Data and Statistical R Code:
Differential Network Analysis

Correspondence: Tova Fuller, Steve Horvath

Here we provide statistical code and data for the paper:

Fuller TF, Ghazalpour A, Aten JE, Drake TA, Lusis AJ, Horvath S (2007) “Weighted Gene Co-expression Network Analysis Strategies Applied to Mouse Weight”, Mamm Genome 18(6):463-472.

Link to paper (PDF).
Link to paper (full text).

The following tutorials provide the statistical code used for applying differential weighted gene coexpression network analysis to mouse liver tissue samples, and for validating results.

Abstract

Here we illustrate differential network analysis by comparing the connectivity and module structure of two networks based on the liver expression data of lean and heavy mice. This unbiased method for comparing two phenotypically distinct subgroups of mouse samples serves as a method for understanding the underlying differential gene co-expression network topology giving rise to altered biological pathways.

We also utilize a weighted gene co-expression network analysis (WGCNA) approach based on expression and genotype data from a previously studied BxH F2 mouse intercross as well as a new BxD cross. Specifically, we utilize weighted gene co-expression network analysis (WGCNA) methods to demonstrate preservation of modules, intramodular connectivity and gene significance. We also obtain linear models in both data crosses using a module QTL identified in the BxH data that resides on the 19th chromosome.

Article Supplemental Information

Appendices

• Appendix A: Building weighted gene co-expression networks
• Appendix B: GS.SNP and the LOD score
• Appendix C: Sector plot functional enrichment results discussion

Supplementary Tables

• Supplementary Table 1 (pdf): Data and calculated measures in simulated example
• Supplementary Table 2 (pdf): Depiction of different marker coding schema
• Supplementary Table 3 (xls): Functional enrichment results for the blue module genes in both BxH and BxD data
• Supplementary Table 4 (xls): Functional enrichment results for sector 2 genes
• Supplementary Table 5 (xls): Functional enrichment results for sector 3 genes
• Supplementary Table 6 (xls): Functional enrichment results for sector 5 genes
• Supplementary Table 7 (xls): Functional enrichment results for sector 7 genes
• Supplementary Table 8 (xls): Functional enrichment results for sector 8 genes

Supplementary Figures

• Supplementary Figure 1: Scatterplots of kIN versus kME in 4 largest BxH modules
• Supplementary Figure 2: Expression heat map and visual depiction of module eigengene in simulated example
• Supplementary Figure 3: Scatterplots of GS.SNP versus LOD score with respect to p45529 for different marker coding schema

R Software Tutorials and Data

1. Differential Network Analysis
   1. R Tutorial
   2. Data Files (zipped)
   3. Network Functions
2. BxH WGCNA Validation Analysis
   1. R Tutorial
   2. Data Files (zipped) – may take a while to download
   3. Network Functions
3. Gene Screening Strategies
   1. R Tutorial
   2. Data Files (zipped)
   3. Network Functions

Presentations and Powerpoints

• PowerPoint slide presentation (contains animations, fonts optimized for Mac users)
• PDF slide presentation (does not contain animations, fonts should work for all users)

Other material regarding weighted gene co-expression network analysis

• Weighted gene co-expression network page
• Generalized Topological Overlap Measure (GTOM) page
• The weighted gene co-expression network analysis method is described in Theory Paper 1: Zhang and Horvath (2005)
• For a more mathematical description of weighted gene co-expression networks consider Theory Paper 2: Dong and Horvath (2007)
• For a detailed discussion of the topological overlap measure, please consider Theory Paper 3: Yip and Horvath (2007)

---