博碩士論文 992213005 詳細資訊

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姓名 陳世元(Shih-yuan Chen)  查詢紙本館藏   畢業系所 系統生物與生物資訊研究所
論文名稱 一個檢定測量微晶片基因表達數據靈敏度的全統計計算法
(An all-statistics method for assessing the sensitivity in the measurement of microarray gene expression data)
★ 人類陰道滴蟲之Myb2蛋白質動態性質研究★ 分析原核生物基因體複製起點與終點的反向對偶對稱現象
★ 分析基因體拷貝數變異所使用的兩種方法比較:隱藏馬可夫模型與成對高斯合併法★ 使用兩種方法偵測基因體拷貝數變異:成對高斯合併法與隱藏馬可夫模型
★ 以整體晶片數據為母體應用於分析基因差異表達的z檢定方法★ GSLHC - 運用基因組及層次類聚以生物功能群將有生物活性的複合物定性的方法
★ 運用嶄新抗體固著策略發展及驗證新式抗體微晶片平台★ Drug-resistant colon cancer cells produce high carcinoembryonic antigen and might not be cancer-initiating cells
★ 創傷性關節炎軟骨之退化進程- 大鼠模型基因體圖譜研究★ 運用時間序列微陣列資料來預測調控基因
★ 以大鼠嗜鉻性瘤細胞株建立神經訊號傳遞之細胞分子生物學模型★ 一種找尋再利用藥物複合物來系統性治療複雜疾病的架構:大腸直腸腺瘤的應用
★ 以上皮細胞間質化與增生相關功能來描述癌症幹細胞之基因型★ 從共表達差異基因對導出正常腦老化及因阿茲海默症特定腦區導致在功能性基因途徑與樞紐基因子網絡之變化
★ 以疾病進展趨勢挑選基因法識別正常腦老化與阿爾茨海默氏症在特定腦區引發的關鍵功能路徑與調節路徑之變化★ 中國傳統醫藥蒙古黃耆在HCT116結腸癌細胞體外和體內實驗呈現腫瘤抑制作用
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摘要(中) 在生物醫學領域研究中,微陣列晶片已成為測量基因表現的重要工具。 然而使用晶片測量基因表現一直以來都有低再現性的問題。 雖然造成這樣的原因仍然未被完全的確定,然而平台的設計問題已被MicroArray Quality Control計畫使用大尺度研究排除在外。在這樣的晶片測量中,樣本的雜交前處理包含培養、加藥處理、晶片平台特定樣本製備等皆會引入生物性誤差,而晶片平台固有的隨機誤差則是技術性誤差,而此兩種誤差是混合在測量中難以量化分離。 越來越多的證據顯示生物性誤差是誤差的主要成分,但是缺乏一個評估生物性誤差的方法,使得實驗者不斷懷疑數據的可靠性。 在這裡我們發展出一個評估樣本生物性誤差及晶片組敏感性的方法來解決此問題,這個方法是全新的,用一個全統計的方式來計算且不需要正歸化晶片訊號。我們使用此方法研究350組公開的晶片資料集的生物性誤差,我們發現生物性誤差是晶片誤差的主要來源,而我們的結果顯示有一部份的晶片組靈敏度都是低的,這或許可以解釋為什麼研究相同疾病卻有高度不相似的差異表現基因清單。 這樣的結果也指出如果不在樣本處理上有改善,再現性的問題也仍然會出現在次世代定序等未來科技的測量上。
摘要(英) Measurement of gene expression using microarray has been an extremely important research tool in biology and medicine. However, poor reproducibility of array-based results remains a long-standing issue. Although the cause for the problem has not been firmly identified, platform design and test site have been ruled out in a large-scale study by the MicroArray Quality Control project. In such measurements, prehybridization error (biological variance, or BV) introduced during sample processing (e.g. culture and treatment) and platform-specific sample preparation, and inherent random error of the technology (technical variance, or TV) are coupled and difficult to quantify separately. Increasing evidence points to BV as the primary cause but lack of a method for assessing BV keeps the experimentalist in constant doubt of data reliability. Here, we developed a procedure, Measuring Improper Sample Handling (MISH), as a solution for the problem and produced a computer package for its implementation. MISH is novel, all-statistics procedure and does not require normalization. For demonstration, we applied MISH to study the BV in 350 public data sets. Part of the result may be taken as a characterization of BV of the Affymetrix GeneChip Human Genome U133 Plus 2.0 Array platform. We found that BV was the dominant error in the data sets studied and that, for data sets from biological replicates, sample processing introduced the most error. Our analysis showed that a large number of public cohort data sets had low sensitivity on contrasts, which may well explain why studies on same diseases yielded highly dissimilar lists of DEGs. This suggests that the reproducibility issue will remain a concern for measurements based on next-generation sequencing, and on any future technology that does not focus on improvement in sample processing.
