Eigengene networks for studying the relationships between co-expression modules

By Peter Langfelder, Steve Horvath. BMC Systems Biology, 2007

838 citations recorded by the source corpus.

BackgroundThere 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.ResultsWe 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.ConclusionEigengene 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.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/d9e370465967352eca00debdf5527e12f4b587d7

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Citations in the corpus

Utility of RNA Sequencing for Analysis of Maize Reproductive Transcriptomes (2011, methodology): “3 patterns of the genes in each module, eigengenes for each module were calculated and displayed in a heat map (Langfelder and Horvath, 2007) (Fig.”
The responses and adaptations of microbial communities to salinity in farmland soils: a molecular ecological network analysis. (2017, methodology): “Furthermore, eigengene analysis (Langfelder and Horvath, 2007; Horvath and Dong, 2008) was performed to reveal the higher-order organization of the constructed pMENs.”
WGCNA: an R package for weighted correlation network analysis (2008, methodology): “Consensus module detection When dealing with multiple adjacency matrices representing different networks, it can be interesting to find consensus modules, defined as modules that are present in all or most networks [12].”
Aging effects on DNA methylation modules in human brain and blood tissue (2012, methodology): “Studies of our group [42,45-48] and of independent groups [49] provide empirical evidence that the topological overlap measure is a biologically meaningful network similarity measure.”
Weighted Gene Coexpression Network Analysis: State of the Art (2010, neutral): “Many biological networks share topological properties (Horvath and Dong, 2008; Horvath et al., 2006; Langfelder and Horvath, 2007; Oldham et al., 2006).”
Systems biology and gene networks in neurodevelopmental and neurodegenerative disorders (2015, background): “This module-level association approach reduces the problem of multiple comparisons and highlights the advantages of using networks as an organizing framework 96 .”
Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2 (2011, methodology): “The relationships among eigengenes are usually visualized by average-linkage hierarchical analysis as a clustering dendrogram (25).”
Gene co-expression analysis for functional classification and gene–disease predictions (2017, background): “The resulting modules often represent biological processes [63, 64] and can be phenotype specific [65].”
Is My Network Module Preserved and Reproducible? (2011, background): “In Supplementary Text S5 and Supplementary Table S4, we report three empirical studies of consensus modules [44] which are constructed in such a way that genes within consensus modules are highly co-expressed in all given input microarray data sets.”
Geometric Interpretation of Gene Coexpression Network Analysis (2008, background): “A high level view of modules and their centroids (eigengenes) can be used to define eigengene networks [52].”
Genome-wide changes in lncRNA, splicing, and regional gene expression patterns in autism (2016, positive): “The consensus of these networks (median edge strength across all bootstrapped networks) was then used as the final network 39 , ensuring that a subset of samples does not drive the network structure.”