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    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

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