請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29525完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉仁沛(Jen-Pei Liu) | |
| dc.contributor.author | Shih-Ting Chiu | en |
| dc.contributor.author | 邱詩婷 | zh_TW |
| dc.date.accessioned | 2021-06-13T01:09:25Z | - |
| dc.date.available | 2007-07-26 | |
| dc.date.copyright | 2007-07-26 | |
| dc.date.issued | 2007 | |
| dc.date.submitted | 2007-07-19 | |
| dc.identifier.citation | 彭國倫，FORTRAN 95 程式設計，2002，台北：碁峰資訊股份有限公司
Affymetrix Technical Note 2, “Fine tuning your data analysis: tunable parameters of the Affymetrix® Expression analysis statistical algorithms.” Part No. 701138 Rev 2, 2001. Benjamini, Y and Hochberg, Y. (1995) Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B (Methodological) 57 (1): 289–300. Black, M.A. and Doerge, R.W.. (2001) Calculation of the minimum number of replicate spots required for detection of significant gene expression fold change in microarray experiments. Technical Report. Department of Statistics, Purdue University. Chen, Y., Dougherty, E.R. and Bittner, M.L.. (1997) Ratio-based decisions and the quantitative analysis of cDNA microarray images. J Biomed. Opt., 2, 364-374. DeRisi, J.L., Penland, L., Brown, M.L., Meltzer, P.S., Ray, M., Chen, Y., Su, Y.A., and Trent, J.M. 1996. Use of a cDNA microarray to analysis gene expression pattern in human cancer. Nat. Genet. 14: 457-460. Dudoit, S., Yang, Y.H., Callow, M.J. and Speed, T.P.. (2002) Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica,12, 111-139. Dai J.Y. (2006) Evaluation of Statistical Methods for Identification of Differentially Expressed Genes in Microarray Experiment., Master thesis, Taiwan University Efron B. (1979). 'Bootstrap Methods: Another Look at the Jackknife'. The Annals of Statistics 7 (1): 1–26. Holy, D.C., Rattray, M., Jupp, R. and Brass, A.. (2002) Making sense of microarray data distributions. Bioinformatics, 18, 576-584. Ideker, T., Thorsson, V., Siegel, A.F. and Hood, L.E.. (2000) Testing for differentially expressed genes by maximum-likelihood analysis of microarray data. J Comput. Biol., 7, 805-817. IMSL Library IMSL Library, (1990-2000),Visual Numerics. Inc.,Visual Numerics. Inc.:5.1.2600.2180 Luo, J., Duggan, D.J., Chen, Y., Sauvageot, J., Ewing, C.M., Bittner, M.L., Trent, J.M. and Isaaxs, W.B.. (2001) Human prostate cancer and benign prostatic hyperplasia: molecular dissection by gene expression profiling. Cancer Res., 61, 4683-4688. Rocke, D.M. and Durbin, B.. (2001) A model for measurement error for gene expression arrays. J. Comput. Biol., 8, 557-569. Simon, R.M., Korn, E.L., McShane, L.M., Radmacher, M.D., Wright, G.W. and Zhao, Y.. (2003) Design and Analysis of DNA Mircoarray Investigations. New York: Springer. Tusher V.G., Tibshirani R., and Chu G.（2001）Significance analysis of microarrays applied to the ionizing radiation response. Proc. Nat. Acad. Sci. USA 98:5116-5121. Tsai, C.A, Chen, Y.J. and Chen, J.J.. (2003) Testing for differentially expressed genes with microarray data. Nucl. Acids Res., 31, e52. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/29525 | - |
| dc.description.abstract | 傳統的假設檢定法利用檢定兩組樣本差異是否等於零來鑑定基因是否有顯著表現，但卻沒有考慮到具有生物意義的倍數變化量，然而在生物領域中基因表現的倍數變化超過某些定值即認定該基因是有表現的。相較於傳統的假設檢定法，2006年由戴家彥碩士論文提出一個區間假設的雙單尾檢定法，此方法不僅能考慮到生物意義亦能更準確地鑑別出有顯著表現的基因。在此我們將進一步將區間假設的雙單尾檢定法推展到無母數領域，以區間假設為基礎，應用多變量排列作出可以偵測基因表現值最小變化量的非介量檢定方法，探討其決策程序、整體型一錯誤、平均檢定力以及型一誤差。
模擬結果顯示在足夠的陣列重複數之下，區間假設檢定方法相較於其它傳統假設檢定方法，不僅整體與平均型一錯誤較低，檢定力亦比傳統的單尾檢定方法來得好，而非介量的多元排列檢定法能進ㄧ步改善這種區間假設檢定。 | zh_TW |
| dc.description.abstract | The traditional hypothesis for identification of differentially expressed genes fails to take the biological meaning fold changes into consideration. However, a gene is differentially expressed if its fold change exceeds a threshold value in biological field. Compared with the traditional hypothesis of equality, the two one-sided tests procedure based on interval hypothesis（Liu, et al, 2007）not only consider the minimal biologically meaningful expression but truly identify the differentially expressed genes. To continue the research, we will apply multivariate permutation test to the interval hypothesis. Based on this proposed method, we conduct a simulation study to investigate its power, overall type I error and average type I error when the normal assumption of expression levels is in doubt.
