Eigengene networks for studying the relationships between co-expression modules
Open accessAbstract
BACKGROUND: There is evidence that genes and their protein products are organized into functional modules according to cellular processes and pathways. Gene co-expression networks have been used to describe the relationships between gene transcripts. Ample literature exists on how to detect biologically meaningful modules in networks but there is a need for methods that allow one to study the relationships between modules. RESULTS: We show that network methods can also be used to describe the relationships between co-expression modules and present the following methodology. First, we describe several methods for detecting modules that are shared by two or more networks (referred to as consensus modules). We represent the gene expression profiles of each module by an eigengene. Second, we propose a method for constructing an eigengene network, where the edges are undirected but maintain information on the sign of the co-expression information. Third, we propose methods for differential eigengene network analysis that allow one to assess the preservation of network properties across different data sets. We illustrate the value of eigengene networks in studying the relationships between consensus modules in human and chimpanzee brains; the relationships between consensus modules in brain, muscle, liver, and adipose mouse tissues; and the relationships between male-female mouse consensus modules and clinical traits. In some applications, we find that module eigengenes can be organized into higher level clusters which we refer to as meta-modules. CONCLUSION: Eigengene networks can be effective and biologically meaningful tools for studying the relationships between modules of a gene co-expression network. The proposed methods may reveal a higher order organization of the transcriptome. R software tutorials, the data, and supplementary material can be found at the following webpage: http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/EigengeneNetwork.
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The resulting modules from clustering often represent biological processes.
“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 [63]
Subsequently, networks were modeled based on co-expression across a series of perturbations with genes as nodes and correlations as edges.
“Subsequently, networks were modeled based on co-expression across a series of perturbations with the genes encoding the transcripts corresponding to the nodes and the correlations to the edges [67–69].”
From Multi-omics approaches to disease (2017) · cites this paper as [68]
The modified WGCNA used bootstrapping 100 times and a consensus network to ensure that a subset of samples does not drive the network structure.
“Notably, we used a modified version of WGCNA that involves bootstrapping the underlying dataset 100 times and constructing 100 networks. The consensus of these networks (median edge strength across all bootstrapped networks) was then used as the final network39, ensuring that a subset of samples does not drive the network structure.”
From Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016) · cites this paper as [39]
A consensus analysis identifies modules that group together genes correlated in both data sets, with respect to both time point and genotype.
“The rationale is that a consensus analysis identifies modules that group together genes correlated in both data sets, i.e., both with respect to time point as well as genotype.”
From Elevated TREM2 Gene Dosage Reprograms Microglia Responsivity and Ameliorates Pathological Phenotypes in Alzheimer’s Disease Models (2018) · cites this paper as Langfelder and Horvath, 2007
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- Authors
- Langfelder, P., & Horvath, S.
- Affiliations
- University of California, Los Angeles (US)
- Corresponding author
- Horvath, Steve
- Venue
- BMC Systems Biology
- Published
- 2007-11-21
- Volume
- 1
- Issue
- 1
- Pages
- 54-54
- ISSN
- 1752-0509 (ISSN-L)
- PMC
- PMC2267703
- OpenAlex
- W1964305071
- Cited by
- 867
- References
- 33
- Citations by year
- 2026: 34 · 2025: 41 · 2024: 62 · 2023: 62 · 2022: 94 · 2021: 90 · 2020: 73 · 2019: 70 · 2018: 62 · 2017: 67 · 2016: 58 · 2015: 34 · 2014: 21 · 2013: 28 · 2012: 23
- FWCI
- 3.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Systems Biology (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Funders
- National Institute of Allergy and Infectious Diseases (U19AI063603)
- 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
- gene co-expression network, consensus modules, differential network analysis, sex differences in gene expression, clinical traits, mouse tissues
- MeSH terms
- Animals, Brain, Pan troglodytes, Computer Simulation, Female, Humans, Male, Models, Genetic, Species Specificity, Tissue Distribution, Gene Expression Profiling, Databases, Genetic, Systems Biology, Mice
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