Institute of Pharmaceutical Sciences, Pharmaceutical (Bio-)Analysis , University of Tübingen , Tübingen 72076 , Germany.
Anal Chem. 2019 Aug 6;91(15):9836-9843. doi: 10.1021/acs.analchem.9b01505. Epub 2019 Jul 9.
Due to variation in instrument response caused by various sources of errors throughout an analytical assay, data normalization plays an indispensable role in untargeted LC-MS profiling, yet limited accepted guidelines on this topic exist. In this work, a systematic comparison of several normalization techniques, mainly focusing on internal standard-based approaches, has been performed to derive some general recommendations. For generation of untargeted lipidomic data, a comprehensive ultra-high performance liquid chromatography (UHPLC)-electrospray ionization (ESI)-quadrupole time of flight (QTOF)-MS/MS method was utilized. To monitor instrument stability and evaluate normalization performance, quality control (QC) samples, prepared from aliquots of all experimental samples, were embedded in the sequence. Stable isotope labeled standards, representing differing lipid classes, were spiked to each sample as internal standards for postacquisition normalization. Various metrics were used to compare distinct normalization strategies, with reduction of variation in QC samples being the critical requirement for acceptance of successful normalization. The comparison of intragroup coefficients of variation (CVs), median absolute deviations (MADs), and variance enables simple selection of the best performance of normalization with improved and coherent results. Furthermore, the importance for normalization in critical data sets, showing only minor effects between groups with high variation and outliers, is pointed out. Apart from normalization, also, influences of used raw data types are demonstrated. In addition, effects of various factors throughout the processing workflow were investigated and optimized. Eventually, implementation of quality control samples, even if not required for normalization, provided a useful basis for assessing data quality. Due to lack of consensus for selecting optimum normalization, suggestions for validating data integrity are given.
由于分析过程中各种误差源导致仪器响应的变化，数据归一化在非靶向 LC-MS 分析中起着不可或缺的作用，但目前关于这个主题的接受准则有限。在这项工作中，主要针对基于内标法的方法，对几种归一化技术进行了系统比较，得出了一些一般性建议。为了生成非靶向脂质组学数据，使用了全面的超高效液相色谱 (UHPLC)-电喷雾电离 (ESI)-四极杆飞行时间 (QTOF)-MS/MS 方法。为了监测仪器稳定性并评估归一化性能，将来自所有实验样品等分的 QC 样品嵌入到序列中。将代表不同脂质类别的稳定同位素标记标准品作为内标物添加到每个样品中，用于采集后的归一化。使用各种指标来比较不同的归一化策略，QC 样品的变异减少是成功归一化的关键要求。比较组内变异系数 (CV)、中位数绝对偏差 (MAD) 和方差，可以简单地选择归一化性能最佳的方法，得到改进且一致的结果。此外，还指出了在具有高变异性和离群值的组间差异较小的数据集中归一化的重要性。除了归一化，还展示了所用原始数据类型的影响。此外，还研究和优化了处理工作流程中各种因素的影响。最终，即使不需要归一化，QC 样品的实施也为评估数据质量提供了有用的基础。由于缺乏选择最佳归一化的共识，因此给出了验证数据完整性的建议。
