Computer Science Colloquium

Gene Set Enrichment Analysis

Sayan Mukherjee
MIT

Monday, March 8, 2004 11:30 A.M.
Room 1302 Warren Weaver Hall
251 Mercer Street
New York, NY 10012-1185

Directions: http://cs.nyu.edu/csweb/Location/directions.html
Colloquium Information: http://cs.nyu.edu/csweb/Calendar/colloquium/index.html

Hosts:

Richard Cole cole@cs.nyu.edu, (212) 998-3119

Abstract

The selection and analysis of differentially expressed gene profiles (markers) helps associate a biological phenotype with its underlying molecular mechanisms and provides valuable insights into the structure of pathways and cellular regulation. However, analyzing and interpreting a given list of gene markers to glean useful biological insights can be extremely challenging. This is in part due to the difficulty of objectively evaluating how well members of a given a pathway or functional class of interest (Gene Set) are represented in the markers list. To address this problem we introduce a statistical methodology called Gene Set Enrichment Analysis (GSEA) for determining whether a given Gene Set is over-represented or enriched in a Gene List of markers ordered by their correlation with a phenotype or class distinction of interest. The method is based upon a score computed as the maximum deviation of a random walk (in the same spirit as the Kolmogorov-Smirnov statistic) and uses permutation testing to assess significance. When multiple Gene Sets are tested simultaneously we propose two approaches to address the multiplicity: Validation GSEA which controls the Familywise error rate (FWER) and Discovery GSEA which controls the False Discovery rate (FDR). The utility of this procedure will be illustrated on two biological problems: validating a mouse model of lung cancer and finding chromosomal dislocations for myeloid leukemia.


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