English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 63190/95884 (66%)
造访人次 : 4667206      在线人数 : 409
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU 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

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/69208

    题名: Normalization Methods for Analysis of Microarray Gene Expression Data
    作者: 陳怡如;Ralph L. Kodell;Frank Sistare;Chen, James J.
    贡献者: 淡江大學統計學系
    日期: 2003-08-01
    上传时间: 2011-10-23 16:36:20 (UTC+8)
    摘要: This paper investigates subset normalization to adjust for location biases (e.g., splotches) combined with global normalization for intensity biases (e.g., saturation). A data set from a toxicogenomic experiment using the same control and the same treated sample hybridized to six different microarrays is used to contrast the different normalization methods. Simple t-tests were used to compare two samples for dye effects and for treatment effects. The numbers of genes that reproducibly showed significant p-values for the unnormalized data and normalized data from different methods were evaluated for assessment of different normalization methods. The one-sample t-statistic of the ratio of red to green samples was used to test for dye effects using only control data. For treatment effects, in addition to the one-sample t-test of the ratio of the treated to control samples, the two-sample t-test for testing the difference between treated and control samples was also used to compare the two approaches. The method that combines a subset approach (median or lowessfit) for location adjustment with a global lowess fit for intensity adjustment appears to perform well.
    關聯: Journal of Biopharmaceutical Statistics 13(1), pp.57-74
    DOI: 10.1081/BIP-120017726
    显示于类别:[統計學系暨研究所] 期刊論文





    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回馈