This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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 - #309100 |
Title:
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Modeling Spatial Structure Using High-Throughput Data
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Author(s):
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Ke Wang*+ and Xinlei Wang and Guanghua Xiao
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Companies:
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Southern Methodist University and Southern Methodist University and UT Southwestern Medical Center
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Address:
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3225 Daniel Avenue, Dallas, TX, 75275-0332, USA
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Keywords:
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microarray ;
bayesian spatial model
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Abstract:
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High throughput technology, which allows for simultaneously collecting large amount of data, has been developed as an important tool in scientific discovery. The density and volume of the data generated in a single experiment continue to grow quickly due to advanced technology. These high density data are often spatially correlated. Since it is typical in practice that a few replicates available due to high cost, modeling the spatial correlation can improve estimation efficiency, and lead to more reliable scientific findings. We use regression model to detect differentially expressed genes in probe level. The spatial correlation of coefficients across different locations (probes in our study) can be modeled using AR(1) model. Both estimators can be smoothed for the expression level of control group, and for the expression level difference between control and treatment groups.
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