We present the following new results: (1) A definition of eigengene networks in gene co-expression data that can be used to explore relationships of modules in co-expressed genes. Our networks are undirected, but the links retain the sign of the co-expression relationship between the respective nodes. (2) Simple and efficient approaches to detect consensus modules, i.e., modules that are shared between two or more different networks. (3) Visual as well as quantitative methods for studying module relationships within a network as well as the differences between eigengene networks.
As examples, we apply our methods to several recently published
empirical microarray datasets. We detect and analyze consensus modules
in human and chimpanzee brains and relate them to the brain regions in which they
are most differentially expressed.
In the second application, we analyze consensus modules
across data from four different female mouse tissues.
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