This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #307343
Title: A Decision-Theoretic Approach to Gene Set Analysis
Author(s): Simina Maria Boca*+ and Hector Corrada Bravo and Giovanni Parmigiani and Jeffrey Leek
Companies: Johns Hopkins Bloomberg School of Public Health and Johns Hopkins Bloomberg School of Public Health and Harvard University and Johns Hopkins Bloomberg School of Public Health
Address: 615 N Wolfe Street, Baltimore, MD, 21205 ,
Keywords: Genomics ; Gene set analysis ; Bayesian methods ; High-dimensional data ; Decision theory

In genomics, data is often analyzed on a gene by gene basis. Frequently, there is annotation information available that divides these genes into sets, for example GO sets or KEGG pathways. This information can be integrated with the data generated by the present experiment or study. We cast this set-level inference problem in a decision theoretic framework. Our solution addresses two specific problems: 1) the overlap between sets and 2) p-values that are difficult to interpret scientifically.

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