Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method.
Methodological study of affine transformations of gene expression data with proposed robust non-parametric multi-dimensional normalization method.
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DOI：
10.1186/1471-2105-7-100
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发表时间：
2006-03-01
影响因子：
3
通讯作者：
Hössjer O
中科院分区：
文献类型：
--
作者：
Bengtsson H;Hössjer O
微阵列数据的低层处理和标准化是微阵列分析中最重要的步骤，对下游分析有着深远的影响。到目前为止，已经提出了多种方法，但不清楚哪种方法是最好的。因此，重要的是进一步研究不同的标准化方法的细节和一般的微阵列数据的性质。对基因表达数据的仿射模型进行了方法学研究。重点是两个渠道的比较研究，但研究结果也推广到单渠道和多渠道的数据。讨论适用于斑点以及原位合成的微阵列数据。现有的规范化方法，如曲线拟合（“lowess”）规范化，平行和垂直的平移规范化，分位数规范化，但也染料交换规范化的光线中的仿射模型和他们的优点和缺点进行了研究，在这种情况下。作为这项研究的直接结果，我们提出了一个强大的非参数多维仿射归一化方法，它可以应用于任何数量的微阵列与任何数量的通道单独或一次全部。一个高质量的cDNA微阵列数据集与穗控制被用来证明仿射模型和建议的归一化方法的权力。我们发现，一个仿射模型可以解释非线性强度依赖的系统效应，在观察到的对数比。仿射归一化去除了非差异表达基因的这种伪像，并确保获得负对数比和正对数比之间的对称性，这在鉴定差异表达基因时是基本的。此外，仿射归一化使不同通道中的经验分布更加均匀，这是分位数归一化的目的，也可以解释为什么染料交换归一化有效或失败。所有方法都可以在aroma包中使用，这是一个独立于R平台的包。
Low-level processing and normalization of microarray data are most important steps in microarray analysis, which have profound impact on downstream analysis. Multiple methods have been suggested to date, but it is not clear which is the best. It is therefore important to further study the different normalization methods in detail and the nature of microarray data in general. A methodological study of affine models for gene expression data is carried out. Focus is on two-channel comparative studies, but the findings generalize also to single- and multi-channel data. The discussion applies to spotted as well as in-situ synthesized microarray data. Existing normalization methods such as curve-fit ("lowess") normalization, parallel and perpendicular translation normalization, and quantile normalization, but also dye-swap normalization are revisited in the light of the affine model and their strengths and weaknesses are investigated in this context. As a direct result from this study, we propose a robust non-parametric multi-dimensional affine normalization method, which can be applied to any number of microarrays with any number of channels either individually or all at once. A high-quality cDNA microarray data set with spike-in controls is used to demonstrate the power of the affine model and the proposed normalization method. We find that an affine model can explain non-linear intensity-dependent systematic effects in observed log-ratios. Affine normalization removes such artifacts for non-differentially expressed genes and assures that symmetry between negative and positive log-ratios is obtained, which is fundamental when identifying differentially expressed genes. In addition, affine normalization makes the empirical distributions in different channels more equal, which is the purpose of quantile normalization, and may also explain why dye-swap normalization works or fails. All methods are made available in the aroma package, which is a platform-independent package for R.
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影响因子：
7
作者：
Barczak, A;Rodriguez, MW;Erle, DJ
通讯作者：
Erle, DJ
影响因子：
7
作者：
Callow, MJ;Dudoit, S;Rubin, EM
通讯作者：
Rubin, EM
影响因子：
3
作者：
Bengtsson, H;Jönsson, G;Vallon-Christersson, J
通讯作者：
Vallon-Christersson, J
影响因子：
1.7
作者：
Kerr, MK;Martin, M;Churchill, GA
通讯作者：
Churchill, GA
影响因子：
1.8
作者：
CLEVELAND, WS
通讯作者：
CLEVELAND, WS