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

Abstract Details

Activity Number: 524
Type: Contributed
Date/Time: Wednesday, August 4, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #309226
Title: Outcome-Informed Clustering of Gene Expression Profiles
Author(s): Jessie Jann Hsu*+ and David Schoenfeld and Dianne Finkelstein
Companies: Harvard University and Harvard University and Harvard University
Address: 170 Brookline Ave #1010, Boston, MA, 02215,
Keywords: Clustering ; K-means ; Bayesian inference ; Microarray
Abstract:

We present a model-based method for simultaneous clustering on gene expression data and outcome. Clustering is a popular learning technique used for subset and pattern discovery in high-dimensional data sets. Though model-based clustering of microarray data has been studied extensively, models that suggest a relationship between gene clusters and clinical outcomes have rarely been explored. We propose a joint model for clustering gene expression profiles and predicting patient outcome. We fit the model using a k-means clustering approach as well as a Bayesian clustering approach and compare the results through simulation studies. These methods are applied to trauma data from the Inflammation and Host Response to Injury research program.


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