Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection
Targeted realignment of LC-MS profiles by neighbor-wise compound-specific graphical time warping with misalignment detection
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DOI：
10.1093/bioinformatics/btaa037
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发表时间：
2020-05-01
期刊：
影响因子：
5.8
通讯作者：
Yu, Guoqiang
中科院分区：
文献类型：
--
作者：
Wu, Chiung-Ting;Wang, Yizhi;Yu, Guoqiang
动机：液相色谱-质谱（LC-MS）是生物样品蛋白质组学和代谢组学分析的标准方法。不幸的是，它遭受不同样品中相同化合物的保留时间（RT）的各种变化，并且这些变化必须随后在数据处理期间进行校正（对齐）。经典的比对方法，如流行的XCMS软件包，通常假设每个样本都有一个时间扭曲函数。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。对于一个大规模的实验，涉及许多样品，存在的不对准成为不可避免的concerning.Results：在这里，我们描述了一个集成的参考自由配置文件对齐方法，邻居明智的化合物特定的图形时间规整（ncGTW），可以检测未对准的功能和对齐配置文件，利用预期的RT漂移结构和化合物特定的翘曲功能。具体而言，ncGTW使用个性化的翘曲函数为不同的化合物，并分配约束边缘上的翘曲函数的相邻样本。经过真实合成数据和内部质量控制样本的验证，ncGTW应用于两个大规模代谢组学LC-MS数据集，识别出许多失调的特征并成功重新对齐它们。否则，这些特征将被丢弃或使用现有方法不校正。ncGTW软件工具目前作为插件开发，用于检测和重新对齐标准XCMS输出中存在的未对齐特征。
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.