關鍵字(中) ★ 全統計計算
★ 基因表達
★ 微陣列晶片
★ 靈敏度
關鍵字(英) ★ all-statistics method
★ gene expression
★ microarray
★ sensitivity
論文目次 中文摘要 i
Abstract ii
致謝 iii
Table of contents iv
List of Figures v
List of Tables v
Chapter 1 Introduction 1
Chapter 2 Material and methods 2
2.1 Materials 2
2.2 Methods 3
2.2.1 Calculating TV for the platform 3
2.2.2 Calculating BV between two arrays 4
2.2.3 Calculating BV of an array 4
2.2.4 Simulating an array 5
2.2.5 Simulating a group of replicate arrays 5
2.2.6 Estimating sensitivity and reproducibility 6
Chapter 3 Results 7
3.1 The attributes of 748 contrasts of array groups from the 350 public data sets 7
3.2 σBV of 748 contrasts of array groups 7
3.3 Sensitivity of 748 contrasts of array groups 8
3.4 Reproducibility of 748 contrasts of array groups 9
3.5 Summary 14
Chapter 4 Discussion 15
Reference 16
Appendix 18
參考文獻 1. Schena, M., et al., Quantitative monitoring of gene expression patterns with a complementary DNA microarray. Science, 1995. 270(5235): p. 467-70.
2. Tan, P.K., et al., Evaluation of gene expression measurements from commercial microarray platforms. Nucleic Acids Res, 2003. 31(19): p. 5676-84.
3. Ramalho-Santos, M., et al., "Stemness": transcriptional profiling of embryonic and adult stem cells. Science, 2002. 298(5593): p. 597-600.
4. Ivanova, N.B., et al., A stem cell molecular signature. Science, 2002. 298(5593): p. 601-4.
5. Miller, R.M., et al., Dysregulation of gene expression in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine-lesioned mouse substantia nigra. J Neurosci, 2004. 24(34): p. 7445-54.
6. Fortunel, N.O., et al., Comment on " ’’Stemness’’: transcriptional profiling of embryonic and adult stem cells" and "a stem cell molecular signature". Science, 2003. 302(5644): p. 393; author reply 393.
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9. Marshall, E., Getting the noise out of gene arrays. Science, 2004. 306(5696): p. 630-1.
10. Michiels, S., S. Koscielny, and C. Hill, Prediction of cancer outcome with microarrays: a multiple random validation strategy. Lancet, 2005. 365(9458): p. 488-92.
11. Ein-Dor, L., O. Zuk, and E. Domany, Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer. Proc Natl Acad Sci U S A, 2006. 103(15): p. 5923-8.
12. Shi, L., et al., The MicroArray Quality Control (MAQC) project shows inter- and intraplatform reproducibility of gene expression measurements. Nat Biotechnol, 2006. 24(9): p. 1151-61.
13. Shi, L., et al., Cross-platform comparability of microarray technology: intra-platform consistency and appropriate data analysis procedures are essential. BMC Bioinformatics, 2005. 6 Suppl 2: p. S12.
14. Guo, L., et al., Rat toxicogenomic study reveals analytical consistency across microarray platforms. Nat Biotechnol, 2006. 24(9): p. 1162-9.
指導教授 李弘謙(Hoong-chien Lee) 審核日期 2012-7-27
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