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    题名: DNA微陣列基因多重檢定比較之問題
    作者: 林雅惠
    Ya-hui Lin
    贡献者: 薛慧敏 老師
    Hui-min Hsueh
    林雅惠
    Ya-hui Lin
    关键词: 個別型一誤差率
    整體誤差率
    多重比較方法
    錯誤發現率
    CWE
    FWE
    MCP
    FDR
    日期: 2001
    上传时间: 2009-09-14
    摘要: 在DNA微陣列基因的實驗中資料包括數千個cDNA 序列，為了要篩選出有差異表現基因，同時針對大量基因個數作假設檢定。若無適當地調整個別檢定問題中的誤差率，則將會膨脹整體的誤差率。在多重假設檢定中為了讓整體誤差率(familywise error rate, FWE)控制在設定水準下，必須調整個別假設檢定之個別型一誤差率CWE的檢定準則，此為多重比較方法(multiple comparison procedures：MCP)。然而當多重比較的個數增加時，控制整體誤差率FWE之傳統的多重比較方法會是過於嚴格的標準，不容易推翻虛無假設，使得檢定的結果太過保守。為了解決此現象，Benjamini and Hochberg(1995) 建議另一種錯誤率：錯誤發現率(false discovery rate：FDR)。錯誤發現率定義為在被拒絕之虛無假設中錯誤拒絕的比例之期望值。而Benjamini and Hochberg(1995)也在文中提出一個得以控制錯誤發現率的多重比較方法，稱為BH方法。本篇論文將詳盡地介紹CWE、FWE和FDR三種誤差率，並提出－修正BH的方法，稱為BH( )。我們將透過電腦模擬驗證出新的修正BH方法之表現比原BH方法有較高的檢定力，且從實例的結果中發現BH( )比原BH方法能檢測出更多的顯著個數。






    關鍵字：個別型一誤差率(CWE)；整體誤差率(FWE)；多重比較方法(MCP)；
    錯誤發現率(FDR)。
    cDNA microarray technology provides tools to study thousands of genes simultaneously. Since a large number of genes are compared, using a conventional significant test leads to the increase of the type I error rate. To avoid the inflation, the adjustment for multiplicity should be considered and a multiple comparison procedure (MCP) that controls the familywise error rate (FWE) is recommended. However, the conservativeness of a MCP that controls FWE becomes more and more severe as the number of comparisons (genes) increases. Instead of FWE, Benjamini and Hochberg (1995) recommended to control the expected proportion of falsely rejecting hypotheses—the false discovery rate (FDR)—and developed a MCP, which has its FDR under control. In this paper, the error rates CWE, FWE and FDR are fully introduced. A new MCP with FDR controlled is developed and its performance is investigated through intensive simulations.











    KEY WORDS：Comparison-wise error rate (CWE)；Familywise error rate (FWE)；Multiple comparison procedure (MCP)；False discovery rate (FDR).
    參考文獻: 1. Benjamini, Y. and Hochberg, Y. (1995) “Controlling the false discovery rate: A practical and powerful approach to multiple testing”. J. R. Statistical Soc. Ser. B, 57, 289-300.
    2. Benjamini, Y. and Liu, W. (1999) “A step-down multiple hypotheses testing procedure that controls the false discovery rate under independence”. Journal of Statistical Planning and Inference, 82(1-2), 163-170.
    3. Benjamini, Y., Yekutieli, D., Reiner, A., Yakubov, R. and Gutman, R. “False Discovery Rate –FDR”. http://www.math.tau.ac.il/~roee/index.htm, Sep 11th, 2002.
    4. Hochberg, Y. (1988) “A sharper Bonferroni procedure for multiple tests of significance”. Biometrika, 75, 800-803.
    5. 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.
    6. Kerr, M. K., Martin, M. and Churchill, G. A.（2000）“Analysis of variance for gene expression microarray data”. Journal of Computational Biology, 7, 819-837.
    7. Miller, R. G..(1981) “Simultaneous Statistical Infrence”. 2nd ed. New York: Springer-Verlag, 67-70.
    8. Nadon, R. and Shoemaker, J. (2002) “Statistical issues with microarrays: processing and analysis”. Trends in Genetics, 18, 265-271.
    9. Simes, R. J. (1986) “An improved Bonferroni procedure for multiple tests of significance”. Biometrika, 73, 751-754.
    10. Yang, Y. H., Dudoit, S., Luu, P. and Speed, T. P. (2001) “Normalization for cDNA microarray data”. In M. L. Bittner, Y. Chen, A. N. Dorsel, and E. R. Dougherty (eds), Microarrays: Optical Technologies and Informatics, Proceedings of SPIE, 4266, 141-152.
    描述: 碩士
    國立政治大學
    統計研究所
    90354021
    90
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0090354021
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

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