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  1. NTU Theses and Dissertations Repository
  2. 生物資源暨農學院
  3. 農藝學系
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33475
完整後設資料紀錄
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dc.contributor.advisor劉仁沛(Jen-Pei Liu)
dc.contributor.authorJia-Yan Daien
dc.contributor.author戴嘉彥zh_TW
dc.date.accessioned2021-06-13T04:42:37Z-
dc.date.available2006-07-21
dc.date.copyright2006-07-21
dc.date.issued2006
dc.date.submitted2006-07-17
dc.identifier.citation[1] Affymetrix Technical Note 2, “Fine tuning your data analysis: tunable parameters of the Affymetrix® Expression analysis statistical algorithms.” Part No. 701138 Rev 2, 2001.
[2] 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.
[3] Benjamini, Y. and Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. J. Roy. Stati. Soc., Ser. B, 57:289-300.
[4] Chow, S.C. and Liu, J.P.. (1995) Design and analysis of bioavailability and bioequivalence studies. New York: Marcel Dekker , Inc.
[5] 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.
[6] 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.
[7] Holy, D.C., Rattray, M., Jupp, R. and Brass, A.. (2002) Making sense of microarray data distributions. Bioinformatics, 18, 576-584.
[8] 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.
[9] 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.
[10] Rocke, D.M. and Durbin, B.. (2001) A model for measurement error for gene expression arrays. J. Comput. Biol., 8, 557-569.
[11] 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.
[12] Tsai, C.A, Chen, Y.J. and Chen, J.J.. (2003) Testing for differentially expressed genes with microarray data. Nucl. Acids Res., 31, e52.
[13] Wang, S. and Ethier, S.. (2004) A generalized likelihood ratio test to identify differentially expressed genes from microarray data. Bioinformatics, 20, 100-104.
[14] Yang, Y.H., Dudoit, S., Luu, P., Lin, D.M., Peng, V., Ngai, J. and Speed, T.P.. (2002a) Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation. Nucleic Acids Res., 30, 4:e15.
[15] Yang, Y.H., Buckley, M.J., Dudoit, S. and Speed, T.P.. (2002b) Comparison of methods for image analysis on cDNA microarray data. J. Comput. Graph. Stat., 11:108-136.
[16] Yang, Y.H., Buckley, M.J., Dudoit, S. and Speed, T.P.. (2001) Analysis of cDNA microarray images. Brief. Bioinf., 2:341-349.
dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/33475-
dc.description.abstract對於鑑定基因是否有顯著表現,現今的方法大多是使用傳統的假設檢定,檢定兩組樣本差異是否等於零。然而傳統的假設檢定並沒有考慮到具有生物意義的倍數變化,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。在微陣列實驗中,由於基因數通常很大且重複數通常很少,所以在檢定基因是否有顯著表現時整體型一錯誤會變的很大,必須使用不同的方法去修正,以期控制整體型一錯誤,例如: Bonferroni的方法、錯誤發現率或著是使用任意的閥值。但這些方法依然沒有考慮到生物準則。因此,我們提出一個考慮生物意義的區間假設檢定,並且提出統計程序及樣本數決定方式,同時也探討了此方法的一些統計特性。比較傳統假設檢定、區間假設檢定等五種方法,以模擬的方式得到經驗的整體型一錯誤、平均型一錯誤以及檢定力模擬結果顯示,區間假設可以有效的控制平均型I 誤差在名目水準之下,且整體型一誤差也比較低,檢定力相較於使用Bonferroni修正來的好。zh_TW
dc.description.abstractCurrent statistical approaches to identifying differentially expressed genes are based on tradition hypotheses of equality. However, traditional hypothesis of equality fail to take into consideration the magnitudes of the biologically meaningful fold changes that truly differentiate the expression levels of genes between groups. Due to the large number of genes tested and small number of specimens available for microarray experiments, the false positive rate for differentially expressed genes is extremely high and requires many different adjustments such as Bonferroni’s method, false discovery rate, or use of an arbitrary cutoff for the p-values. All these adjustments do not have any biological justification. Hence, we propose to use the interval hypotheses by