跳到主要內容

臺灣博碩士論文加值系統

(3.237.6.124) 您好!臺灣時間:2021/07/24 04:37
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:吳肇騰
研究生(外文):Chao-Teng Wu
論文名稱:經驗貝氏方法在重複基因微陣列晶片之應用
論文名稱(外文):
指導教授:樊采虹樊采虹引用關係
指導教授(外文):Tsai-Hung Fan
學位類別:碩士
校院名稱:國立中央大學
系所名稱:統計研究所
學門:數學及統計學門
學類:統計學類
論文種類:學術論文
論文出版年:2003
畢業學年度:92
語文別:中文
論文頁數:97
中文關鍵詞:基因微陣列經驗貝氏方法
外文關鍵詞:
相關次數:
  • 被引用被引用:2
  • 點閱點閱:314
  • 評分評分:
  • 下載下載:43
  • 收藏至我的研究室書目清單書目收藏:0
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。
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
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.
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top