MOTIVATION Liquid chromatography-mass spectrometry (LC-MS) is a standard method for proteomics and metabolomics analysis of biological samples. Unfortunately, it suffers from various changes in the retention times (RT) of the same compound in different samples, and these must be subsequently corrected (aligned) during data processing. Classic alignment methods such as in the popular XCMS package often assume a single time-warping function for each sample. Thus, the potentially varying RT drift for compounds with different masses in a sample is neglected in these methods. Moreover, the systematic change in RT drift across run order is often not considered by alignment algorithms. Therefore, these methods cannot effectively correct all misalignments. For a large-scale experiment involving many samples, the existence of misalignment becomes inevitable and concerning. RESULTS Here, we describe an integrated reference-free profile alignment method, neighbor-wise compound-specific Graphical Time Warping (ncGTW), that can detect misaligned features and align profiles by leveraging expected RT drift structures and compound-specific warping functions. Specifically, ncGTW uses individualized warping functions for different compounds and assigns constraint edges on warping functions of neighboring samples. Validated with both realistic synthetic data and internal quality control samples, ncGTW applied to two large-scale metabolomics LC-MS datasets identifies many misaligned features and successfully realigns them. These features would otherwise be discarded or uncorrected using existing methods. The ncGTW software tool is developed currently as a plug-in to detect and realign misaligned features present in standard XCMS output. AVAILABILITY AND IMPLEMENTATION An R package of ncGTW is freely available at Bioconductor and https://github.com/ChiungTingWu/ncGTW. A detailed user's manual and a vignette are provided within the package. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

中文翻译：

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通过邻位化合物特定的图形时间扭曲和错位检测对LC-MS配置文件进行有针对性的重排。

动机液相色谱-质谱法（LC-MS）是对生物样品进行蛋白质组学和代谢组学分析的标准方法。不幸的是，同一化合物在不同样品中的保留时间（RT）会发生各种变化，因此必须在数据处理过程中对其进行纠正（校正）。经典的比对方法（例如流行的XCMS软件包中的方法）通常为每个样本采用单个时间扭曲功能。因此，在这些方法中忽略了样品中具有不同质量的化合物的潜在变化的RT漂移。此外，比对算法通常不会考虑跨运行顺序的RT漂移的系统变化。因此，这些方法不能有效地校正所有未对准。对于涉及许多样品的大规模实验，对准误差的存在变得不可避免和令人担忧。结果在这里，我们描述了一种集成的，无参考的轮廓对齐方法，即邻域特定于化合物的图形时间扭曲（ncGTW），它可以利用预期的RT漂移结构和特定于化合物的扭曲函数来检测未对齐的特征并对齐轮廓。具体来说，ncGTW对不同的化合物使用个性化的翘曲函数，并在相邻样本的翘曲函数上分配约束边。ncGTW已通过现实的合成数据和内部质量控制样品验证，应用于两个大规模代谢组学LC-MS数据集，可识别许多未对齐的特征并成功对其进行重新对齐。否则，将使用现有方法丢弃或不纠正这些功能。ncGTW软件工具当前是作为插件开发的，用于检测和重新对齐标准XCMS输出中存在的未对齐功能。可用性和实现ncGTW的R包可从Bioconductor和https://github.com/ChiungTingWu/ncGTW免费获得。包装内提供了详细的用户手册和插图。补充信息补充数据可从Bioinformatics在线获得。