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http://dx.doi.org/10.3961/jpmph.21.190

Network Analysis in Systems Epidemiology  

Park, JooYong (Department of Biomedical Sciences, Seoul National University Graduate School)
Choi, Jaesung (Institute of Health Policy and Management, Seoul National University Medical Research Center)
Choi, Ji-Yeob (Department of Biomedical Sciences, Seoul National University Graduate School)
Publication Information
Journal of Preventive Medicine and Public Health / v.54, no.4, 2021 , pp. 259-264 More about this Journal
Abstract
Traditional epidemiological studies have identified a number of risk factors for various diseases using regression-based methods that examine the association between an exposure and an outcome (i.e., one-to-one correspondences). One of the major limitations of this approach is the "black-box" aspect of the analysis, in the sense that this approach cannot fully explain complex relationships such as biological pathways. With high-throughput data in current epidemiology, comprehensive analyses are needed. The network approach can help to integrate multi-omics data, visualize their interactions or relationships, and make inferences in the context of biological mechanisms. This review aims to introduce network analysis for systems epidemiology, its procedures, and how to interpret its findings.
Keywords
Systems epidemiology; Integrative approach; Network analysis; Multi-omics;
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1 Laszlo A, Krippner S. Chapter 3: systems theories: their origins, foundations, and development. In: Jordan JS, editor. Systems theories and a priori aspects of perception. Amsterdam: Elsevier; 1998, p. 47-74.
2 Gentry J, Gentleman R, Huber W. How to plot a graph using Rgraphviz; 2021 [cited 2021 May 1]. Available from: http://www.bioconductor.org/packages/release/bioc/vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf.
3 Batushansky A, Toubiana D, Fait A. Correlation-based network generation, visualization, and analysis as a powerful tool in biological studies: a case study in cancer cell metabolism. Biomed Res Int 2016;2016:8313272.
4 Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods 2018;23(4):617-634.   DOI
5 Perez De Souza L, Alseekh S, Brotman Y, Fernie AR. Network-based strategies in metabolomics data analysis and interpretation: from molecular networking to biological interpretation. Expert Rev Proteomics 2020;17(4):243-255.   DOI
6 Kohl M, Wiese S, Warscheid B. Cytoscape: software for visualization and analysis of biological networks. Methods Mol Biol 2011;696:291-303.   DOI
7 Kolaczyk ED, Csardi G. Statistical analysis of network data with R. New York: Springer; 2014, p. 29-41.
8 Langfelder P, Horvath S. Fast R functions for robust correlations and hierarchical clustering. J Stat Softw 2012;46(11):i11.
9 Kim S. ppcor: an R package for a fast calculation to semi-partial correlation coefficients. Commun Stat Appl Methods 2015;22(6):665-674.   DOI
10 Pedersen TL. Package 'ggraph'; 2021 [cited 2021 Jul 1]. Available from: https://mirror.uned.ac.cr/cran/web/packages/ggraph/ggraph.pdf.
11 Csardi G. Package 'igraph'; 2015 [cited 2021 Jul 1]. Available from: https://cran.microsoft.com/snapshot/2017-05-27/web/packages/igraph/igraph.pdf.
12 Riaz MR, Preston GM, Mithani A. MAPPS: a web-based tool for metabolic pathway prediction and network analysis in the postgenomic era. ACS Synth Biol 2020;9(5):1069-1082.   DOI
13 Li Z, Zhang Y, Hu T, Likhodii S, Sun G, Zhai G, et al. Differential metabolomics analysis allows characterization of diversity of metabolite networks between males and females. PLoS One 2018;13(11):e0207775.   DOI
14 Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: a tutorial paper. Behav Res Methods 2018;50(1):195-212.   DOI
15 Weed DL. Beyond black box epidemiology. Am J Public Health 1998;88(1):12-14.   DOI
16 Dammann O, Gray P, Gressens P, Wolkenhauer O, Leviton A. Systems epidemiology: what's in a name? Online J Public Health Inform 2014;6(3):e198.   DOI
17 Epskamp S, Cramer AO, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: network visualizations of relationships in psychometric data. J Stat Softw 2012;48(4):1-18.
18 Zhou D, Zhu W, Sun T, Wang Y, Chi Y, Chen T, et al. iMAP: a web server for metabolomics data integrative analysis. Front Chem 2021;9:659656.   DOI
19 Wang Y, Wang G, Jing RN, Hu T, Likhodii S, Sun G, et al. Metabolomics analysis of human plasma metabolites reveals the age-and sex-specific associations. Liq Chromatogr Relat Technol 2020;43(5-6):185-194.   DOI
20 Costello CA, Hu T, Liu M, Zhang W, Furey A, Fan Z, et al. Differential correlation network analysis identified novel metabolomics signatures for non-responders to total joint replacement in primary osteoarthritis patients. Metabolomics 2020;16(5):61.   DOI
21 Huang T, Glass K, Zeleznik OA, Kang JH, Ivey KL, Sonawane AR, et al. A network analysis of biomarkers for type 2 diabetes. Diabetes 2019;68(2):281-290.   DOI
22 Floegel A, Wientzek A, Bachlechner U, Jacobs S, Drogan D, Prehn C, et al. Linking diet, physical activity, cardiorespiratory fitness and obesity to serum metabolite networks: findings from a population-based study. Int J Obes (Lond) 2014;38(11):1388-1396.   DOI
23 Fukushima A. DiffCorr: an R package to analyze and visualize differential correlations in biological networks. Gene 2013;518(1):209-214.   DOI
24 Lund E, Dumeaux V. Systems epidemiology in cancer. Cancer Epidemiol Biomarkers Prev 2008;17(11):2954-2957.   DOI
25 Haring R, Wallaschofski H. Diving through the "-omics": the case for deep phenotyping and systems epidemiology. OMICS 2012;16(5):231-234.   DOI
26 Susser M, Susser E. Choosing a future for epidemiology: II. From black box to Chinese boxes and eco-epidemiology. Am J Public Health 1996;86(5):674-677.   DOI
27 Hu FB. Metabolic profiling of diabetes: from black-box epidemiology to systems epidemiology. Clin Chem 2011;57(9):1224-1226.   DOI
28 Yan J, Risacher SL, Shen L, Saykin AJ. Network approaches to systems biology analysis of complex disease: integrative methods for multi-omics data. Brief Bioinform 2018;19(6):1370-1381.
29 Barabasi AL, Gulbahce N, Loscalzo J. Network medicine: a network-based approach to human disease. Nat Rev Genet 2011;12(1):56-68.   DOI
30 Hevey D. Network analysis: a brief overview and tutorial. Health Psychol Behav Med 2018;6(1):301-328.   DOI
31 Cornelis MC, Hu FB. Systems epidemiology: a new direction in nutrition and metabolic disease research. Curr Nutr Rep 2013;2(4):10.1007/s13668-013-0052-4.   DOI