The DOI matches its Crossref record; 46 of the 46 references it cites were found in a registry or an index.
Comparing Statistical Methods for Constructing Large Scale Gene Networks
Open accessAbstract
The gene regulatory network (GRN) reveals the regulatory relationships among genes and can provide a systematic understanding of molecular mechanisms underlying biological processes. The importance of computer simulations in understanding cellular processes is now widely accepted; a variety of algorithms have been developed to study these biological networks. The goal of this study is to provide a comprehensive evaluation and a practical guide to aid in choosing statistical methods for constructing large scale GRNs. Using both simulation studies and a real application in E. coli data, we compare different methods in terms of sensitivity and specificity in identifying the true connections and the hub genes, the ease of use, and computational speed. Our results show that these algorithms performed reasonably well, and each method has its own advantages: (1) GeneNet, WGCNA (Weighted Correlation Network Analysis), and ARACNE (Algorithm for the Reconstruction of Accurate Cellular Networks) performed well in constructing the global network structure; (2) GeneNet and SPACE (Sparse PArtial Correlation Estimation) performed well in identifying a few connections with high specificity.
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“At currently available sample sizes, networks are most reproducible at a module level35,38,39, then at the hub gene level41,223 and, last, at the level of precise gene connectivity rankings or precise module memberships of genes40,49.”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [39]
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“weighted networks constructed with the topological overlap of correlation (for example, by weighted gene co-expression network analysis (WGCNA)42,44) have similar sensitivity and specificity for detecting true network structure for experiments involving monotonic relationships as do networks constructed with nonlinear association measures such as mutual information (for example, by the Algorithm”
From Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015) · cites this paper as [39]
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In the citing paper
“A comparison between these tools and others, including WGCNA, showed that WGCNA and ARACNE perform best at defining the network structure of Escherichia coli [130], for which a well-defined regulatory network was used as a gold standard [131].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [130]
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In the citing paper
“Each of the methods detects specific module changes by design, but they can also detect modular changes that they were not specifically designed for and may outperform other tools in the identification of these changes [130].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [130]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Network analyses of AD brain tissue demonstrate the most reproducibility at the module level, followed by the hub protein level, and then finally at the level of precise protein connectivity rankings and module protein membership [61, 89–94].”
From Systems-based proteomics to resolve the biology of Alzheimer’s disease beyond amyloid and tau (2021) · cites this paper as [91]
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Cites
Cytoscape: A software Environment for integrated models of biomolecular interaction networks
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DAVID: Database for Annotation, Visualization, and Integrated Discovery
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A General Framework for Weighted Gene Co-Expression Network Analysis
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A generalized inverse for matrices
Penrose, R. (1955) · Mathematical Proceedings of the Cambridge Philosophical Society · doi:10.1017/s0305004100030401 · Cited by 4,611
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A Gene-Coexpression Network for Global Discovery of Conserved Genetic Modules
Stuart, J. M., Segal, E., Koller, D., & Kim, S. K. (2003) · Science · doi:10.1126/science.1087447 · Cited by 2,276
Margolin, A. A., Nemenman, I. M., Basso, K., Wiggins, C. H., Stolovitzky, G. A., Favera, R. D., & Califano, A. (2006) · BMC Bioinformatics · doi:10.7916/d8g15zbp · Cited by 2,260
Causal Protein-Signaling Networks Derived from Multiparameter Single-Cell Data
Sachs, K., Perez, O. D., Pe’er, D., Lauffenburger, D. A., & Nolan, G. P. (2005) · Science · doi:10.1126/science.1105809 · Cited by 1,785
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Gene co-expression analysis for functional classification and gene–disease predictions
van Dam, S., Võsa, U., van der Graaf, A., Franke, L., & de Magalhães, J. P. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbw139 · Cited by 995
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Metadata
- Authors
- Allen, J. D., Xie, Y., Chen, M., Girard, L., & Xiao, G.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Harold C. Simmons Comprehensive Cancer Center (US)
- Corresponding author
- Xiao, Guanghua
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2012-01-17
- Volume
- 7
- Issue
- 1
- Pages
- e29348
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3260142
- OpenAlex
- W2061971803
- Cited by
- 186
- References
- 46
- Citations by year
- 2025: 4 · 2024: 6 · 2023: 1 · 2022: 4 · 2021: 13 · 2020: 11 · 2019: 17 · 2018: 15 · 2017: 22 · 2016: 20 · 2015: 25 · 2014: 15 · 2013: 24 · 2012: 9
- FWCI
- 9.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Science Foundation (0907562)
- National Institutes of Health (RR024982)
- National Institute on Drug Abuse (R33DA027592, R21DA027592)
- National Cancer Institute ((P50-CA70907, R01CA152301, P50CA070907)
- National Center for Research Resources (UL1RR024982)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Microbial Metabolic Engineering and Bioproduction
- Keywords
- gene regulatory networks, network inference, weighted gene co-expression network analysis, ARACNE, hub genes, sensitivity and specificity, Escherichia coli, method comparison
- MeSH terms
- Algorithms, Escherichia coli, Reproducibility of Results, Computational Biology, Protein Interaction Mapping, Escherichia coli Proteins, Gene Regulatory Networks, Protein Interaction Maps
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