Normalization Methods for Analysis of Microarray Gene Expression Data

doi:10.1081/BIP-120017726

淡江大學機構典藏 > 商管學院 > 統計學系暨研究所 > 期刊論文 > Item 987654321/69208

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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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