Supervised normalization of microarrays
Supervised normalization of microarrays
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DOI：
10.1093/bioinformatics/btq118
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发表时间：
2010-05-15
期刊：
影响因子：
5.8
通讯作者：
Storey, John D.
中科院分区：
文献类型：
--
作者：
Mecham, Brigham H.;Nelson, Peter S.;Storey, John D.
动机：在利用微阵列技术测量核酸丰度时，一个主要挑战是“归一化”，其目的是将具有生物学意义的信号与其他混杂的信号源区分开来，这些混杂信号源通常是由不可避免的技术因素导致的。直观地很明显，在进行归一化时需要同时考虑真实的生物学信号和混杂因素。然而，最流行的归一化方法并没有利用关于研究的已知信息，无论是在感兴趣的生物学变量方面，还是在研究中已知的技术因素方面，比如批次或阵列处理日期。
结果：我们在此表明，在进行归一化时如果没有包含所有特定于研究的生物学和技术变量，会导致下游分析出现偏差。我们提出了一个通用的归一化框架，该框架采用一个特定于研究的模型，该模型利用与表达研究相关的每一个已知变量。所提出的方法通常适用于现有的所有探针设计，以及单通道和双通道阵列。我们通过真实和模拟的例子表明，与一些最常用的归一化方法相比，该方法具有良好的操作特性。
可用性：一个名为snm的实现该方法的R包将可从Bioconductor（http://bioconductor.org）获取。
联系人：jstorey@princeton.edu
补充信息：补充数据可在Bioinformatics在线获取。
Motivation: A major challenge in utilizing microarray technologies to measure nucleic acid abundances is 'normalization', the goal of which is to separate biologically meaningful signal from other confounding sources of signal, often due to unavoidable technical factors. It is intuitively clear that true biological signal and confounding factors need to be simultaneously considered when performing normalization. However, the most popular normalization approaches do not utilize what is known about the study, both in terms of the biological variables of interest and the known technical factors in the study, such as batch or array processing date.Results: We show here that failing to include all study-specific biological and technical variables when performing normalization leads to biased downstream analyses. We propose a general normalization framework that fits a study-specific model employing every known variable that is relevant to the expression study. The proposed method is generally applicable to the full range of existing probe designs, as well as to both single-channel and dual-channel arrays. We show through real and simulated examples that the method has favorable operating characteristics in comparison to some of the most highly used normalization methods.Availability: An R package called snm implementing the methodology will be made available from Bioconductor (http://bioconductor.org).Contact: jstorey@princeton.eduSupplementary information: Supplementary data are available at Bioinformatics online.