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| Title: | DNA微陣列基因顯著性分析驗證 |
| Authors: | 蘇慧玲 |
| Contributors: | 薛慧敏
蘇慧玲 |
| Keywords: | 整體誤差率
多重比較方法
錯誤發現率 |
| Date: | 2002 |
| Issue Date: | 2009-09-14 |
| Abstract: | 摘 要
在基因微陣列(DNA microarrays)的技術中，可同時得到數以千筆的資料，為了找出具有顯著差異的基因，一般會考慮控制整體誤差率(familywise error rate,FWE) 的多重比較方法(multiple comparison procedures,MCP)。但當基因數或假設檢定個數過多時，其檢定會產生不易拒絕虛無假設的結果，使得結論過於保守。為解決此一問題，Benjamini & Hochberg(1995)建議採用控制錯誤發現率(false discovery rate,FDR)的方法來替代整體誤差率FWE。且Tusher et al.(2001)在DNA微陣列顯著分析(significance analysis of microarrays,SAM)的文章中提出利用排列分佈(permutations)估計錯誤發現率FDR的方法。本篇論文將介紹Tusher et al.(2001)所提出的SAM估計錯誤發現率FDR的方法，且提出一修正SAM方法：SAMM。另外介紹兩種控制顯著水準的統計方法：SAME和SAMT(t檢定)。透過電腦模擬驗證四種方法其錯誤發現率FDR的表現。
Abstract
DNA microarray technology provides tools enable to simultaneously study thousands of genes. A conservative multiple comparison procedure (MCP) controlling the familywise type I error rate (FWE) is considered. However, the conservativeness of a MCP becomes more and more severe as the number of comparisons (genes) increases. Instead of FWE, another error rate, the false discovery rate (FDR), is suggested. Tusher et al.(2001) proposed a statistical procedure, the Significance Analysis of Microarrays (SAM), to analyze a microarray data set. In which, the conclusion is drawn at a specific threshold and the false discovery rate (FDR) of the conclusion is estimated by permutations. In this paper, inspired by the SAM, three other methods are proposed. The performances of these methods are investigated and compared through simulations. |
| Reference: | 參考文獻
Benjamini, Y. & Hochberg, Y. (1995) “Controlling the false discovery rate: a practical and powerful approach to multiple testing”. J. R. Stat. Soc.Ser. B-Methodological, 57,289-300.
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Kerr, M. K., Afshari, C. A., Bennett, L., Bushel, P., Martinez, J., Walker, N. J. and Churchill, G. A. (2001) “Statistical Analysis of a Gene Expression Microarray Experiment with Replication”. Statistica Sinica, 12, 203-218.
Kerr, M.K., Martin, M., and Churchill G.A. (2000). “Analysis of Variance for Gene Expres-sion Microarray Data.” Journal of Computational Biology, 7, 819-837.
Kikuchi, H., Hossain, A., Yoshida, H., and Kobayashi, S. (1998). “Induction of Cytochrome P-450 1A1 by Omeprazole in Human HepG2 Cells is Protein Tyrosine Kinase-Dependent and is Not Inhibited by Alpha-Naphtho avone.” Archives of Biochemical Biophysics , 358, 351-358.
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Storey, J. D. & Tibshirani, R. (2003) “SAM thresholding and false discovery rates for detecting differential gene expression”. In The Analysis of Gene Expression Data: Methods and Software, by G Parmigiani, ES Garrett, RA Irizarry and SL Zeger (editors). Springer, New York. Available at http://www.stat.berkeley.edu/~storey/.
Tusher, V. G., Tibshirani, R., and Chu, G.(2001) “ Significance analysis of microarrays applied to the ionizing radiation response” Proc. Natl. Acad. Sci. USA, 98,5116-5121.
Yang, Y.H., Dudoit, S., Luu, P., and Speed, T.P. (2000) “Normalization for cDNA Microar-ray Data”. Technical report 589, Department of Statistics, University of California, Berkeley. Available at http://www.stat.berkeley.edu/users/terry/zarray
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| Description: | 碩士
國立政治大學
統計研究所
90354005
91 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0090354005 |
| Data Type: | thesis |
| Appears in Collections: | [統計學系] 學位論文
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