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Activity Number:
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418
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Type:
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Contributed
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Date/Time:
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Wednesday, August 9, 2006 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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| Abstract - #307585 |
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Title:
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Uncertainty in Clustering Posterior Distributions of Gene Expression Levels Using MCMC Samples
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Author(s):
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Tanzy Love*+
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Companies:
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Carnegie Mellon University
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Address:
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Department of Statistics, Pittsburgh, PA, 15213,
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Keywords:
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clustering ; posterior distributions ; gene expression
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Abstract:
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In time series or multiple treatment microarray experiments, we are interested in locating groups of genes that react together. Subject matter theory designates these groups as coregulated by the same biologic pathways. The statistical problem is clustering genes based on their expression values over multiple treatments. We don't have values for gene expression, rather replicated measurements with error. To incorporate this uncertainty, we have modeled expression estimates using hierarchical models. This provides posterior probability distributions for quantities such as expression value and expression ratio for two treatments. We also can construct the joint posterior probability distribution of these quantities. We use multiple sampling from the posterior distributions of gene expression vectors to cluster genes and estimate the uncertainty in this clustering, an example with maize
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