Abstract #301828

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JSM 2002 Abstract #301828
Activity Number: 93
Type: Topic Contributed
Date/Time: Monday, August 12, 2002 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing*
Abstract - #301828
Title: Detecting Differential Expression with Semiparametric MixtureModels
Author(s): Michael Newton*+
Affiliation(s): University of Wisconsin, Madison
Address: 1210 W Dayton Street, Madison, Wisconsin, 53706-1613,
Keywords:
Abstract:

Mixture models provide a convenient framework to analyze gene expression profiles. Very simply, some genes are differentially expressed between conditions and some are not, and there are benefits to treating this decision as stochastic. I will discuss a method that treats the distribution of underlying mean gene expression levels nonparametrically. Straightforward computational techniques yield a smooth approximation to the nonparametric maximum likelihood estimate. The method is applied to data from a study of uveal melanoma.

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