School of Pharmaceutical Science and Technology, Tianjin University, Tianjin, China.
Department of Computer Science, National University of Singapore, Singapore, Singapore.
Sci Rep. 2020 Sep 23;10(1):15534. doi: 10.1038/s41598-020-72664-6.
Quantile normalization is an important normalization technique commonly used in high-dimensional data analysis. However, it is susceptible to class-effect proportion effects (the proportion of class-correlated variables in a dataset) and batch effects (the presence of potentially confounding technical variation) when applied blindly on whole data sets, resulting in higher false-positive and false-negative rates. We evaluate five strategies for performing quantile normalization, and demonstrate that good performance in terms of batch-effect correction and statistical feature selection can be readily achieved by first splitting data by sample class-labels before performing quantile normalization independently on each split ("Class-specific"). Via simulations with both real and simulated batch effects, we demonstrate that the "Class-specific" strategy (and others relying on similar principles) readily outperform whole-data quantile normalization, and is robust-preserving useful signals even during the combined analysis of separately-normalized datasets. Quantile normalization is a commonly used procedure. But when carelessly applied on whole datasets without first considering class-effect proportion and batch effects, can result in poor performance. If quantile normalization must be used, then we recommend using the "Class-specific" strategy.
分位数归一化是一种常用于高维数据分析的重要归一化技术。然而，当盲目地应用于整个数据集时，它容易受到类效应比例效应（数据集中文档相关变量的比例）和批次效应（潜在混杂技术变化的存在）的影响，导致更高的假阳性和假阴性率。我们评估了五种执行分位数归一化的策略，并证明通过在独立执行分位数归一化之前按样本类别标签分割数据（“类别特定”），可以轻松实现批次效应校正和统计特征选择方面的良好性能。通过对真实和模拟批次效应的模拟，我们证明了“类别特定”策略（以及其他依赖类似原理的策略）可以轻松优于整个数据的分位数归一化，并且即使在分别归一化数据集的联合分析中，也能保持稳健性并保留有用信号。分位数归一化是一种常用的过程。但是，如果在不首先考虑类效应比例和批次效应的情况下在整个数据集上小心地应用，可能会导致性能不佳。如果必须使用分位数归一化，那么我们建议使用“类别特定”策略。
