Difference between revisions of "GSEA"

(Required Parameters)
(Basic Parameters)
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* '''scoring scheme''' - The statistic used to score hits (gene set members) and misses (non-members)
 
* '''scoring scheme''' - The statistic used to score hits (gene set members) and misses (non-members)
 +
** classic
 +
** weighted
 +
** weighted_p2
 +
** weighted_p1.5
 
* '''metric for ranking genes''' - Class separation metric - gene markers are ranked using this metric to produce the gene list
 
* '''metric for ranking genes''' - Class separation metric - gene markers are ranked using this metric to produce the gene list
 +
** Cosine
 +
** Euclidean
 +
** Manhattan
 +
** Pearson
 
* '''min gene set size''' - Gene sets smaller than this are excluded from the analysis
 
* '''min gene set size''' - Gene sets smaller than this are excluded from the analysis
 
* '''max gene set size''' - Gene sets larger than this are excluded from the analysis
 
* '''max gene set size''' - Gene sets larger than this are excluded from the analysis
 
* '''gene list ordering mode''' - Direction in which the gene list should be ordered
 
* '''gene list ordering mode''' - Direction in which the gene list should be ordered
 
+
** descending
 
+
** ascending
  
 
===Advanced Parameters===
 
===Advanced Parameters===

Revision as of 16:35, 14 January 2014

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Overview

Gene Set Enrichment Analysis (Subramanian et al, 2005)

Prerequisites

  • An expression dataset must be loaded in the Workspace.
  • Two (and only two) array sets must be activated in the Arrays component. They do not need to be marked "Case" or "Control", this will have no effect. These sets define the two classes used to calculate a measure of differential expression and from that the rank order of genes.

Parameters

Required Parameters

GSEA Required Parameters.png

  • select gene set database - Gene sets database from GSEA website.
  • upload gene set database - Gene sets database - .gmt, .gmx, .grp. Upload a gene set if your gene set is not listed as a choice for the gene sets database parameter.
  • collapse probe sets - Select yes to have GSEA collapse each probe set in the expression dataset into a single vector for the gene, which gets identified by its gene symbol.
  • select chip platform - Choose the annotation ("Chip") file that matches the expression dataset loaded in the Workspace.
  • upload chip platform - Upload a chip file if your chip is not listed as a choice for the chip platform parameter.
  • permutation type - Type of permutation to perform.
    • phenotype - permute arrays among the two phenotype classes (preferred).
    • gene set - chose random genes sets of the same size as that being tested.
  • number of permutations - Number of permutations to perform.

Basic Parameters

GSEA Basic Parameters.png

  • scoring scheme - The statistic used to score hits (gene set members) and misses (non-members)
    • classic
    • weighted
    • weighted_p2
    • weighted_p1.5
  • metric for ranking genes - Class separation metric - gene markers are ranked using this metric to produce the gene list
    • Cosine
    • Euclidean
    • Manhattan
    • Pearson
  • min gene set size - Gene sets smaller than this are excluded from the analysis
  • max gene set size - Gene sets larger than this are excluded from the analysis
  • gene list ordering mode - Direction in which the gene list should be ordered
    • descending
    • ascending

Advanced Parameters

GSEA Advanced Parameters.png


  • collapse mode - collapsing mode for probe sets with more than one match
  • normalization mode - normalization to apply
  • randomization mode - Type of phenotype randomization (does not apply to gene set permutations)
  • omit features with no symbol match - If there is no known gene symbol match for a probe set, omit it from the collapsed dataset .

Results

GSEA Results Report.png


GSEA Enrichment Results Snapshot.png


GSEA Heatmap.png

References

  • Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES, Mesirov JP. (2005) Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A. 102(43):15545-50. PubMed 16199517
  • GenePattern modules documentation.