title: Statistical methods for identifying differentially expressed gene combinations.
abstract: 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.
authors.affiliation: Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Yen-Yi
authors.initials: YY
authors.lastname: Ho
authors.forename: Leslie
authors.initials: L
authors.lastname: Cope
authors.forename: Marcel
authors.initials: M
authors.lastname: Dettling
authors.forename: Giovanni
authors.initials: G
authors.lastname: Parmigiani
mesh_terms: D019295:Computational Biology / Q000706:statistics & numerical data*; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000706:statistics & numerical data; D005821:Genetic Techniques / Q000706:statistics & numerical data*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data