Department Biomedical Informatics, Ohio State University, 250 Lincoln Tower, 1800 Cannon Dr. Columbus, Columbus, OH, 43210, USA.
Department of Medicine, Indiana University School of Medicine, 545 Barnhill Drive, Indianapolis, IN, 46202, USA.
BMC Bioinformatics. 2019 Dec 20;20(Suppl 24):679. doi: 10.1186/s12859-019-3247-x.
RNA sequencing technologies have allowed researchers to gain a better understanding of how the transcriptome affects disease. However, sequencing technologies often unintentionally introduce experimental error into RNA sequencing data. To counteract this, normalization methods are standardly applied with the intent of reducing the non-biologically derived variability inherent in transcriptomic measurements. However, the comparative efficacy of the various normalization techniques has not been tested in a standardized manner. Here we propose tests that evaluate numerous normalization techniques and applied them to a large-scale standard data set. These tests comprise a protocol that allows researchers to measure the amount of non-biological variability which is present in any data set after normalization has been performed, a crucial step to assessing the biological validity of data following normalization.
In this study we present two tests to assess the validity of normalization methods applied to a large-scale data set collected for systematic evaluation purposes. We tested various RNASeq normalization procedures and concluded that transcripts per million (TPM) was the best performing normalization method based on its preservation of biological signal as compared to the other methods tested.
Normalization is of vital importance to accurately interpret the results of genomic and transcriptomic experiments. More work, however, needs to be performed to optimize normalization methods for RNASeq data. The present effort helps pave the way for more systematic evaluations of normalization methods across different platforms. With our proposed schema researchers can evaluate their own or future normalization methods to further improve the field of RNASeq normalization.
RNA 测序技术使研究人员能够更好地了解转录组如何影响疾病。然而，测序技术通常会无意中将实验误差引入 RNA 测序数据中。为了克服这一问题，通常会应用标准化方法来减少转录组测量中固有的非生物学变异性。然而，各种标准化技术的比较效果尚未以标准化的方式进行测试。在这里，我们提出了一些测试方法来评估许多标准化技术，并将它们应用于一个大规模的标准数据集。这些测试包括一个方案，该方案允许研究人员测量在进行标准化后任何数据集存在的非生物学变异性的量，这是评估标准化后数据的生物学有效性的关键步骤。
在这项研究中，我们提出了两种测试方法来评估针对大规模数据集进行的标准化方法的有效性，该数据集是为系统评估目的而收集的。我们测试了各种 RNA-Seq 标准化程序，并得出结论，与测试的其他方法相比，每百万转录物（TPM）是表现最佳的标准化方法，因为它保留了生物学信号。
标准化对于准确解释基因组和转录组实验的结果至关重要。然而，需要做更多的工作来优化 RNA-Seq 数据的标准化方法。目前的努力有助于为不同平台的标准化方法进行更系统的评估铺平道路。通过我们提出的方案，研究人员可以评估他们自己或未来的标准化方法，以进一步改进 RNA-Seq 标准化领域。
