  Gene co-expression network differential analysis is designed to help
biologists understand gene expression patterns under different condition. By
comparing different gene co-expression networks we may find conserved part as
well as condition specific set of genes. Taking the network as a collection as
modules, we use a sample-saving method to construct condition-specific gene
co-expression network, and identify differentially expressed subnetworks as
conserved or condition specific modules which may be associated with biological
processes. We have implemented the method as an R package which establishes a
pipeline from expression profile to biological explanations. The usefulness of
the method is also demonstrated by synthetic data as well as Daphnia magna gene
expression data under different environmental stresses.
