請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57888完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 劉力瑜 | |
| dc.contributor.author | Hsin-Ying Tsai | en |
| dc.contributor.author | 蔡欣穎 | zh_TW |
| dc.date.accessioned | 2021-06-16T07:09:43Z | - |
| dc.date.available | 2014-07-11 | |
| dc.date.copyright | 2014-07-11 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-07-08 | |
| dc.identifier.citation | Ackermann, M. and Strimmer, K. (2009) A general modular framework for gene set enrichment analysis. BMC Bioinformatics, 10, 47.
Bair, E. (2013) Identification of significant features in DNA microarray data. Wiley Interdiscip. Rev. Comput. Stat., 5, 309–325. Beisbarth, T. and Speed, T.P. (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics, 20, 1464–1465. Beisvag, V. et al. (2006) GeneTools – application for functional annotation and statistical hypothesis testing. BMC Bioinformatics, 7, 470. Van Belle, G. et al. (2004a) Categorical data: contingency tables. In, Biostatistics. John Wiley & Sons, Inc., Hoboken, NJ, USA., pp. 208–252. Van Belle, G. et al. (2004b) One- and Two-Sample Inference. In, Biostatistics. John Wiley & Sons, Inc., Hoboken, NJ, USA., pp. 117–150. Chen, M. et al. (2013) A powerful Bayesian meta-analysis method to integrate multiple gene set enrichment studies. Bioinformatics, 29, 862–869. Consortium, G.O. (2004) The Gene Ontology (GO) database and informatics resource. Nucleic Acids Res., 32, D258–D261. Daniel P. Berrar et al. (2003) A practical approach to microarray data analysis Kluwer Academic Publishers, Dordrecht. Eisen, M.B. et al. (1998) Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. U. S. A., 95, 14863–14868. Goeman, J.J. et al. (2004) A global test for groups of genes: testing association with a clinical outcome. Bioinformatics, 20, 93–99. Hollander, M. et al. (2013) Nonparametric statistical methods John Wiley & Sons, Hoboken, NJ, USA. Joshi-Tope, G. et al. (2005) Reactome: a knowledgebase of biological pathways. Nucleic Acids Res., 33, D428–D432. Kanehisa, M. and Goto,S. (2000) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res., 28, 27–30. Khatri, P. et al. (2004) Onto-Tools: an ensemble of web-accessible, ontology-based tools for the functional design and interpretation of high-throughput gene expression experiments. Nucleic Acids Res., 32, W449–W456. Kong, S.W. et al. (2006) A multivariate approach for integrating genome-wide expression data and biological knowledge. Bioinforma. Oxf. Engl., 22, 2373–2380. Lashkari, D.A. et al. (1997) Yeast microarrays for genome wide parallel genetic and gene expression analysis. Proc. Natl. Acad. Sci. U. S. A., 94, 13057–13062. Naeem, H. et al. (2012) Rigorous assessment of gene set enrichment tests. Bioinformatics, 28, 1480–1486. Nikiforov, A.M. (1994) Algorithm AS 288: Exact Smirnov two-sample tests for abitrary distributions. J. R. Stat. Soc. Ser. C Appl. Stat., 43, 265–270. Nishimura, D. (2001) BioCarta. Biotech Softw. Internet Rep., 2, 117–120. Qiu, D. et al. (2008) Rice Gene Network Inferred from Expression Profiling of Plants Overexpressing OsWRKY13, a Positive Regulator of Disease Resistance. Mol. Plant, 1, 538–551. Quackenbush, J. (2001) Computational analysis of microarray data. Nat. Rev. Genet., 2, 418–427. Quackenbush, J. (2002) Microarray data normalization and transformation. Nat. Genet., 32, 496–501. Shah, N.H. and Fedoroff, N.V. (2004) CLENCH: a program for calculating Cluster ENriCHment using the Gene Ontology. Bioinformatics, 20, 1196–1197. Shen, K. and Tseng, G.C. (2010) Meta-analysis for pathway enrichment analysis when combining multiple genomic studies. Bioinformatics, 26, 1316–1323. Subramanian, A. et al. (2005) Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. U. S. A., 102, 15545–15550. Tsai, C.-A. and Liu, L.-Y.D. (2013) Identifying Gene Set Association Enrichment Using the Coefficient of Intrinsic Dependence. PLoS ONE, 8, e58851. Tusher, V.G. et al. (2001) Significance analysis of microarrays applied to the ionizing radiation response. Proc. Natl. Acad. Sci., 98, 5116–5121. Zeeberg, B.R. et al. (2003) GoMiner: a resource for biological interpretation of genomic and proteomic data. Genome Biol., 4, 1–8. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/57888 | - |
