Tuesday, 2 June 2015

Analyzing pathway expression using GSEA

For my first study I profiled gene expression in the retina/RPE/choroid using microarray and analyzed the resulting data-sets using Gene Set Enrichment Analysis (GSEA). GSEA evaluates genome-wide expression profiles to determine whether classes of genes (gene sets) are over-represented. These gene sets are based on a priori knowledge, such as KEGG pathways. GSEAs strength lies in its ability to identify subtle changes distributed across a transcript network that may be missed by more traditional single-gene analyses approaches. This approach can unify results across seemingly disparate related data-sets, which is valuable in a research area like mine where relatively few similarities have been identified across the transcriptome-wide studies conducted to date. On a practical level, the results of GSEA are also more interpretable than large lists of individual differentially-expressed genes as they are based in an established biological framework.

When I first started using GSEA to evaluate my dataset our lab had a subscription to Pathway Studio. The Pathway Studio implementation was easy to use and had great graphics; unfortunately our licence expired before I was finished. I switched to the Broad Institute’s GSEA software using the graphical java interface. I’m now glad I was forced to move to a freeware platform, as I think the Broad software gave me greater control of the analysis and the ability to explore the results in more depth. 

The Broad GSEA wiki provides a useful over-view of how to use the application. I found that leading edge analysis was a particularly important tool when interpreting results. This analysis identifies the core genes responsible for enrichment of a gene set, and over-lap in these genes across enriched pathways.

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