Data Variance and Statistical Significance in 2D-Gel Electrophoresis and DIGE Experiments: Comparison of the Effects of Normalization Methods
Data Variance and Statistical Significance in 2D-Gel Electrophoresis and DIGE Experiments: Comparison of the Effects of Normalization Methods
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DOI：
10.1021/pr101080e
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发表时间：
2011-03-01
影响因子：
4.4
通讯作者：
Collins, Richard A.
中科院分区：
文献类型：
--
作者：
Keeping, Andrew J.;Collins, Richard A.
鉴定不同生物样品之间蛋白质相对丰度的变化经常受到技术噪音的干扰。在这项工作中，我们比较了8种常用的归一化方法在二维凝胶电泳和差异凝胶电泳（DIGE）实验，他们的能力，以减少噪音和他们的影响的蛋白质的列表，其丰度的差异在两个样品之间被确定为具有统计学意义。关于降低噪声，我们发现，虽然所有的方法都改善了未归一化的数据，循环线性归一化是最不适合基于凝胶的蛋白质组学和其他方法的性能是相似的。我们还发现在DIGE数据中，归一化方法的选择对噪声的影响小于在实验设计中使用内部参考的决定，并且需要使用内部参考的归一化和标准化来最大限度地减少方差。尽管大多数标准化方法实现了类似的降噪，但其丰度被确定为在生物组之间显著不同的蛋白质列表取决于标准化方法的选择而不同。这项工作提供了一个直接比较的影响，在常见的实验设计的背景下的归一化方法。
Identifying changes in the relative abundance of proteins between different biological samples is often confounded by technical noise. In this work, we compared eight normalization methods commonly used in two-dimensional gel electrophoresis and difference gel electrophoresis (DIGE) experiments for their ability to reduce noise and for their influence on the list of proteins whose difference in abundance between two samples is determined to be statistically significant. With respect to reducing noise we find that, while all methods improve upon unnormalized data, cyclic linear normalization is the least well suited to gel-based proteomics and the performances of the other methods are similar. We also find in DIGE data that the choice of normalization method has less of an impact on the noise than does the decision to use an internal reference in the experimental design and that both normalization and standardization using the internal reference are required to maximally reduce variance. Despite the similar noise reduction achieved by most normalization methods, the list of proteins whose abundance was determined to differ significantly between biological groups differed depending on the choice of normalization method. This work provides a direct comparison of the impact of normalization methods in the context of common experimental designs.