| dc.description.abstract | 微陣列可同時偵測數以萬計的基因表現量，要如何在繁多的基因中挑選出重要且具代表性的基因為大家致力的目標。挑選具代表性基因的研究單位為單一基因，著重於檢定每個基因在一個微陣列資料中不同條件下的表現量是否顯著不同，再將較顯著的基因挑選出。相較於關注研究單一基因的表現，而後衍生出以探討一群基因顯著性為目的的研究，即為基因富集檢定 (gene enrichment test)。基因富集檢定研究對象為一群根據研究目的定義而選取的基因稱為基因組，目標是分析選定基因組內的基因在不同條件下表現量是否顯著不同。基因富集檢定已發展出許多統計方法，常用的基因富級檢定方法大致可分為兩類：單變量方法與多變量方法。單變量方法彙整基因組內各基因單變量檢定統計量的結果作為基因組顯著與否的依據，而多變量方法視基因組表現量資料為高維度的常態分佈進行統計分析，得以考慮基因間的相關性。論文中先以模擬資料比較常用的基因富級檢定方法，以檢定力 (power) 與曲線下面積 (AUC) 做為優劣比較的指標，並以實際資料比對不同方法富集檢定的結果。結果顯示霍特林T方及基因集合富集分析在模擬資料及實際資料皆能檢定出最多顯著基因組，而魏可遜等級和檢定及柯史檢定的敏感度及特異度最高，綜合來說，基因集合富集分析穩定且準確度高，為表現最佳的方法。 | zh_TW |
| dc.description.abstract | Microarray aims to simultaneously monitor the expression of thousands of genes. It is usually the objective to mine important information from the data, such as the representative genes that differentially expressed (DE) under different conditions. In recent years, several gene set enrichment tests have been proposed to search for a DE gene set under different conditions. The gene set enrichment tests can be divided into two categories, univariate and multivariate methods. The former summarizes univariate statistics from each gene in the set to infer whether the gene set is significantly DE or not, while the latter considers the correlation among genes by assuming multinormal distribution and using multivariate analysis. In this study, we compared seven gene set enrichment tests by simulations. The tests were also practiced on a real microarray dataset. The results showed that Hotellin's T2 and gene set enrichment analysis were the most powerful. Wilcoxon rank sum test and Kolmogorov-Smirnov test had the best sensitivity and specificity. In conclusion, gene set enrichment analysis was the most robust method to detect DE gene sets. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-16T07:09:43Z (GMT). No. of bitstreams: 1 ntu-103-R01621202-1.pdf: 1276893 bytes, checksum: dee960dda8d05a176a5b0a7eead810bb (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 摘要 i
Abstract ii 目錄 iii 圖目錄 iv 表目錄 v 壹、前言 1 貳、材料與方法 4 一、模擬資料 4 (一) 模擬一 4 (二) 模擬二 5 (三) 模擬三 5 二、實際資料 6 三、基因富集檢定統計法 6 (一) 單變量檢定方法 7 (二) 多變量檢定方法 11 参、結果 13 一、模擬一 13 二、模擬二及模擬三 13 三、實際資料 14 肆、討論 16 伍、結論 20 陸、參考文獻 22 柒、附錄 43 一、R函數指令 43 二、中英對照表 46 | |
| dc.language.iso | zh-TW | |
| dc.subject | 曲線下面積 | zh_TW |
| dc.subject | 基因富集檢定 | zh_TW |
| dc.subject | 檢定力 | zh_TW |
| dc.subject | 多變量方法 | zh_TW |
| dc.subject | 單變量方法 | zh_TW |
| dc.subject | 微陣列資料 | zh_TW |
| dc.subject | multivariate methods | en |
| dc.subject | gene set enrichment tests | en |
| dc.subject | power | en |
| dc.subject | area under curve | en |
| dc.subject | univariate methods | en |
| dc.subject | microarray | en |
| dc.title | 基因富集檢定方法之比較 | zh_TW |
| dc.title | Comparison of statistical methods for gene set enrichment tests | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 廖振鐸,蔡政安 | |
| dc.subject.keyword | 微陣列資料,基因富集檢定,檢定力,曲線下面積,單變量方法,多變量方法, | zh_TW |
| dc.subject.keyword | microarray,gene set enrichment tests,power,area under curve,univariate methods,multivariate methods, | en |
| dc.relation.page | 47 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2014-07-08 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-103-1.pdf 未授權公開取用 | 1.25 MB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。