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研究生:何孟如
研究生(外文):Meng-Ru Ho
論文名稱:關於微陣列資料的前景與背景之變異數分析模型
論文名稱(外文):Foreground and Background ANOVA Models for Microarray Data
指導教授:盧鴻興盧鴻興引用關係
指導教授(外文):Henry Horng-Shing Lu
學位類別:碩士
校院名稱:國立交通大學
系所名稱:統計所
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:91
語文別:英文
論文頁數:34
中文關鍵詞:微陣列前景變異數分析模型背景變異數分析模型加權最小平方法排列檢定拔靴法
外文關鍵詞:MicroarrayForeground ANOVA modelBackground ANOVA modelWeighted least squares estimationPermutation testBootstrap
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微陣列 (microarray) 技術是一種能偵測訊息核糖核酸 (mRNA, messenger ribosnucleic acid) 不同表現的有利工具。因其能提供大量的資料,此技術在最近被廣泛的利用在生物與醫藥應用方面,但微陣列在生物上、實驗系統與隨機的變化使得分析微陣列的資料具有很大的挑戰性。本篇論文提出前景與背景之變異數分析模型 (foreground and background ANOVA models) 來分析微陣列資料中的生物、實驗與隨機的影響,利用加權最小平方法 (Weighted least squares estimation) 估計模型中的參數,並且採用重抽樣的方法,例如: 排列檢定(permutation tests)與拔靴法 (boostrap) ,得到信賴區間與顯著機率值(p-values)。本篇的結果與即時聚合鏈反應 (Real Time PCR) 和其他相關微陣列實驗的結果作比較。

Spotted cDNA microarray (i.e., microscopic array) is a powerful tool to detect differential expressed genes in mRNA. Because of its capacity of high throughput, it has been widely used in biological and medical applications recently. Due to the biological, systematic and random variations of microarrays, it is a big challenge to analyze microarray data. In this paper we propose the foreground and background ANOVA models to explore the effects of biological, systematic and random variations from the microarray data. Weighted least squares estimation by the approach of coordinatewise descent is proposed to estimate the parameters. The confidence intervals and p-values are obtained by the resampling methods, including permutation tests and bootstrap methods. The results are compared with the findings by Real Time PCR and other microarray experiments.

Contents
Chapter 1. Introduction (1)
Chapter 2. Methodologies (4)
2.1 ANOVA Models (4)
2.2 Weighted-Least-Squares Estimation (7)
2.3 Filter Unreliable Spots (11)
2.4 Permutation or Bootstrap Tests (12)
Chapter 3. Empirical Studies (18)
3.1 Experiment Designs (18)
3.2 The Designs of Probes in One Array (18)
3.3 Effects in Arrays (20)
3.4 Confounded Variables (22)
3.5 Parameter Size vs. Sample Size (22)
3.6 RT-PCR Verified Genes (22)
3.7 Other Candidates Genes (23)
3.8 Housekeeping Genes (23)
3.9 Empirical Results (24)
3.10 The Screening Procedure in Lee’s Lab (27)
Chapter 4. Discussions and Further Works (28)
4.1 Model Comparison (28)
4.1 Flexibility and Limitation (28)
4.1 Future Studies (30)
References (31)

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