A permutation-based multiple testing method for time-course microarray experiments |
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Authors: | Insuk Sohn Kouros Owzar Stephen L George Sujong Kim and Sin-Ho Jung |
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Institution: | (1) Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina 27710, USA;(2) CALGB Statistical Center, Durham, North Carolina 27705, USA;(3) Skin Research Institute, AmorePacific R&D Center, Yongin, 449-729, Republic of Korea;(4) R&D Center, Komipharm International Co, LTD, Kyounggi-do, 429-450, Republic of Korea |
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Abstract: | Background Time-course microarray experiments are widely used to study the temporal profiles of gene expression. Storey et al. (2005) developed a method for analyzing time-course microarray studies that can be applied to discovering genes whose expression
trajectories change over time within a single biological group, or those that follow different time trajectories among multiple
groups. They estimated the expression trajectories of each gene using natural cubic splines under the null (no time-course)
and alternative (time-course) hypotheses, and used a goodness of fit test statistic to quantify the discrepancy. The null
distribution of the statistic was approximated through a bootstrap method. Gene expression levels in microarray data are often
complicatedly correlated. An accurate type I error control adjusting for multiple testing requires the joint null distribution
of test statistics for a large number of genes. For this purpose, permutation methods have been widely used because of computational
ease and their intuitive interpretation. |
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