BackgroundGene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.ResultsTo address this challenge, we introduce Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. We demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise enrichment methods. Further, we provide examples of its utility in differential pathway activity and survival analysis. Lastly, we show how GSVA works analogously with data from both microarray and RNA-seq experiments.ConclusionsGSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org.

A plethora of candidate genes have been identified for complex polygenic disorders, but the underlying disease mechanisms remain largely unknown. We explored the pathophysiology of type 2 diabetes (T2D) by analyzing global gene expression in human pancreatic islets. A group of coexpressed genes (module), enriched for interleukin-1-related genes, was associated with T2D and reduced insulin secretion. One of the module genes that was highly overexpressed in islets from T2D patients is SFRP4, which encodes secreted frizzled-related protein 4. SFRP4 expression correlated with inflammatory markers, and its release from islets was stimulated by interleukin-1β. Elevated systemic SFRP4 caused reduced glucose tolerance through decreased islet expression of Ca(2+) channels and suppressed insulin exocytosis. SFRP4 thus provides a link between islet inflammation and impaired insulin secretion. Moreover, the protein was increased in serum from T2D patients several years before the diagnosis, suggesting that SFRP4 could be a potential biomarker for islet dysfunction in T2D.