Department of Statistics, Purdue University, West Lafayette, IN 47906, USA.
Bioinformatics. 2012 Oct 15;28(20):2584-91. doi: 10.1093/bioinformatics/bts497. Epub 2012 Aug 22.
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.
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.
进一步推进 RNA-Seq 技术及其应用需要开发有效的 RNA-Seq 数据标准化方法。目前，不同的标准化方法通过与某个金标准的相关性来进行比较和验证。不同技术或平台生成的基因表达测量值，如实时逆转录聚合酶链反应 (qRT-PCR) 或微阵列，通常用作金标准。尽管目前的方法直观且易于实施，但当金标准本身也存在测量误差 (ME) 时，它在统计学上就变得不够充分了。此外，目前的方法没有信息量，因为标准化方法与某个金标准的相关性并不能提供有关标准化 RNA-Seq 测量值的确切质量的太多信息。
我们建议使用基于 qRT-PCR、微阵列和 RNA-Seq 基因表达数据的 ME 模型系统来比较和验证 RNA-Seq 标准化方法。该方法不假设金标准的存在。标准化方法的性能可以通过一组系统参数来描述，这些参数称为性能参数，并且可以一致地估计这些性能参数。因此，可以通过比较它们对应的估计性能参数来比较不同的标准化方法。我们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，应用提出的方法来比较五种现有的 RNA-Seq 标准化方法，并深入了解这些方法的优缺点。