consideration of the minimal biologically meaningful expression levels for identification of differentially expressed genes. Based on the interval hypothesis, statistical procedures were proposed and the methods for sample size determination are also given. Statistical properties of the proposed procedures are investigated. A large simulation study was conducted to empirically compare the overall type I error, average type I error and power of the traditional hypothesis using unpaired two-sample t-test, the traditional hypothesis using the unpaired two-sample t-test with Bonferroni adjustment, the fixed fold-change rule, the method of combination of the traditional hypothesis using unpaired two-sample t-test and fixed fold-change rule, and the proposed interval under various combinations of fold changes, variability and sample sizes. Simulation results show that the proposed procedures based on the interval hypothesis not only can control the average type I error rate at the nominal level but also provide sufficient power to detect differentially expressed gene. Numeric data from public domains illustrate the proposed methods.en
dc.description.provenanceMade available in DSpace on 2021-06-13T04:42:37Z (GMT). No. of bitstreams: 1
ntu-95-R93621209-1.pdf: 2052183 bytes, checksum: 19604d6fa48f2fbdb36a71a8f3827fd6 (MD5)
Previous issue date: 2006
en
dc.description.tableofcontents摘要 I
ABSTRACT II
誌謝 III
CONTENTS IV
LIST OF TABLES VI
LIST OF FIGURES XVIII
CHAPTER 1 INTRODUCTION 1
CHAPTER 2 LITERATURE REVIEW 3
2.1 TYPE I AND TYPE II ERRORS 3
2.2 OVERALL AND AVERAGE TYPE I ERROR 4
2.3 THE INTENSITY DATA FROM MICROARRAY EXPERIMENTS 5
2.3.1 Statistical Models for Background-subtracted Raw Intensity Data 5
2.3.2 Statistical Models for Log-transformed Data 7
2.4 THE CURRENT PROCEDURES 8
2.4.1 The Unpaired Two-sample t-test 8
2.4.2 The Unpaired Two-sample t-test with Bonferroni Adjustment 9
2.4.3 The Fixed Fold-change Rule 9
2.4.4 Combination of the Unpaired Two-sample t-test and Fixed Fold-change Rule 9
CHAPTER 3 PROPOSED METHODS 11
3.1 THE INTERVAL HYPOTHESIS 11
3.2 TWO ONE-SIDES TESTS PROCEDURE 12
3.3 POWER AND SIZE OF TESTS 13
3.4 POWER OF TWO ONE-SIDED TESTS PROCEDURE 13
3.5 SAMPLE SIZE DETERMINATION 17
3.6 COMPARISON WITH THE COMBINATION OF THE UNPAIRED TWO-SAMPLE T-TEST AND FIXED FOLD-CHANGE RULE AND THE INTERVAL HYPOTHESIS 18
3.7 THE EMPIRICAL OVERALL AND AVERAGE TYPE I ERROR RATE AND AVERAGE POWER 18
CHAPTER 4 SIMULATION STUDY 20
4.1 PARAMETER COMBINATIONS AND SIMULATION PROCESS 20
4.1.1 Parameter Combination 20
4.1.2 Simulation Process 20
4.2 SIMULATION RESULTS 23
4.2.1 Summary 24
CHAPTER 5 EXAMPLE 28
5.1 THE TRADITIONAL HYPOTHESIS USING UNPAIRED TWO-SAMPLE T-TEST 29
5.2 THE TRADITIONAL HYPOTHESIS USING UNPAIRED TWO-SAMPLE T-TEST WITH BONFERRONI ADJUSTMENT 30
5.3 THE FOLD-CHANGE RULE 31
5.4 THE METHOD OF COMBINATION OF THE TRADITIONAL HYPOTHESIS USING TWO-SAMPLE T-TEST AND FOLD-CHANGE RULE 32
5.5 THE PROPOSED INTERVAL HYPOTHESIS 33
CHAPTER 6 CONCLUSION AND DISCUSSION 34
REFERENCES 35
APPENDIX A 37
APPENDIX B 38
APPENDIX C 41
dc.language.isoen
dc.title微陣列實驗中檢測不同表現基因之統計方法評估zh_TW
dc.titleEvaluation of Statistical Methods for Identification of Differentially Expreseed Genes in Microarray Experimentsen
dc.typeThesis
dc.date.schoolyear94-2
dc.description.degree碩士
dc.contributor.oralexamcommittee廖振鐸(Chen-Tuo Liao),蕭金福(Chin-Fu Hsiao)
dc.subject.keyword區間假設檢定,整體型一錯誤,平均型一錯誤,檢定力,倍數變化,zh_TW
dc.subject.keywordInterval hypothesis,Type I error,Power,Fold change,en
dc.relation.page89
dc.rights.note有償授權
dc.date.accepted2006-07-18
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
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