Enhancing gene set enrichment using networks

摘要: Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands differentially expressed genes. This has led to the introduction set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA results in hundreds regulated sets. Similar they contain, are a correlative fashion because share many describe related processes. Using these kind neighborhood information construct networks allows identify highly connected sub-networks as well poorly islands singletons. We show here how topological and other network features can be used filter prioritize routine DGE studies. Community detection combination with automatic labeling representation clusters further constitute an appealing intuitive visualization results. The RICHNET workflow described does not require human intervention thus conveniently incorporated automated pipelines.