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Activity Number:
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379
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 4, 2009 : 2:00 PM to 3:50 PM
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Sponsor:
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Biometrics Section
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| Abstract - #305693 |
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Title:
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Bayesian Analysis of Microarray Experiments with Multiple Sources of Variation: A Mixed Model Approach
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Author(s):
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Cumhur Y. Demirkale*+ and Dan Nettleton and Tapabrata Maiti
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Companies:
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Iowa State University and Iowa State University and Michigan State University
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Address:
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2505 Aspen Road, Ames, IA, 50010,
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Keywords:
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microarrays ; Bayesian ; variance component
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Abstract:
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Some microarray experiments have complex experimental designs that call for modeling of multiple sources of variation through the inclusion of multiple random factors. While data on thousands of genes are collected in these experiments, the sample size for each gene is usually small. Therefore, in a classical gene-by-gene mixed linear model analysis, there will be very few degrees of freedom to estimate the variance components of all random factors considered in the model and low statistical power for testing fixed effect(s) of interest. To address these challenges, we propose a hierarchical Bayesian modeling strategy to account for important experimental factors and complex correlation structure among the expression measurements for each gene.
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