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Gene Set Enrichment Analysis
Gene set enrichment analysis or GSEA is a computational method that determines if a defined set of significantly up-regulated or down-regulated genes shows statistically significant differences between two biological states. This method looks at a set of genes that share a common biological functions. For example, the glutamatergic synapse pathway consists of genes that code for glutamatergic receptors and genes that code for glutamate transport into the synaptic cleft. Significant alterations of this set of genes would indicate enrichment of the glutamatergic synapse pathway. This would be predictive of a phenotype of an altered state of glutamate release, uptake, and activation. Many software tools for gene set enrichment analysis take into account the probability of observing a specific number of differentially expressed genes in a given pathway that is greater than or equal to the probability observed by random chance. This allows researchers to gain biological insights from a list of significant genes.

There are many open source R packages and online tools available for a gene set enrichment analysis. Tools for a gene set enrichment analysis include Bioconductor packages such as, SPIA, GAGE , DOSE , and topGO, and online tools such as DAVID and Panther. Also available are many visualization tools such as KEGG, Reactome , PANTHER pathways , BioCyc , and WikiPathways.