Functional analysis with GSEA. One step closer to a breakthrough

Incorporating functional information such as pathways, gene sets and other structured lists of biologically functionally relevant information in the analysis and interpretation of biological data can be essential for analysis breakthroughs.

Qlucore Omics Explorer (QOE) includes a generic workbench for analysis of functional information gathered in lists, using the Gene Set Enrichment Analysis (GSEA) method. The workbench makes this method directly available within QOE and it requires only two additional mouse-clicks to start the functional analysis. The tight integration with all other functionality in QOE as well as the optimized performance provides extensive benefits over standalone solutions.

To perform a functional analysis, two components are needed: a data set with measurements of the features (variables) and the functional information stored in lists. The GSEA method calls these lists “gene sets”. The functional information can typically be gene ontology categories or pathways. Gene set definitions are often acquired from open online repositories such as MSigDB and Reactome.

The Gene Set Enrichment Analysis (GSEA) algorithm is a computational method that determines whether an a priori defined gene set shows strong and concordant associations with a given predictor (for example, differences between two biological groups).

In Qlucore Omics Explorer you perform the GSEA analysis by starting “New GSEA Workbench” from the View menu, selecting your ranking criteria and pressing 'Run'. It couldn't be easier!

You can read more about pathway analysis with Qlucore Omics Explorer in the “How to Do Pathway analysis” document. There you will also find a webinar demonstrating the GSEA functionality.

References: GSEA: Subramanian, Tamayo, et al. 2005 Proc Natl Acad Sci U S A 102(43):15545-50. MSigDB: Liberzon et al. 2011 Bioinformatics 27(12):1739-4. Reactome: Croft et al. 2014 PMID: 24243840 and Milacic et al. 2012 PMID:24213504