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- John D. Storey
- Department of Biostatistics, University of Washington, Seattle, WA 98195; Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Palo Alto, CA 94304; and Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
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- Wenzhong Xiao
- Department of Biostatistics, University of Washington, Seattle, WA 98195; Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Palo Alto, CA 94304; and Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
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- Jeffrey T. Leek
- Department of Biostatistics, University of Washington, Seattle, WA 98195; Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Palo Alto, CA 94304; and Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
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- Ronald G. Tompkins
- Department of Biostatistics, University of Washington, Seattle, WA 98195; Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Palo Alto, CA 94304; and Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
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- Ronald W. Davis
- Department of Biostatistics, University of Washington, Seattle, WA 98195; Stanford Genome Technology Center and Department of Biochemistry, Stanford University, Palo Alto, CA 94304; and Department of Surgery, Shriners Burn Center and Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114
書誌事項
- 公開日
- 2005-09-02
- DOI
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- 10.1073/pnas.0504609102
- 公開者
- Proceedings of the National Academy of Sciences
この論文をさがす
説明
<jats:p>Characterizing the genome-wide dynamic regulation of gene expression is important and will be of much interest in the future. However, there is currently no established method for identifying differentially expressed genes in a time course study. Here we propose a significance method for analyzing time course microarray studies that can be applied to the typical types of comparisons and sampling schemes. This method is applied to two studies on humans. In one study, genes are identified that show differential expression over time in response to<jats:italic>in vivo</jats:italic>endotoxin administration. By using our method, 7,409 genes are called significant at a 1% false-discovery rate level, whereas several existing approaches fail to identify any genes. In another study, 417 genes are identified at a 10% false-discovery rate level that show expression changing with age in the kidney cortex. Here it is also shown that as many as 47% of the genes change with age in a manner more complex than simple exponential growth or decay. The methodology proposed here has been implemented in the freely distributed and open-source<jats:sc>edge</jats:sc>software package.</jats:p>
収録刊行物
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- Proceedings of the National Academy of Sciences
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Proceedings of the National Academy of Sciences 102 (36), 12837-12842, 2005-09-02
Proceedings of the National Academy of Sciences