Analyze biological findings with integrated GSEA workbench

There are many ways to analyze the outcome of an experiment in a biological context. In Qlucore Omics Explorer 3.0 we are introducing an integrated Gene Set Enrichment Analysis (GSEA) workbench. This will give you access to a biological analysis of your data with only two mouse clicks. Close integration with the rest of the functionality in Qlucore Omics Explorer also provides other benefits such as the possibility to study the impact of predefined gene sets in multiple experimental data sets.

In addition to GSEA you can use functionality such as the integrated Gene Ontology Browser, the direct download of data from Gene Expression Omnibus (GEO), and the versatile variable analysis tools to further interpret your data. This newsletter provides an introduction to GSEA functionality.

Gene Set Enrichment Analysis (GSEA) is a computational method which determines whether an a priori defined set of genes shows strong and concordant associations with a given predictor (for example, the differences between two biological groups). The method has two phases:

Phase One is the initialization:

• You define a repository of gene sets that you would like to compare your experimental data with. There are several public databases with gene sets, e.g. the MSigDB. You can also create your own repository by storing relevant gene sets in a folder.
• All genes in the experiment data set are ranked according to the ranking criteria you select. It can, for instance, be a t-test statistic or a signal-to-noise ratio. We call the resulting list the ranked experiment list.

Phase Two is the calculation of the enrichment score for each gene set in the repository:

• The algorithm steps through the ranked experiment list, and updates a running score in each step. Genes found in the examined gene set increase the score, and genes not in the gene set decrease the score.
• The enrichment score for a gene set is defined as the largest (positive or negative) value of the running score.

A high positive enrichment score implies that the genes in the gene set are found early in the ranked experiment list and that the gene set is co-regulated with the experiment. A high negative enrichment score implies that the matches are found at the end of the ranked experiment list.

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

References: Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Subramanian, Tamayo, et al. 2005 Proc Natl Acad Sci U S A 102(43):15545-50. Molecular Signatures Database (MSigDB), reference Liberzon et al. 2011 Bioinformatics 27(12):1739-4. Note that the use of the MSigDB is subject to certain conditions