On gene ranking using replicated microarray time course data |
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Authors: | Tai Yu Chuan Speed Terence P |
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Institution: | Institute for Human Genetics, 513 Parnassus Avenue S965, University of California, San Francisco, California 94143-0794, U.S.A. email:; and Department of Statistics, 367 Evans Hall, University of California, Berkeley, California 94720-3860, U.S.A. email:;  |
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Abstract: | Summary . Consider the ranking of genes using data from replicated microarray time course experiments, where there are multiple biological conditions, and the genes of interest are those whose temporal profiles differ across conditions. We derive a multisample multivariate empirical Bayes' statistic for ranking genes in the order of differential expression, from both longitudinal and cross-sectional replicated developmental microarray time course data. Our longitudinal multisample model assumes that time course replicates are independent and identically distributed multivariate normal vectors. On the other hand, we construct a cross-sectional model using a normal regression framework with any appropriate basis for the design matrices. In both cases, we use natural conjugate priors in our empirical Bayes' setting which guarantee closed form solutions for the posterior odds. The simulations and two case studies using published worm and mouse microarray time course datasets indicate that the proposed approaches perform satisfactorily. |
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Keywords: | Cross-sectional Empirical Bayes Gene ranking Longitudinal Microarray time course |
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