Identification of coordinate gene expression changes across phenotypes or biological conditions is the basis of the ability
to decode the role of gene expression regulatory networks. Statistically, the identification of these changes can be viewed
as a search for groups (most typically pairs) of genes whose expression provides better phenotype discrimination when considered
jointly than when considered individually. Such groups are defined as being jointly differentially expressed. In this chapter
several approaches for identifying jointly differentially expressed groups of genes are reviewed of compared on a set of simulations.
Key Words High-order interactions – liquid correlation – microarray data – entropy – joint differential expression – correlation