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系統識別號 U0026-0507201014535000
論文名稱(中文) 多時期微陣列資料的模型配適與比較
論文名稱(英文) Model Fitting and Comparison for Time-Course Microarray Data
校院名稱 成功大學
系所名稱(中) 統計學系碩博士班
系所名稱(英) Department of Statistics
學年度 98
學期 2
出版年 99
研究生(中文) 鍾孟倫
研究生(英文) Meng-Lun Chung
學號 r2697406
學位類別 碩士
語文別 英文
論文頁數 60頁
口試委員 指導教授-詹世煌
口試委員-李隆安
口試委員-陳君厚
口試委員-辛致煒
口試委員-鄧致剛
中文關鍵字 多時期微陣列  基因表現值  迴歸模型 
英文關鍵字 time-course microarray data  gene expression  regression model 
學科別分類
中文摘要 近年來由於基因微陣列(microarray)技術的蓬勃發展,生物學家可以在同一時間內分析成千上萬的基因,並可利用微陣列技術研究在不同環境下之基因表現差異,探索基因微妙的運作過程。多時期微陣列(time-course microarray)是在特定的一段實驗時間中,分別在不同時間點量測基因表現值,能有效瞭解基因在隨著時間而有不同的表現變化情形。而往往基因體的運作過程極為複雜,要在龐雜的基因表現數據中萃取出有意義的生物訊息與找出顯著的基因就成為統計上的熱門議題。本文以迴歸模型配適多時期的微陣列基因資料,考慮了分段線性迴歸模型,二次迴歸模型以及傅立葉迴歸模型,並以迴歸配適後的轉折點與斜率分群。藉由分群的方法,找出在不同環境下有相似表現行為的基因。
英文摘要 Microarray technologies in molecular biology make it possible to simultaneously measure the expression levels of thousands of genes for a certain organism, has advanced to unravel the biological insight at the genome scale and to study the behavior of massive genes simultaneously under various environment conditions. The experiment of time-course microarray is the expression profiles of genes are repeatedly measured over a time period, and is effective not only in studying gene expression profile levels cross time but also in exploring functions and interactions of genes. Since biological processes are complex systems, the major statistic strategy is how to character the behaviors of genes and identify significant genes. It is believed that genes demonstrating similar expression profiles over time might give an informative insight into underlying biological mechanisms work. This thesis intends to investigate the significant genes in time-course microarray experiment, and present regression models for clustering periodic patterns of gene expression. We consider piecewise linear regression, quadratic regression and Fourier regression function to fit the time-course microarray data, finding a best model in fitting the calorie restriction microarray data.
論文目次 Chaper1 Introduction 1

Chaper2 Significant Gene Analysis for Microarray Data 3
2.1. Literature Review 3
2.2. Modeling Time-Course Microarray Data 5

Chaper3 Simulation 7
3.1. Simulation 1: Model comparison 7
3.1.1. Simulation Setting 7
3.1.2. Simulation Results 9
3.2. Simulation 2: K-means clustering method based on regression model 11
3.2.1. Simulation Setting 11
3.2.2. Simulation Results 15

Chaper4 Model Fitting and Identification of Significant Gene Expressions 17
4.1. Calorie Restriction and Time-Course Microarray Data 17
4.2. Patterns of Gene Expression 19
4.3. Model fitting for Microarray data 21
4.3.1. Fitting piecewise linear regression function to time-course microarray data 21
4.3.2. Quadratic regression model 28
4.3.3. Fourier regression model 33

Chaper5 Conclusions 38

References 40

Appendix 42
參考文獻 Alter, O., Brown, P. O., Botstein, D. (2000), “Singular value decomposition for genome-wide expression data processing and modeling”, Proceedings National Academy of Science, 97, 10101-10106.

Bar-Joseph, et al (2003), “Continuous representations of time series gene expression data”, Journal of Computational Biology, 10(3-4), 341-356.

Efron, B., Tibshirani, R., Storey, J. D. and Tusher, V. (2001), "Empirical Bayes analysis of a microarray experiment", Journal of the American Statistical Association, 96, 1151-1160.

Eisen MB, Spellman PT, Brown PO, Botstein D (1998), “Cluster analysis and display of genome-wide expression patterns”, Proceedings National Academy of Science, 95(25), 14863-14868.

Ernst J, Nau GJ, Bar-Joseph Z. (2005), “Clustering short time series gene expression data”, Bioinformatics, 21, 159-168.

Fraley,C. and Raftery,A.E. (2002), “Model-based clustering, discriminant analysis, and Density Estimation”, Journal of the American Statistical Association, 97, 611-631.

Kim Jaehee, Kim Haseong (2008), “Clustering of change patterns using Fourier coefficients”, Bioinformatics, 24,184-191.

Luan Y, Li H (2003), “Clustering of time-course gene expression data using a mixed-effects model with B-splines”, Bioinformatics, 19, 474-482.

Luan Y, Li H (2004), “Model-based methods for identifying periodically expressed genes based on time course microarray gene expression data”, Bioinformatics, 20, 332-339.

Newton et al. (2001),“On Differential Variability of Expression Ratios: Improving Statistical Inference About Gene Expression Changes From Microarray Data,” Journal of Computational Biology, 8, 37–52.

Ramoni MF, Sebastiani P, Kohane IS (2002), “Cluster analysis of gene expression dynamics”, Proceedings National Academy of Science, 99(14), 9121-9126.

Serban,N. and Wasserman, L. (2005), “CATS: clustering after transformation and
Smoothing”, Journal of the American Statistical Association, 471, 611-631.

Tusher, V. G., Chu, G. and Tibshirani, R. (2001), "Significance analysis of microarrays applied to the ionizing radiation response", Proceedings National Academy of Science, 98, 5116-5121.

Wakefield, J., Zhou, C. and Self, S. (2003), “Modelling gene expression data over time: curve clustering with informative prior distributions”, Bayesian Statistics 7, 721–732.
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