DiffCoEx: a simple and sensitive method to find differentially coexpressed gene modules
1 Groningen Bioinformatics Center, University of Groningen, Kerklaan 30, 9751 NN Haren, the Netherlands
2 Institute of Molecular, Cell and Systems Biology, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow G12 8QQ, UK
3 Department of Genetics, University Medical Center Groningen, 9700 RB, Groningen, the Netherlands
BMC Bioinformatics 2010, 11:497 doi:10.1186/1471-2105-11-497Published: 6 October 2010
Large microarray datasets have enabled gene regulation to be studied through coexpression analysis. While numerous methods have been developed for identifying differentially expressed genes between two conditions, the field of differential coexpression analysis is still relatively new. More specifically, there is so far no sensitive and untargeted method to identify gene modules (also known as gene sets or clusters) that are differentially coexpressed between two conditions. Here, sensitive and untargeted means that the method should be able to construct de novo modules by grouping genes based on shared, but subtle, differential correlation patterns.
We present DiffCoEx, a novel method for identifying correlation pattern changes, which builds on the commonly used Weighted Gene Coexpression Network Analysis (WGCNA) framework for coexpression analysis. We demonstrate its usefulness by identifying biologically relevant, differentially coexpressed modules in a rat cancer dataset.
DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.