政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/30906
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  全文笔数/总笔数 : 113303/144284 (79%)
造访人次 : 50818071      在线人数 : 781
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
搜寻范围 查询小技巧：
  • 您可在西文检索词汇前后加上"双引号"，以获取较精准的检索结果
  • 若欲以作者姓名搜寻，建议至进阶搜寻限定作者字段，可获得较完整数据
    •  进阶搜寻
    政大機構典藏 > 商學院 > 統計學系 > 學位論文 >  Item 140.119/30906


    请使用永久网址来引用或连结此文件: https://nccur.lib.nccu.edu.tw/handle/140.119/30906


    题名: 模擬高密度寡聚核甘酸微陣列矩陣資料及正規化方法之探討
    A Simulation Study on High Density Oligonucleotide Microarray Data With Discussion of Normalization Methods
    作者: 吳小萍
    Wu, Hsiao-Ping
    贡献者: 郭訓志
    蔡紋琦
    Kuo, Hsun-Chih
    Tsai, Wen-Chi
    吳小萍
    Wu, Hsiao-Ping
    关键词: 微陣列矩陣
    正規化
    microarray
    normalization
    日期: 2005
    上传时间: 2009-09-14
    摘要: 微陣列矩陣晶片是一門現今被廣泛使用在許多領域的生物醫學研究，在本文，我們主要是對寡核甘酸微陣列矩陣晶片資料的正規化感興趣。為了比較不同的正規化方法，我們致力於模擬更接近真實寡核甘酸微陣列矩陣晶片的資料。在資料的模擬上，我們主要是根據Li和Wong的模型來進行模擬，並利用階層法來設定模型的參數。最後為了判別正規化方法的好壞，我們模擬了100組資料，並且利用四個判斷準則來做比較。模擬的結果表示，我們所提出的新方法
    (LOESS to Average)，一般來說都比其他的正規化方法來的好。
    Microarray technology is now widely used in many areas of biomedical research. In this thesis, we are interested in the normalization for oligonucleotide Microarray data. We aimed to simulate more realistic oligonucleotide microarry data in order to compare different normalization methods. The data simulation was based on Li and Wong`s model with a hierarchical setup for parameters. In order to compare normalization methods, 100 data sets were simulated data. The performance of ten normalization methods was assessed based on four comparison criteria. Simulation results suggest that our new proposed normalization method, LOESS
    to Average, is generally a better method than other normalization methods.
    參考文獻: [1] Affymetrix (2002), Statistical algorithms description
    document, Technical report, Affymetrix.
    [2] B. M. Bolstad, R. A. Irizarry, M. Astrand and T. P.
    Speed (2003), A comparison of normalization methods for
    high density oligonucleotide array data based on
    variance and bias, Bioinformatics, 19(2), 185-193.
    [3] R. A. Irizarry, B. Hobbs, F. Collin, Y. D. Beazer-
    Barclay, K. J. Antonellis, U. Scherf and T. P. Speed
    (2003), Exploration, normalization, and summaries of
    high density oligonucleotide array probe level data,
    Biostatistics, 4(2), 249-264.
    [4] C. Li and W. H. Wong (2001a), Model-based analysis of
    oligonucleotide arrays: expression index computation
    and outlier detection, Proceedings of the National
    Academy of Science USA, 98, 31-36.
    [5] C. Li and W. H. Wong (2001b), Model-based analysis of
    oligonucleotide arrays: model validation, design issues
    and standard error application, Genome Biology 2(8):
    research 0032.1-0032.11.
    [6] R. A. Irizarry, B. M. Bolstad, F. Collin, L. M. Cope,
    B. Hobbs and T. P. Speed (2003), Summaries of
    affymetrix GeneChip probe level data, Nucleic Acids
    Research, 31(4), e15.
    [7] B. Bolstad (2001), Probe level quantile normalization of
    high density oligonucleotide array data, Division of
    Biostatistics.
    [8] B. Bolstad (2002), Comparing the effects of background,
    normalization and summarization on gene expression
    estimates.
    [9] Affymetrix (2001), GeneChip arrays provide optimal
    sensitivity and specificity for microarray expression
    analysis, Affymetrix.
    [10] B. M. Bolstad (2004), Low-level analysis of high-
    density Oligonucleotide array data: background,
    normalization and summarization.
    [11] D. Holder, R. F. Raubertas, V. Bill Pikounis, V.
    Svetnik and K. Soper, statistical analysis of high
    density oligonucleotide arrays: a safer approach,
    Merck Research Laboratories, WP37C-305, West Point, PA
    19486.
    [12] F. Naef, D. A. Lim, N. Patil and M. O. Magnasco
    (2001),From features to expression: High-density
    oligonucleotide array analysis revisited, Tech Report,
    1, 1-9.
    [13] R. Sasik, E. Calvo and J. Corbeil (2002), Statistical
    analysis of high-density oligonucleotide arrays: a
    multiplicative noise model, Bioinformatics 18(12),
    1633-1640.
    [14] dChip User`s Manual (2005)
    http://biosun1.harvard.edu/complab/dchip
    [15] 薛慧芬 (2005), The research of normalization methods
    for high density oligonucleotide array, Thesis at
    National Chengchi University.
    [16] S. Dudoit, Y. H. Yang, M. J. Callow and T. P. Speed
    (2000), Statistical methods for identifying
    differentially expressed genes in replicated cDNA
    microarray experiments.
    描述: 碩士
    國立政治大學
    統計研究所
    93354023
    94
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0093354023
    数据类型: thesis
    显示于类别:[統計學系] 學位論文

    文件中的档案:

    档案 大小格式浏览次数
    index.html0KbHTML2313检视/开启


    在政大典藏中所有的数据项都受到原著作权保护.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - 回馈