Identification of responsive gene modules by network-based gene clustering and extending: application to inflammation and angiogenesis

ClustEx, a two-step method based on the new formulation, was developed and applied to identify the responsive gene modules of human umbilical vein endothelial cells (HUVECs) in inflammation and angiogenesis models by integrating the time-course microarray data and genome-wide PPI data. It shows better performance than several available module identification tools by testing on the reference responsive gene sets. Gene set analysis of KEGG pathways, GO terms and microRNAs (miRNAs) target gene sets further supports the ClustEx predictions.Taking the closely-connected and co-expressed DE genes in the condition-specific gene network as the signatures of the underlying responsive gene modules provides a new strategy to solve the module identification problem. The identified responsive gene modules of HUVECs and the corresponding enriched pathways/miRNAs provide useful resources for understanding the inflammatory and angiogenic responses of vascular systems.Understanding of cell responses to environmental stimuli is one of the central tasks of molecular biology. Genome-wide gene expression profiling techniques, such as microarray and deep sequencing, are widely used to identify the responsive genes whose expressions are significantly changed after the stimulus. But identifying the responsive genes by differential expressions does not consider the complex gene-gene interactions or regulation information. Increasing evidences suggest that cell responses are usually organized as pathways or responsive gene modules consisting of a group of interacted genes at the molecular level [1-4]. Identification of the responsive gene modules rather than independent responsive genes can provide better understanding of the underlying molecular mechanisms. With the increasing content of the gene-gene interaction databases, such as protein-protein interaction (PPI) databases and pathway databases, several methods have been developed to identify the responsive gene modules by finding an activ