This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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360
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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| Abstract - #307343 |
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Title:
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A Decision-Theoretic Approach to Gene Set Analysis
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Author(s):
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Simina Maria Boca*+ and Hector Corrada Bravo and Giovanni Parmigiani and Jeffrey Leek
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Companies:
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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
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Address:
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615 N Wolfe Street, Baltimore, MD, 21205 ,
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Keywords:
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Genomics ;
Gene set analysis ;
Bayesian methods ;
High-dimensional data ;
Decision theory
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Abstract:
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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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