Ho Yen-Yi, Cope Leslie, Dettling Marcel, Parmigiani Giovanni
Johns Hopkins University, Baltimore, MD, USA.
Methods Mol Biol. 2007;408:171-91. doi: 10.1007/978-1-59745-547-3_10.
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.
识别跨表型或生物学条件的协同基因表达变化是解码基因表达调控网络作用能力的基础。从统计学角度来看，这些变化的识别可被视为寻找基因组（最典型的是基因对），这些基因的表达在联合考虑时比单独考虑时能提供更好的表型区分。这样的基因组被定义为联合差异表达。在本章中，我们回顾了几种识别联合差异表达基因组的方法，并在一组模拟中对它们进行了比较。
