When is hub gene selection better than standard meta-analysis?
Open accessAbstract
Since hub nodes have been found to play important roles in many networks, highly connected hub genes are expected to play an important role in biology as well. However, the empirical evidence remains ambiguous. An open question is whether (or when) hub gene selection leads to more meaningful gene lists than a standard statistical analysis based on significance testing when analyzing genomic data sets (e.g., gene expression or DNA methylation data). Here we address this question for the special case when multiple genomic data sets are available. This is of great practical importance since for many research questions multiple data sets are publicly available. In this case, the data analyst can decide between a standard statistical approach (e.g., based on meta-analysis) and a co-expression network analysis approach that selects intramodular hubs in consensus modules. We assess the performance of these two types of approaches according to two criteria. The first criterion evaluates the biological insights gained and is relevant in basic research. The second criterion evaluates the validation success (reproducibility) in independent data sets and often applies in clinical diagnostic or prognostic applications. We compare meta-analysis with consensus network analysis based on weighted correlation network analysis (WGCNA) in three comprehensive and unbiased empirical studies: (1) Finding genes predictive of lung cancer survival, (2) finding methylation markers related to age, and (3) finding mouse genes related to total cholesterol. The results demonstrate that intramodular hub gene status with respect to consensus modules is more useful than a meta-analysis p-value when identifying biologically meaningful gene lists (reflecting criterion 1). However, standard meta-analysis methods perform as good as (if not better than) a consensus network approach in terms of validation success (criterion 2). The article also reports a comparison of meta-analysis techniques applied to gene expression data and presents novel R functions for carrying out consensus network analysis, network based screening, and meta analysis.
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In biological networks, mathematical derivations show that only intra-modular hub genes are more relevant than other nodes, as opposed to inter-modular hub genes.
“Hubs are frequently more relevant to the functionality of networks than other nodes [69]. This is also the case in biological networks [32], although mathematical derivations show that this is only the case for intra-modular hub genes (as opposed to inter-modular hub genes [64, 65]).”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [65]
The Introduction (page unavailable) says that coexpression-network applications should focus on intramodular rather than whole-network hubs. The Discussion (page unavailable) says whole-network hubs are often uninteresting, not that mathematical derivations establish intramodular hubs as the only relevant nodes. The cited passages do not make the claimed comparison with inter-modular hubs. (LLM, conf=0.86)
Experimental — accuracy not yet measuredIntramodular hub genes, the highly connected hub nodes in a gene network, have been shown to regulate other genes in the network and are termed key network drivers (KNDs).
“Intramodular hub genes, the highly connected hub nodes in a gene network, have been shown to regulate other genes in the network and termed as key network drivers (KNDs)”
From Sex specific molecular networks and key drivers of Alzheimer’s disease (2023) · cites this paper as [42]
The “Network Hubs: Nodes with High Connectivity” section defines intramodular hubs as nodes with high connectivity within a module. It does not call them key network drivers or establish that they regulate other genes. The “Limitations” section says the results have no direct relationship to regulatory-network analysis and notes that transcription regulators may be better targets when regulatory relationships are of interest. (LLM, conf=0.89)
Experimental — accuracy not yet measuredThe resulting modules from clustering can be phenotype specific.
“The resulting modules often represent biological processes [63, 64] and can be phenotype specific [65].”
From Gene co-expression analysis for functional classification and gene–disease predictions (2017) · cites this paper as [65]
At currently available sample sizes, networks are most reproducible at module level, then hub gene level, and least at precise gene connectivity rankings or module memberships.
“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 [223]
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- Authors
- Langfelder, P., Mischel, P. S., & Horvath, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Horvath, Steve
- Venue
- PLoS ONE
- Publisher
- Public Library of Science (PLoS)
- Published
- 2013-04-17
- Volume
- 8
- Issue
- 4
- Pages
- e61505
- ISSN
- 1932-6203 (ISSN-L)
- PMC
- PMC3629234
- OpenAlex
- W2016474243
- Cited by
- 313
- References
- 103
- Citations by year
- 2026: 10 · 2025: 17 · 2024: 40 · 2023: 18 · 2022: 24 · 2021: 36 · 2020: 32 · 2019: 28 · 2018: 20 · 2017: 25 · 2016: 23 · 2015: 20 · 2014: 13 · 2013: 6
- FWCI
- 9.25
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS ONE (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, other-oa)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health (UL1TR000124, AI28697)
- Center for AIDS Research, University of Washington (AI28697)
- National Institute on Drug Abuse (R01DA030913)
- National Cancer Institute (P50CA092131)
- National Institute of Allergy and Infectious Diseases (AI-28 697, P30AI028697)
- National Center for Research Resources
- National Center for Advancing Translational Sciences
- Clinical and Translational Science Institute, University of California, Los Angeles (UL1TR000124)
- UCLA AIDS Institute (AI28697)
- Center for AIDS Research, University of California Los Angeles (AI28697)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- meta-analysis, weighted gene co-expression network analysis, gene co-expression network, consensus modules, gene expression data, lung cancer survival, reproducibility
- MeSH terms
- Animals, Computer Simulation, Female, Humans, Male, Mice, Inbred C57BL, Neoplasms, Meta-Analysis as Topic, Reproducibility of Results, Mice, Gene Regulatory Networks, Molecular Sequence Annotation
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