dong-feng Li
- School of Mathematical Sciences, Peking University, Beijing, China
| | - Ping Wu
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
| | - Jie Wang
- Guang'anmen Hospital, China Academy of Chinese Medical Sciences, Beijing, China
- * E-mail: (JW); (ZW); (YYW)
| | - Zhong Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- * E-mail: (JW); (ZW); (YYW)
| | - Yong-yan Wang
- Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing, China
- * E-mail: (JW); (ZW); (YYW)
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Mooney MA, Wilmot B. Gene set analysis: A step-by-step guide. Am J Med Genet B Neuropsychiatr Genet 2015; 168:517-27. [PMID: 26059482 PMCID: PMC4638147 DOI: 10.1002/ajmg.b.32328] [Citation(s) in RCA: 53] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Accepted: 05/20/2015] [Indexed: 12/21/2022]
Abstract
To maximize the potential of genome-wide association studies, many researchers are performing secondary analyses to identify sets of genes jointly associated with the trait of interest. Although methods for gene-set analyses (GSA), also called pathway analyses, have been around for more than a decade, the field is still evolving. There are numerous algorithms available for testing the cumulative effect of multiple SNPs, yet no real consensus in the field about the best way to perform a GSA. This paper provides an overview of the factors that can affect the results of a GSA, the lessons learned from past studies, and suggestions for how to make analysis choices that are most appropriate for different types of data. © 2015 Wiley Periodicals, Inc.
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Affiliation(s)
- Michael A. Mooney
- Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon
| | - Beth Wilmot
- Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, Oregon,OHSU Knight Cancer Institute, Portland, Oregon,Oregon Clinical and Translational Research Institute, Portland, Oregon,Correspondence to: Beth Wilmot, Department of Medical Informatics & Clinical Epidemiology, Division of Bioinformatics & Computational Biology, Oregon Health & Science University, Portland, OR 97239.
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Bennett L, Kittas A, Muirhead G, Papageorgiou LG, Tsoka S. Detection of composite communities in multiplex biological networks. Sci Rep 2015; 5:10345. [PMID: 26012716 PMCID: PMC4446847 DOI: 10.1038/srep10345] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2014] [Accepted: 03/26/2015] [Indexed: 12/23/2022] Open
Abstract
The detection of community structure is a widely accepted means of investigating the
principles governing biological systems. Recent efforts are exploring ways in which
multiple data sources can be integrated to generate a more comprehensive model of
cellular interactions, leading to the detection of more biologically relevant
communities. In this work, we propose a mathematical programming model to cluster
multiplex biological networks, i.e. multiple network slices, each with a different
interaction type, to determine a single representative partition of composite
communities. Our method, known as SimMod, is evaluated through its application to
yeast networks of physical, genetic and co-expression interactions. A comparative
analysis involving partitions of the individual networks, partitions of aggregated
networks and partitions generated by similar methods from the literature highlights
the ability of SimMod to identify functionally enriched modules. It is further shown
that SimMod offers enhanced results when compared to existing approaches without the
need to train on known cellular interactions.
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Affiliation(s)
- Laura Bennett
- Centre for Process Systems Engineering, Department of Chemical Engineering,University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Aristotelis Kittas
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom
| | - Gareth Muirhead
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom
| | - Lazaros G Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering,University College London, Torrington Place, London WC1E 7JE, United Kingdom
| | - Sophia Tsoka
- Department of Informatics, Faculty of Natural and Mathematical Sciences, King's College London, Strand, London WC2R 2LS, UnitedKingdom
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Bennett L, Kittas A, Liu S, Papageorgiou LG, Tsoka S. Community structure detection for overlapping modules through mathematical programming in protein interaction networks. PLoS One 2014; 9:e112821. [PMID: 25412367 PMCID: PMC4239042 DOI: 10.1371/journal.pone.0112821] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2014] [Accepted: 10/15/2014] [Indexed: 12/05/2022] Open
Abstract
Community structure detection has proven to be important in revealing the underlying properties of complex networks. The standard problem, where a partition of disjoint communities is sought, has been continually adapted to offer more realistic models of interactions in these systems. Here, a two-step procedure is outlined for exploring the concept of overlapping communities. First, a hard partition is detected by employing existing methodologies. We then propose a novel mixed integer non linear programming (MINLP) model, known as OverMod, which transforms disjoint communities to overlapping. The procedure is evaluated through its application to protein-protein interaction (PPI) networks of the rat, E. coli, yeast and human organisms. Connector nodes of hard partitions exhibit topological and functional properties indicative of their suitability as candidates for multiple module membership. OverMod identifies two types of connector nodes, inter and intra-connector, each with their own particular characteristics pertaining to their topological and functional role in the organisation of the network. Inter-connector proteins are shown to be highly conserved proteins participating in pathways that control essential cellular processes, such as proliferation, differentiation and apoptosis and their differences with intra-connectors is highlighted. Many of these proteins are shown to possess multiple roles of distinct nature through their participation in different network modules, setting them apart from proteins that are simply ‘hubs’, i.e. proteins with many interaction partners but with a more specific biochemical role.
