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ABSTRACT: Background
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.Results
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.Conclusions
DiffCoEx is a simple and sensitive method to identify gene coexpression differences between multiple conditions.
SUBMITTER: Tesson BM
PROVIDER: S-EPMC2976757 | biostudies-literature | 2010 Oct
REPOSITORIES: biostudies-literature
Tesson Bruno M BM Breitling Rainer R Jansen Ritsert C RC
BMC bioinformatics 20101006
<h4>Background</h4>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. H ...[more]