Title page for 90225012


[Back to Results | New Search]

Student Number 90225012
Author Chao-Teng Wu(吳肇騰)
Author's Email Address s0225012@cc.ncu.edu.tw
Statistics This thesis had been viewed 1771 times. Download 1054 times.
Department Graduate Institute of Statistics
Year 2003
Semester 1
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title 經驗貝氏方法在重複基因微陣列晶片之應用
Date of Defense 2003-12-16
Page Count 97
Keyword
  • 基因微陣列
  • 經驗貝氏方法
  • Abstract   過去為研究基因之功能以及相互影響的模式,所應用的方法往往需要大量時間與金錢,卻常無法得到有效的實驗結果,而近年來由於生物晶片 (biochips) 製作技術的成熟,可同時對大量資料作實驗,因而使其應用範圍越加廣泛。
      本文根據Shieh和Fan (2003) 建立一伽碼-常態-伽碼之三成份混合模型分析單一晶片之基因表現資料,並推廣至重複實驗晶片以經驗貝氏方法分析基因表現資料,希望能在多片資料具共同模型與獨立模型中取其折衷,結果顯示經驗貝氏方法確實具此效果,並依其參數估計結果以貝氏預測勝算用之於差異表現基因的鑑別上。最後,將結果運用於一組真實的重複實驗基因微陣列資料中。
    Table of Content 第一章 緒論 ……………………………………………… 1
    1.1 研究動機與目的 …………………………………… 1
    1.2 文獻回顧 …………………………………………… 4
    1.2 研究方法 …………………………………………… 8
    第二章 單片資料之混合模型 …………………………… 11
    2.1 均等-常態-均等模型  ……………………………… 11
    2.2 伽瑪-常態-伽瑪模型  ……………………………… 14
    2.3 單晶片模型之參數估計 ……………………………… 15
    2.3.1 最大概似估計 ……………………………… 15
    2.3.2 貝氏估計 …………………………………… 17
    2.4 異常表現基因之選取 ………………………………… 20
    第三章 重複實驗資料之混合模型 ……………………… 23
    3.1 重複資料之伽瑪-常態-伽瑪模型 …………………… 23
    3.1.1 高維度模型之參數估計 ……………………… 24
    3.1.2 低維度模型之參數估計 ……………………… 26
    3.2 經驗貝氏模型 ……………………………………… 26
    3.2.1 貝氏估計 ……………………………………… 27
    3.2.2 經驗貝氏估計 ………………………………… 29
    3.3 異常表現基因之選取 ……………………………… 32
    第四章 模擬研究與實例分析 …………………………… 35
    4.1 均方誤差 …………………………………………… 35
    4.1.1 高維度模型之資料 …………………………… 35
    4.1.2 低維度模型之資料 …………………………… 37
    4.2 貝氏風險 …………………………………………… 39
    4.2.1 高維度模型之資料 …………………………… 39
    4.2.2 低維度模型之資料 …………………………… 40
    4.3 差異表現基因之鑑別 ……………………………… 42
    4.3.1 高維度模型之資料 …………………………… 42
    4.3.2 低維度模型之資料 …………………………… 46
    4.4 實例分析 …………………………………………… 49
    4.4.1 參數估計 ……………………………………… 49
    4.4.2 臨界值的決定 ………………………………… 51
    4.3 差異表現基因之鑑別 …………………………… 52
    第五章 結論 ……………………………………………… 88
    參考文獻 …………………………………………………… 90
    附錄 ………………………………………………………… 96
    Reference 中文部分:
    [1] 基因微陣列之簡介及其應用:國科會微陣列基因體醫學核心實驗室;網址:http://microarray.mc.ntu.edu.tw/
    [2] 交大生物科技諮詢網;網址:http://biotech.life.nctu.edu.tw/
    英文部分:
    [1] Berger, J.O. (1984). The robust Bayesian viewpoint (with discussion). In Robustness in Bayesian Statistics, ed. J. Kadane, Amsterdam: North Holland.
    [2] Berger, J.O. (1985). Statistical decision theory and Bayesian analysis. New York: Springer-Verlag.
    [3] Berger, J.O. (1986). Robust Bayes and empirical Bayes analysis with -contaminated priors. Annals of Statistics, 14, 461-486.
    [4] Carlin, B.P. and Louis, T.A. (2000). Bayes and empirical Bayes methods for data analysis. New York: Chapman and Hall,
    [5] Casella, G. (1985). An introduction to empirical Bayes data analysis. The American Statistician, 39, 83-87.
    [6] Chen, Y., Dougherty, E.R., and Bittner, M.L. (1997). Ratio-based decisions and the quantitative analysis of cDNA microarray images. Journal of Biomedical Optics, 4, 364–374.
    [7] Dempster, A.P., Laird, N.M. and Rubin, D.B. (1977). Maximum likelihood estimation from incomplete data via the EM algorithm (with discussion). Journal of the Royal Statistical Society, Series B, 39, 1-38.
    [8] Dennis, B. and Patil, G.P. (1984). The gamma distribution and weighted multimodal gamma distribution as models of population abundance. Mathematical Biosciences, 68, 187-212.
    [9] Dudoit, S., Yang, Y.H., Callow, M.J. and Speed, T.P. (2000). Statistical methods for identifing differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica, 12, 111-139.
    [10] Efron, B., Tibshirani, R., Goss, V., and Chu, G. (2001). Microarrays and their use in a comparative experiment. Journal of the American Statistical Association, 96, 1151-1160.
    [11] Eisen, M.B., Spellman, P.T., Brown, P.O., and Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proceedings of the National Academy of Sciences, 95, 14863–14868.
    [12] Geman, S. and Geman, D. (1984). Stochastic relaxation, gibbs distributions and the Bayesian restoration of images. IEEE Trans. on Pattern Analysis and Machine Intelligence, 6, 721-741.
    [13] Gilks, W.R., Richardson, S., and Spiegelhalter, D.J., Eds. (1996). Markov Chain Monte Carlo in practice. London:Chapman and Hall.
