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Activity Number:
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514
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Type:
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Contributed
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Date/Time:
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Thursday, August 7, 2008 : 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 - #301472 |
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Title:
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A Model-Based Approach for Detecting Differentially Expressed Genes and Estimating the FDR
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Author(s):
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Shunpu Zhang*+
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Companies:
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University of Nebraska-Lincoln
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Address:
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Department of Statistics, Lincoln, NE, 68583-0963,
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Keywords:
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microarray ; differentially expressed genes ; mixture model method ; t-mixture model ; false discovery rate
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Abstract:
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The main challenge of the analysis of microarray data is to detect differentially expressed (DE) genes under different conditions. In this article, we propose a new model based method for analyzing the microarray data obtained under two experimental conditions. We show that our proposed method does not have the problems of the non-model based methods. In addition, our method provides simultaneous control of the gene-specific significance level and the false discovery rate (FDR). Finally, the proposed method is evaluated using extensive simulations and real microarray data.
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