A method for normalizing microarrays using genes that are not differentially expressed

• 成果类型: Article
• 署名作者: Reilly, C; Wang, CC; Rutherford, M
• 署名单位: University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214503000000800
• 发表日期: 2003
• 页码: 868-878
• 关键词: respiratory syndrome virus; porcine epidemic abortion; mystery swine disease; lelystad virus; infection; models; pigs; macrophages; prrsv; lungs

摘要

One of the more challenging, yet easily overlooked, aspects of the analysis of microarrays is how to normalize arrays so that comparisons can be made across arrays. Most studies that utilize microarrays to detect differential gene expression between samples find the data only enable one to conclude that a handful of genes are differentially expressed. The basic idea here is to use the genes that are not differentially expressed to conduct the normalization. Of course, because one cannot determine which genes are diffierentially expressed until the normalization is conducted, this is a nontrivial problem. Here a general framework and computational method (using the Gibbs sampler) is devised to allow for such normalization. We apply the method to a gene expression experiment aimed at furthering our understanding of Porcine reproductive and respiratory syndrome virus, a major source of economic loss in the swine industry.