    [14] Golub, T.R., Slonim, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Bloomfield, C.D., and Lander, E.S. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring, Science, 286, 531–537.
    [15] Greenwood, J.A. and Durand, D. (1960). Aids for fitting the gamma distribution by maximum likelihood. Technometrics, 2, 55-65.
    [16] Hastie, T., Tibshirani, R., Eisen, M., Brown, P., Ross, D., Scherf, U., Weinstein, J., Alizadeh, A., Staudt, L., and Botstein, D. (2001). Gene shaving: A new class of clustering methods for expression arrays. Genome Biology, 2(1), research0003.1-0003.12.
    [17] Hastings, W.K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
    [18] Ibrahim, J.G., Chen, M.H. and Gary, R.J. (2002) Bayesian models for genes expression with DNA microarray data. Journal of the American Statistical Association, 457, 88-99.
    [19] Kendziorski, C.M., Newton, M.A., Lan, H. and Gould, M.N. (2003) On parametric empirical Bayes methods for the comparing multiple groups using replicated gene expression profiles. Statistics in Medicine, 22, 3899-3914.
    [20] Kerr, M.K., Afshari, C.A., Bennett, L., Bushel, P., Martinez, J., Walker, N.J. and Churchill G.A. (2002) Statistical analysis of a gene expression microarray experiment with replication. Statistica Sinica, 12, 203-217.
    [21] Metropolis, N., Rosenbluth, A.W., Rosenbluth, M.N., Teller, A.H. and Teller, E. (1953). Educations of stat calculations by fast computing machines. Journal of Chemical Physics, 21, 1087-1091.
    [22] Morris, C.N., (1983a). Parametric empirical Bayes inference: Theory and applications. Journal of the American Statistical Association, 78, 47-65.
    [23] Morris, C.N., (1983b). Natural exponential families with quadratic variance functions: Statistical theory. Annals of Statistics, 11, 515-529
    [24] Newton, M.A., Kendziorski, C.M., Richmond, C.S., Blattner, F.R. and Tsui, K.W. (2001). On differrential variability of expression ratios: Improving statistical inference about gene expression changes from microarray data. Journal of Computational Biology, 8, 37-52.
    [25] Robbins, H. (1955). An empirical Bayes approach to statistics. In Proceedings of 3rd Berkeley Symp. Mathematical Statistics And Probability, 1, Berkeley, CA: Univ. of California Press, 157-164.
    [26] Sabatti, C. (2001). Inference on gene expression changes as measured with DNA microarrays.
    網址:http://www.stat.ucla.edu:16080/~sabatti/statarray/change.pdf
    [27] Sapir, M. and Churchill, G.A. (2000). Estimating the posterior probability of differential gene expression from microarray data. Poster, The Jackson Laboratory.
    網址:http://www.jax.org/research/churchill/
    [28] Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.
    [29] Shieh and Fan (2003). Analyzing single-slide microarray gene expression data by a Bayesian approach. Manuscript.
    [30] Spellman, P.T., Sherlock, G., Zhang, M.Q., Iyer, V.R., Anders, K., Eisen, M.B., Brown, P.B., Botstein, D., and Futcher, B. (1998). Comprehensive identification of cell cycle-regulated genes of the yeast saccharomyces cerevisiae by microarray hybridization. Molecular Biology of the Cell, 9, 3273–3297.
    [31] Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., and Dmitrovsky, E. (1999). Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation. Proceedings of the National Academy of Sciences, 96, 2907–2912.
    [32] Tanner, M.A. (1996). Tools for statistical inference: Methods for the exploration of posterior distributions likelihood functions. Third Edition. Springer, New York.
    [33] Tibshirani, R., Hastie, T., Eisen, M., Ross, D., Botstein, D., and Brown, P. (2000). Clustering methods for the analysis of DNA microarray data. Stanford University.
    Advisor
  • Tsai-Hung Fan(樊采虹)
  • Files
  • 90225012.pdf
  • approve immediately
    Date of Submission 2004-01-15

    [Back to Results | New Search]


    Browse | Search All Available ETDs

    If you have dissertation-related questions, please contact with the NCU library extension service section.
    Our service phone is (03)422-7151 Ext. 57407,E-mail is also welcomed.