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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 李文宗(Wen-Chung Lee) | |
dc.contributor.author | Yi-Ting Lin | en |
dc.contributor.author | 林奕廷 | zh_TW |
dc.date.accessioned | 2021-06-16T10:18:59Z | - |
dc.date.available | 2018-09-24 | |
dc.date.copyright | 2013-09-24 | |
dc.date.issued | 2013 | |
dc.date.submitted | 2013-08-16 | |
dc.identifier.citation | 1. Pounds, S.B., Estimation and control of multiple testing error rates for microarray studies. Brief Bioinform, 2006. 7(1): p. 25-36.
2. Benjamini, Y. and Y. Hochberg, Controlling the False Discovery Rate - a Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society Series B-Methodological, 1995. 57(1): p. 289-300. 3. Storey, J.D. and R. Tibshirani, Statistical significance for genomewide studies. Proc Natl Acad Sci U S A, 2003. 100(16): p. 9440-5. 4. Alon, U., et al., Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proc Natl Acad Sci U S A, 1999. 96(12): p. 6745-50. 5. Stambolian, D., et al., Meta-analysis of genome-wide association studies in five cohorts reveals common variants in RBFOX1, a regulator of tissue-specific splicing, associated with refractive error. Hum Mol Genet, 2013. 22(13): p. 2754-64. 6. Efron, B., Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. Journal of the American Statistical Association, 2004. 99: p. 96-104. 7. Liao, J.G., et al., A mixture model for estimating the local false discovery rate in DNA microarray analysis. Bioinformatics, 2004. 20(16): p. 2694-701. 8. Scheid, S. and R. Spang, A stochastic downhill search algorithm for estimating the local false discovery rate. IEEE/ACM Trans Comput Biol Bioinform, 2004. 1(3): p. 98-108. 9. Strimmer, K., A unified approach to false discovery rate estimation. BMC Bioinformatics, 2008. 9: p. 303. 10. Efron, B. and R. Tibshirani, Empirical bayes methods and false discovery rates for microarrays. Genet Epidemiol, 2002. 23(1): p. 70-86. 11. Strimmer, K., fdrtool: a versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics, 2008. 24(12): p. 1461-2. 12. Klaus, B. and K. Strimmer, fdrtool: Estimation of (Local) False Discovery Rates and Higher Criticism. R package version 1.2.10., 2012. 13. Green, G.H. and P.J. Diggle, On the operational characteristics of the Benjamini and Hochberg False Discovery Rate procedure. Stat Appl Genet Mol Biol, 2007. 6: p. Article27. 14. Zhang, J. and K.R. Coombes, Sources of variation in false discovery rate estimation include sample size, correlation, and inherent differences between groups. BMC Bioinformatics, 2012. 13 Suppl 13: p. S1. 15. Gold, D.L., J.C. Miecznikowski, and S. Liu, Error control variability in pathway-based microarray analysis. Bioinformatics, 2009. 25(17): p. 2216-21. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/60468 | - |
dc.description.abstract | 在進行DNA微陣列基因晶片資料分析時,由於檢定次數龐大,會遇到多重檢定問題。控制錯誤發現率是一個新的思維,可以有較Bonferroni校正更佳的檢定力。然而研究者忽略了錯誤發現率控制方法有三個層次的變異,進而做出錯誤的闡釋。本研究中,我們利用拔靴法估計局部錯誤發現率、錯誤發現率控制數值與偽陽性個數比例等三個層次的變異。作者使用大腸癌基因表現實際資料與眼屈光不正GWAS實際資料,呈現三個層次變異大小。兩筆真實資料皆顯示明顯的變異。在顯著的基因中,局部錯誤發現率、q-值與偽陽性個數比例的拔靴法標準誤最大分別可達0.0891、0.0273與0.0828。我們呼籲在使用錯誤發現率控制方法時,不應只列出錯誤發現率控制數值或q-值,還需呈現錯誤發現率控制方法有三個層次的變異,才可確保每個部分都會有正確的闡釋。 | zh_TW |
dc.description.abstract | The problem of multiple hypothesis testing arises during the analysis of DNA microarray data owing to numerous statistical tests. Controlling the false discovery rate (FDR) is a revolutionary idea with higher power than traditional error control procedures. However, there are three levels of variation in FDR control, which result in incorrect interpretations if ignored by investigators. In this study, we estimate the variation of local FDR, FDR control value, and false discovery proportion by bootstrap. A colon cancer gene-expression data and a visual refractive errors GWAS data will be analyzed as demonstration. Each data shows apparent variation. Among significant genes, the largest bootstrap standard error of local FDR, overall q-value and false discovery proportion are 0.0891, 0.0273 and 0.0828 respectively. Therefore, we urge the importance of reporting three levels of variation in FDR control to ensure correct interpretations. | en |
dc.description.provenance | Made available in DSpace on 2021-06-16T10:18:59Z (GMT). No. of bitstreams: 1 ntu-102-R00849008-1.pdf: 476320 bytes, checksum: ed23aefa4003e12214b6dc371f91151f (MD5) Previous issue date: 2013 | en |
dc.description.tableofcontents | 口試委員審定書........I
摘要................II ABSTRACT..........III 前言................1 方法................2 實例................4 討論................6 參考文獻.............7 附錄1:電腦模擬.......11 附錄2:電腦程式.......14 | |
dc.language.iso | zh-TW | |
dc.title | 錯誤發現率控制方法變異之量度 | zh_TW |
dc.title | Characterizing the Variability of False Discovery Rate Control | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 蔡政安(Chen-An Tsai),林菀俞(Wan-Yu Lin) | |
dc.subject.keyword | 錯誤發現率控制方法,拔靴法,變異,局部錯誤發現率,偽陽性個數比例, | zh_TW |
dc.subject.keyword | false discovery rate control,bootstrap,variation,local FDR,false discovery proportion, | en |
dc.relation.page | 15 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2013-08-16 | |
dc.contributor.author-college | 公共衛生學院 | zh_TW |
dc.contributor.author-dept | 流行病學與預防醫學研究所 | zh_TW |
顯示於系所單位: | 流行病學與預防醫學研究所 |
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