Eigengene networks for studying relationships between co-expression modules

Peter Langfelder and Steve Horvath


Dept. of Human Genetics, UC Los Angeles

Peter (dot) Langfelder (at) gmail (dot) com, SHorvath (at) mednet (dot) ucla (dot) edu


Reference

Langfelder P and Horvath S, Eigengene networks for studying relationships between co-expression modules. BMC Systems Biology 2007, 1:54 doi:10.1186/1752-0509-1-54 (link to BMC Systems Biology)

Abstract

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, which allows one to assess the preservation of network properties (e.g., modules) 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.

Conclusions: Eigengene networks can be an effective and biologically meaningful method for studying the relationship between modules of a gene co-expression network. The proposed methods may reveal a higher order organization of the transcriptome.

Software Availability: R software tutorials, the data, and supplementary material can be found at this webpage.

Presentation:

Powerpoint format PDF format

Additional information, R code for statistical analysis, data

  1. Application 1: Differential analysis of consensus eigengene networks in human and chimpanzee brain expression data.

  2. Application 2: Differential analysis of consensus eigengene networks across four tissues in female mice

  3. Application 3: Differential analysis of consensus eigengene networks in female and male mouse liver tissues

  4. Permutation study of consensus module significance

  5. Simulation study of consensus module detection
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Page by Peter Langfelder. Network image courtesy of the laboratory of Jake Lusis at UCLA.