ArticlePloS one2013
When is hub gene selection better than standard meta-analysis?
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 192 papers, 4 of them syntheses that pooled it.
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Who cites it
192 citing papers in PubMed, 4 syntheses or guidelines pooled it, 341 citations in OpenAlex.
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- Induction of a common microglia gene expression signature by aging and neurodegenerative conditions: a co-expression meta-analysis.Acta neuropathologica communications · 2015Pooled it
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- Genomic convergence in hibernating mammals elucidates the genetics of metabolic regulation in the hypothalamus.Science (New York, N.Y.) · 2025Article
- Human gut microbiome gene co-expression network reveals a loss in taxonomic and functional diversity in Parkinson's disease.NPJ biofilms and microbiomes · 2025Article
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- Large-scale network analysis of the cerebrospinal fluid proteome identifies molecular signatures of frontotemporal lobar degeneration.Nature aging · 2025Article
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132 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.