Systematic comparison of RNA-Seq normalization methods using measurement error models
Systematic comparison of RNA-Seq normalization methods using measurement error models
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DOI：
10.1093/bioinformatics/bts497
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发表时间：
2012-10-15
期刊：
影响因子：
5.8
通讯作者：
Zhu, Yu
中科院分区：
文献类型：
--
作者：
Sun, Zhaonan;Zhu, Yu
动机：RNA-Seq技术及其应用的进一步发展要求开发有效的RNA-Seq数据归一化方法。目前，不同的标准化方法进行了比较，并通过它们与某个金标准的相关性进行验证。通过不同的技术或平台（如实时逆转录聚合酶链反应（qRT-PCR）或微阵列）生成的基因表达测量通常用作金标准。虽然目前的方法是直观的，易于实施，它变得统计不足时，金标准也受到测量误差（ME）。此外，目前的方法是没有信息的，因为与一定的金标准的归一化方法的相关性不提供太多的信息规范化RNA-Seq measurements.Results的确切质量：我们建议使用系统的ME模型的基础上qRT-PCR，微阵列和RNA-Seq基因表达数据，比较和验证RNA-Seq归一化方法。这种方法并不假设存在金本位。归一化方法的性能可以由系统的一组参数来表征，这些参数被称为性能参数，并且这些性能参数可以被一致地估计。因此，不同的归一化方法可以通过比较它们相应的估计性能参数来进行比较。我们应用所提出的方法来比较现有的五种RNA-Seq标准化方法，使用来自微阵列质量控制和测序质量控制项目的两个RNA样本的基因表达数据，并对这些方法的优缺点有了很大的了解。
Motivation: Further advancement of RNA-Seq technology and its application call for the development of effective normalization methods for RNA-Seq data. Currently, different normalization methods are compared and validated by their correlations with a certain gold standard. Gene expression measurements generated by a different technology or platform such as Real-time reverse transcription polymerase chain reaction (qRT-PCR) or Microarray are usually used as the gold standard. Although the current approach is intuitive and easy to implement, it becomes statistically inadequate when the gold standard is also subject to measurement error (ME). Furthermore, the current approach is not informative, because the correlation of a normalization method with a certain gold standard does not provide much information about the exact quality of the normalized RNA-Seq measurements.Results: We propose to use the system of ME models based on qRT-PCR, Microarray and RNA-Seq gene expression data to compare and validate RNA-Seq normalization methods. This approach does not assume the existence of a gold standard. The performance of a normalization method can be characterized by a group of parameters of the system, which are referred to as the performance parameters, and these performance parameters can be consistently estimated. Different normalization methods can thus be compared by comparing their corresponding estimated performance parameters. We applied the proposed approach to compare five existing RNA-Seq normalization methods using the gene expression data of two RNA samples from the microArray Quality Control and Sequencing Quality Control projects and gained much insight about the pros and cons of these methods.