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Semilinear high-dimensional model for normalization of microarray data: a theoretical analysis and partial consistency; Comment.
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Journal of the American Statistical Association
- Article date:
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September 1, 2005
- Author:
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;
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Copyright informationCOPYRIGHT 2005 American Statistical Association. This material is published under license from the publisher through the Gale Group, Farmington Hills, Michigan. All inquiries regarding rights should be directed to the Gale Group. (Hide copyright information)
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Normalization is a critical component in microarray data analysis. Its purpose is to remove systematic biases in the observed expression values and to establish baseline intensity ratios across the whole dynamic range. Many researchers have considered this problem (see, e.g., Chen, Dougherty, and Bittner 1997; Kerr, Martin, and Churchill 2000; Yang et al. 2002; Yang, Dudoit, Luu, and Speed 2001; Tseng, Oh, Rohlin, Liao, and Wong 2001; Park et al. 2003). In particular, Fan, Tam, Vande Woude, and Ren (2004) proposed a semilinear in-slide model (SLIM) method that makes use of replications of a subset of genes in an array. In the present interesting and stimulating article, Fan, ...
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