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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 - #304803 |
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Title:
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Multivariate Analysis of Variance Test for Gene Set Analysis
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Author(s):
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Chen-An Tsai*+ and James Chen
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Companies:
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China Medical University and National Center for Toxicological Research
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Address:
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91 Hsueh-Shih Road, Taichung, 404, Taiwan
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Keywords:
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Gene Set Enrichment Analysis ; multivariate analysis of variance ; Type I error ; power
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Abstract:
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Gene Set Enrichment Analysis (GSEA) is a statistical approach to determine whether some functionally predefined sets of genes express differently under different experimental conditions. Most alternative GSEA methods are developed for data collected from two experimental conditions, and most is based on a univariate gene-by-gene analysis or assume independence without accounting for functional relationships among genes in the gene set. In this paper, a multivariate analysis of variance (MANOVA) approach is proposed for studies with two or more experimental conditions. The MANOVA test and six other GSEA published methods are evaluated using simulations and publicly available microarray data sets under two and three experimental conditions. The MANOVA test appears to perform the best in terms of control of Type I error and power under the models considered in the simulations.
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