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研究生:戴郁佳
論文名稱:運用統計方法與微陣列技術探討放射線照射後之未同步化腦腫瘤細胞內基因表現
論文名稱(外文):Statistical Analysis of the Gene Expression for Non-synchronized Cell Cycles of Human Glioma Cells after Gamma Irradiation by cDNA Microarray
指導教授:盧鴻興盧鴻興引用關係
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:2002
畢業學年度:90
語文別:英文
論文頁數:63
中文關鍵詞:微陣列技術非同步化
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微陣列技術是一種高生產量的技術,利用這種新技術我們可以在很短的時間裡觀察到大量基因的表現情形。為了探討放射線照射後的腦腫瘤細胞內基因表現情形,我們根據陽明大學吳國海教授實驗室所提供的微陣列實驗資料,進行統計分析。此實驗為了模擬真實活體中基因的表現情形,並未進行同步化的步驟。另一方面,非同步化的實驗也可節省實驗的經費與時間。然而,非同步化的資料卻形成分析上的挑戰。本篇論文嘗試利用統計方法,分析微陣列技術與細胞流速測量之資料,估計基因在生物細胞週期中的真實表現量,藉此區隔非同步化的影響。

Microarray is a high throughput technique. We can observe a large number of gene expressions by this new technique in a short time. In order to understand the gene expression profiles in different phases of cell cycles of human glioma cells after gamma irradiation, microarray experiments are performed in Professor Ngo’s laboratory at National Yang-Ming University (Ngo, Chan, Chang, 2001). The experiments are non-synchronized to imitate the in vivo expression pattern. Furthermore, these will provide fast and economic approaches to screen the gene functions. However, these data bring us challenges in analysis. This study is an attempt to separate the effect of non-synchronization from the genuine cell cycle function by statistical methods for microarray data with cell proportions measured by flow cytometry.

1. Abstract
2. Introduction
3. Data Description and Scientific Aims
4. Methodologies
a. Assumptions of Genuine Expression Profiles
b. Linear and Nonlinear Models
c. Sliced Inverse Regression
d. Permutation Tests for ANOVA
e. Clustering Methods
5. Normalization
6. Simulation Studies
7. Empirical Results
8. Conclusions and Discussions
9. References

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