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Affiliation(s)
- Laura Bennett
- Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, WC1E 7JE, London, United Kingdom
| | - Aristotelis Kittas
- Department of Informatics, King's College London, Strand, WC2R 2LS, London, United Kingdom
| | - Songsong Liu
- Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, WC1E 7JE, London, United Kingdom
| | - Lazaros G. Papageorgiou
- Centre for Process Systems Engineering, Department of Chemical Engineering, UCL (University College London), Torrington Place, WC1E 7JE, London, United Kingdom
| | - Sophia Tsoka
- Department of Informatics, King's College London, Strand, WC2R 2LS, London, United Kingdom
- * E-mail:
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Mooney MA, Nigg JT, McWeeney SK, Wilmot B. Functional and genomic context in pathway analysis of GWAS data. Trends Genet 2014; 30:390-400. [PMID: 25154796 DOI: 10.1016/j.tig.2014.07.004] [Citation(s) in RCA: 86] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2014] [Revised: 07/18/2014] [Accepted: 07/18/2014] [Indexed: 02/07/2023]
Abstract
Gene set analysis (GSA) is a promising tool for uncovering the polygenic effects associated with complex diseases. However, the available techniques reflect a wide variety of hypotheses about how genetic effects interact to contribute to disease susceptibility. The lack of consensus about the best way to perform GSA has led to confusion in the field and has made it difficult to compare results across methods. A clear understanding of the various choices made during GSA - such as how gene sets are defined, how single-nucleotide polymorphisms (SNPs) are assigned to genes, and how individual SNP-level effects are aggregated to produce gene- or pathway-level effects - will improve the interpretability and comparability of results across methods and studies. In this review we provide an overview of the various data sources used to construct gene sets and the statistical methods used to test for gene set association, as well as provide guidelines for ensuring the comparability of results.
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Affiliation(s)
- Michael A Mooney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA
| | - Joel T Nigg
- Division of Psychology, Department of Psychiatry, Oregon Health & Science University, Portland, OR, USA; Department of Behavioral Neuroscience, Oregon Health & Science University, Portland, OR, USA
| | - Shannon K McWeeney
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA.
| | - Beth Wilmot
- Division of Bioinformatics and Computational Biology, Department of Medical Informatics and Clinical Epidemiology, Oregon Health & Science University, Portland, OR, USA; Oregon Clinical and Translational Research Institute, Portland, OR, USA; OHSU Knight Cancer Institute, Portland, OR, USA
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Liu X, Pan L. Detection of driver metabolites in the human liver metabolic network using structural controllability analysis. BMC SYSTEMS BIOLOGY 2014; 8:51. [PMID: 24885538 PMCID: PMC4024020 DOI: 10.1186/1752-0509-8-51] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/08/2014] [Accepted: 04/28/2014] [Indexed: 12/20/2022]
Abstract
Background Abnormal states in human liver metabolism are major causes of human liver diseases ranging from hepatitis to hepatic tumor. The accumulation in relevant data makes it feasible to derive a large-scale human liver metabolic network (HLMN) and to discover important biological principles or drug-targets based on network analysis. Some studies have shown that interesting biological phenomenon and drug-targets could be discovered by applying structural controllability analysis (which is a newly prevailed concept in networks) to biological networks. The exploration on the connections between structural controllability theory and the HLMN could be used to uncover valuable information on the human liver metabolism from a fresh perspective. Results We applied structural controllability analysis to the HLMN and detected driver metabolites. The driver metabolites tend to have strong ability to influence the states of other metabolites and weak susceptibility to be influenced by the states of others. In addition, the metabolites were classified into three classes: critical, high-frequency and low-frequency driver metabolites. Among the identified 36 critical driver metabolites, 27 metabolites were found to be essential; the high-frequency driver metabolites tend to participate in different metabolic pathways, which are important in regulating the whole metabolic systems. Moreover, we explored some other possible connections between the structural controllability theory and the HLMN, and find that transport reactions and the environment play important roles in the human liver metabolism. Conclusion There are interesting connections between the structural controllability theory and the human liver metabolism: driver metabolites have essential biological functions; the crucial role of extracellular metabolites and transport reactions in controlling the HLMN highlights the importance of the environment in the health of human liver metabolism.
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Affiliation(s)
| | - Linqiang Pan
- Key Laboratory of Image Information Processing and Intelligent Control, School of Automation, Huazhong University of Science and Technology, Luoyu Road 1037, 430074 Wuhan, China.
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Ahmed HA, Mahanta P, Bhattacharyya DK. Negative Correlation Aided Network Module Extraction. ACTA ACUST UNITED AC 2012. [DOI: 10.1016/j.protcy.2012.10.079] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Ficklin SP, Feltus FA. Gene coexpression network alignment and conservation of gene modules between two grass species: maize and rice. PLANT PHYSIOLOGY 2011; 156:1244-56. [PMID: 21606319 PMCID: PMC3135956 DOI: 10.1104/pp.111.173047] [Citation(s) in RCA: 68] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2011] [Accepted: 05/20/2011] [Indexed: 05/17/2023]
Abstract
One major objective for plant biology is the discovery of molecular subsystems underlying complex traits. The use of genetic and genomic resources combined in a systems genetics approach offers a means for approaching this goal. This study describes a maize (Zea mays) gene coexpression network built from publicly available expression arrays. The maize network consisted of 2,071 loci that were divided into 34 distinct modules that contained 1,928 enriched functional annotation terms and 35 cofunctional gene clusters. Of note, 391 maize genes of unknown function were found to be coexpressed within modules along with genes of known function. A global network alignment was made between this maize network and a previously described rice (Oryza sativa) coexpression network. The IsoRankN tool was used, which incorporates both gene homology and network topology for the alignment. A total of 1,173 aligned loci were detected between the two grass networks, which condensed into 154 conserved subgraphs that preserved 4,758 coexpression edges in rice and 6,105 coexpression edges in maize. This study provides an early view into maize coexpression space and provides an initial network-based framework for the translation of functional genomic and genetic information between these two vital agricultural species.
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