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論文名稱 Title |
基因表現與改變形態的模型分群法研究 Model-Based Clustering for Gene Expression and Change Patterns |
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系所名稱 Department |
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畢業學年期 Year, semester |
語文別 Language |
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學位類別 Degree |
頁數 Number of pages |
45 |
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研究生 Author |
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指導教授 Advisor |
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召集委員 Convenor |
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口試委員 Advisory Committee |
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口試日期 Date of Exam |
2011-06-23 |
繳交日期 Date of Submission |
2011-07-29 |
關鍵字 Keywords |
傅立葉係數、基因表現、模型分群法、小波係數、酵母菌細胞週期 Gene expression, Model-based clustering, Wavelet coefficients, Fourier coefficients, Yeast cell cycle data |
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統計 Statistics |
本論文已被瀏覽 5827 次,被下載 5 次 The thesis/dissertation has been browsed 5827 times, has been downloaded 5 times. |
中文摘要 |
生物上認為具有相關性的基因之間擁有相似的模式,所以研究細胞基因表現與改變型態為一個重要的議題。在本篇論文中,透過模型分群法,找出擁有相似表現與改變型態的基因。將觀測基因表現模式的數據,透過傅立葉轉換和小波轉換,所得的傅立葉係數和小波係數作為分群的變數。在本研究中提出兩階段的分群方法,對基因表現與改變型態分群,並透過模擬研究來比較兩種分群方法的效率性。在實證分析上,以酵母菌細胞週期的數據例,探討分群方法的可行性。 |
Abstract |
It is important to study gene expression and change patterns over a time period because biologically related gene groups are likely to share similar patterns. In this study, similar gene expression and change patterns are found via model-based clustering method. Fourier and wavelet coefficients of gene expression data are used as the clustering variables. A two-stage model-based method is proposed for stepwise clustering of expression and change patterns. Simulation study is performed to investigate the effectiveness of the proposed methodology. Yeast cell cycle data are analyzed. |
目次 Table of Contents |
論文審定書i 謝誌ii 摘要iii Abstract iv 1 Introduction 1 2 The Model and Orthogonal Transform 2 2.1 Fourier Transform 3 2.2 Wavelet Transform 7 3 Clustering methods 10 3.1 Model-based Agglomerative Hierarchical Clustering 10 3.1.1 Model-based Clustering 10 3.1.2 Model-based Agglomerative Hierarchical Clustering 11 3.1.3 Estimation of the Number of Clusters 11 3.2 K-means Clustering Method 13 3.3 The Clustering Strategies 13 3.3.1 One-stage Method 14 3.3.2 Two-stage Method 15 3.3.3 Two-stage(A) Method 15 4 The Cell Cycle and Saccharomyces Cerevisiae Data 15 5 Simulation Study 17 5.1 Comparison with Kim and Kim (2008) 17 5.2 Simulation with Yeast Cell Cycle Data 19 6 Conclusion 21 References 23 Appendix 24 A.1 Tables 24 A.2 Figures 31 |
參考文獻 References |
[1] Banfeild, J. D. and Raftery, A. E. (1993). Model-Based Gaussian and Non-Gaussian Clustering. Biometrics, 49, 803-821. [2] Fraley, C. (1998). Algorithms for model-based Gaussian hierarchical clustering. SIAM Journal on Scienti‾c Computing, 20, 270-281. [3] Fraley, C. and Raftery, A. E. (2006). MCLUST Version 3 for R: Normal Mixture Mod- eling and Model-Based Clustering. Technical Report no. 504, Department of Statistics, University of Washington. [4] Kim, J. and Kim, H. (2008). Clustering of Change Patterns Using Fourier Coefficients. Bioinformatics, 24, 184-191. [5] Percival, D. B. and Walden, A. T. (2000). Wavelet Methods for Time Series Analysis. Cambridge University Press, Cambridge. [6] Scott, A. J. and Symons, M. J. (1971). Clustering methods based on likelihood ratio criteria. Biometrics, 27, 387-397. [7] Wei, W.S. (2006). Time series analysis : univariate and multivariate methods, 2nd Edition. Pearson Addison Wesley, Boston. |
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