= GSEA Gene Set Enrichment Analysis (www.broadinstitute.org/gsea ) =

==== quick guide to GSEA interpretation of the results ====
 * which values to consider for interpretating results:
|| number of gene-set tested || 1 gene-set || few gene-sets (3-10)|| a lot of gene-sets >3000 ||
|| ES || (./) ||not recommended ||not recommended ||
|| NES ||not informative || (./) || (./) ||
|| nom p-value || (./) ||better to use FDR ||better to use FDR ||
|| FDR || not informative || (./) || (./) (*) ||
(*) FDR values are going to be pessimistic due to the high number of tested gene-sets and therefore the high p-value adjustment needed.

 * ES (enrichment score): reflects the degree to which a gene-set is overrepresented at the top or bottom of a ranked list of genes.
 * NES (normalized enrichment score): NES corrects for differences in ES between gene-sets due to differences in gene-set sizes.  It enables to compare the scores of the different tested gene-sets with each other.
  NES = actual ES / mean of all ESs obtained from all random permutations for the single gene-set that is being tested
 * nom p-value: The nominal p value estimates the statistical significance of the enrichment score for a single gene set. THe p-value is calculated from the null distribution.
  Using gene-set permutation, the null distribution is created by generating, for each permutation, a random gene set the same size as your specified    gene set by selecting that number of genes from all of the genes in your expression data set (or pre-ranked list), and then calculating the enrichment score for that randomly selected gene set. The distribution of those enrichment scores across all of the permutations constitutes the null distribution.
 * FDR: corrects for multiple hypothesis testing and enable a more correct comparison of the different tested gene-sets with each other. 
  * note: for a given gene-set S and observed NES, called NES*, FDR is [% of all NES (including permutations) >= NES*] / [% of all observed NES (=NES for all tested gene-sets) >= NES*]

{{attachment:plotGSEA_FDR_pval.png}}

=== Explanation of GSEA ES score and values from Wang and Murray (BMC Bioinformatics 2013): ===
 * [[attachment:GSEA_explanation_Wang_Murray.pdf |  link to explanation of GSEA by Wang and Murray]]

=== Tips on how to install GSEA locally and launch it from the command line: ===

 * Download and Save the gsea2-2.0.14.jar file in your folder Documents
 * open your console/terminal window
 * Type the command for MAC:
  *"java -Xmx2G -jar ~/Documents/gsea2-2.0.14.jar"
 * Type the command for Windows:
  * "cd Documents"
  * "java -Xmx2G –jar gsea2-2.0.14.jar"

=== FAQs ===