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.
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.
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.