In recent years, mass spectrometry-based metabolomics has increasingly been applied to large-scale epidemiological studies of human subjects. However, the successful use of metabolomics in this context is subject to the challenge of detecting biologically significant effects despite substantial intensity drift that often occurs when data are acquired over a long period or in multiple batches. Numerous computational strategies and software tools have been developed to aid in correcting for intensity drift in metabolomics data, but most of these techniques are implemented using command-line driven software and custom scripts which are not accessible to all end users of metabolomics data. Further, it has not yet become routine practice to assess the quantitative accuracy of drift correction against techniques which enable true absolute quantitation such as isotope dilution mass spectrometry. We developed an Excel-based tool, MetaboDrift, to visually evaluate and correct for intensity drift in a multi-batch liquid chromatography – mass spectrometry (LC–MS) metabolomics dataset. The tool enables drift correction based on either quality control (QC) samples analyzed throughout the batches or using QC-sample independent methods. We applied MetaboDrift to an original set of clinical metabolomics data from a mixed-meal tolerance test (MMTT). The performance of the method was evaluated for multiple classes of metabolites by comparison with normalization using isotope-labeled internal standards. QC sample-based intensity drift correction significantly improved correlation with IS-normalized data, and resulted in detection of additional metabolites with significant physiological response to the MMTT. The relative merits of different QC-sample curve fitting strategies are discussed in the context of batch size and drift pattern complexity. Our drift correction tool offers a practical, simplified approach to drift correction and batch combination in large metabolomics studies.

近年来，基于质谱的代谢组学已越来越多地应用于人类受试者的大规模流行病学研究。然而，在这种情况下成功使用代谢组学面临挑战，即检测生物学上显着的影响，尽管在长时间或分批获取数据时经常会发生强度的大幅漂移。已经开发了许多计算策略和软件工具来帮助校正代谢组学数据中的强度漂移，但是大多数这些技术都是使用命令行驱动的软件和自定义脚本来实现的，而代谢组学数据的所有最终用户都无法访问这些脚本和自定义脚本。进一步，相对于能够进行真正绝对定量的技术（例如同位素稀释质谱法）来评估漂移校正的定量精度，尚未成为常规操作。我们开发了基于Excel的工具MetaboDrift，用于在多批次液相色谱-质谱（LC-MS）代谢组学数据集中直观地评估和校正强度漂移。该工具可以基于在整个批次中分析的质量控制（QC）样本或使用独立于QC样本的方法来进行漂移校正。我们将MetaboDrift应用于来自混合膳食耐受性测试（MMTT）的一组原始的临床代谢组学数据。通过与使用同位素标记的内标进行归一化比较，评估了该方法对多种代谢产物的性能。基于QC样本的强度漂移校正显着改善了与IS标准化数据的相关性，并导致检测到对MMTT具有显着生理反应的其他代谢物。在批次大小和漂移模式复杂度的背景下，讨论了不同QC样本曲线拟合策略的相对优点。我们的漂移校正工具为大型代谢组学研究提供了一种实用，简化的漂移校正和批次组合方法。