The simulation results indicate that because of lower overall type I error and average type I error and higher average power, the interval hypothesis works better than the traditional hypothesis of equality when there are enough replicates in array. And the multivariate permutation test which is a non-parametric approach could improve the ability of identifying gene expression with interval hypothesis. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-13T01:09:25Z (GMT). No. of bitstreams: 1 ntu-96-R94621204-1.pdf: 677903 bytes, checksum: 8a60750c380e2df18d95096f10ff12df (MD5) Previous issue date: 2007 | en |
| dc.description.tableofcontents | 論文口試委員審定書 I
謝辭 II 摘要 III ABSTRACT IV CONTENTS V LIST OF FIGURES VII LIST OF TABLES VIII CHAPTER 1 INTRODUCTION 1 CHAPTER 2 CURRENT METHODS 3 2.1 THE TRADITIONAL AND THE INTERVAL HYPOTHESIS 3 2.1.1 The Traditional Hypothesis of Equality 3 2.1.2 The Interval Hypothesis 4 2.2 THE CURRENT PROCEDURES WITH THE TRADITIONAL HYPOTHESIS 5 2.2.1 Unpaired Two-sample t-test 5 2.2.2 Unpaired Two-sample t-test with Bonferroni Adjustment 6 2.2.3 The Fixed Fold-change Rule 6 2.2.4 Combination of the Unpaired Two-sample t-test and Fold-changes Rule 7 2.3 THE TWO ONE-SIDED TESTS PROCEDURE BASED ON INTERVAL HYPOTHESIS 7 CHAPTER 3 MULTIVARIATE PERMUTATION METHOD 9 3.1 PERMUTATION TEST 9 3.1.1 Permutation t-test 9 3.1.2 Multivariate Permutation Test 10 3.2 INTERVAL HYPOTHESIS BASED ON MULTIVARIATE PERMUTATION TEST 12 CHAPTER 4 SIMULATION 15 4.1 STATISTICAL MODELS TO GENERATING MICROARRAY EXPERIMENT DATA 15 4.1.1 Statistical Models for Generating Background-subtracted Raw Intensity Data 15 4.1.2 Statistical Models for Normalized Log-transformed Data 17 4.2 THE EMPIRICAL OVERALL AND AVERAGE TYPE I ERROR AND AVERAGE POWER 18 4.3 SIMULATION PROCEDURE 22 4.3.1 Multivariate Permutation Process 23 4.3.2 Parameter Combinations 25 4.4 SIMULATION RESULTS 26 4.4.1 Comparison between Different Size of Arrays 26 4.4.2 Results by Different Methods 27 4.4.3 Result from Different Settings of Multiplicative and Additive Error 28 4.4.4 Compare the Result from Four Different Models 28 CHAPTER 5 EXAMPLE 31 CHAPTER 6 DISCUSSION AND CONCLUSION 38 | |
| dc.language.iso | en | |
| dc.subject | 倍數變化 | zh_TW |
| dc.subject | 區間假設檢定 | zh_TW |
| dc.subject | 雙單尾檢定法 | zh_TW |
| dc.subject | 多元排列檢定法 | zh_TW |
| dc.subject | 整體型一錯誤 | zh_TW |
| dc.subject | 平均型一錯誤 | zh_TW |
| dc.subject | 檢定力 | zh_TW |
| dc.subject | Fold change | en |
| dc.subject | Overall and Average Type I error | en |
| dc.subject | Power | en |
| dc.subject | Multivariate permutation | en |
| dc.subject | Two one-sided tests | en |
| dc.subject | Interval hypothesis | en |
| dc.title | 偵測基因表現值最小變化量的多變量排列方法之研究 | zh_TW |
| dc.title | A Study on the Multivariate Permutation Test to Detect the Minimal Fold Changes of Gene Expression Levels | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 95-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖振鐸(Chen-Tuo Liao),季瑋珠(Wei-Chu Chie) | |
| dc.subject.keyword | 倍數變化,區間假設檢定,雙單尾檢定法,多元排列檢定法,整體型一錯誤,平均型一錯誤,檢定力, | zh_TW |
| dc.subject.keyword | Fold change,Interval hypothesis,Two one-sided tests,Multivariate permutation,Power,Overall and Average Type I error, | en |
| dc.relation.page | 82 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2007-07-23 | |
| dc.contributor.author-college | 生物資源暨農學院 | zh_TW |
| dc.contributor.author-dept | 農藝學研究所 | zh_TW |
| 顯示於系所單位： | 農藝學系 | |
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