AntDAS: Automatic Data Analysis Strategy for UPLC-QTOF-Based Nontargeted Metabolic Profiling Analysis
AntDAS: Automatic Data Analysis Strategy for UPLC-QTOF-Based Nontargeted Metabolic Profiling Analysis
复制标题
AntDAS：基于 UPLC-QTOF 的非靶向代谢谱分析的自动数据分析策略
DOI：
10.1021/acs.analchem.7b03160
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发表时间：
2017-10-17
影响因子：
7.4
通讯作者：
She, Yuanbin
中科院分区：
文献类型：
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作者：
Fu, Hai-Yan;Guo, Xiao-Ming;She, Yuanbin
高质量的数据分析方法仍然是基于超高效液相色谱-四极杆飞行时间质谱的代谢谱分析的瓶颈。本工作旨在通过提出一种新的数据分析策略来解决这个问题，其中（1）使用先进的基于多尺度高斯平滑的峰提取策略自动提取UPLC-QTOF数据集中的色谱峰； (2) 峰注释、阶段用于对属于同一化合物的碎片离子进行聚类。借助高分辨率质谱仪，（3）通过新的峰对齐方法有效地对样品进行时移校正； (4)采用新开发的自适应网络搜索算法来注册组件； (5)然后使用方差分析和层次聚类分析等统计方法来鉴定潜在的标记化合物；最后，(6) 通过将提取的峰信息（包括高精度 m/z 和保留时间）与包含 500 多种植物代谢物的化合物库进行匹配来进行化合物鉴定。使用手动设计的 18 种化合物的混合物来评估该方法的性能，并在不同浓度水平下检测所有化合物。所开发的方法通过包含 2000 多个成分的极其复杂的植物数据集进行综合评估。结果表明，所开发方法的性能与 XCMS 相当。 MATLAB GUI 代码可从 http://software.tObaccodb.org/software/antdas 获取。
High-quality data analysis methodology remains a bottleneck for metabolic profiling analysis based on ultraperformance liquid chromatography-quadrupole time-Of-flight mass spectrometry. The present work aims to address this problem by proposing a novel data analysis strategy wherein (1) chromatographic peaks in the UPLC-QTOF data set are automatically extracted by using an advanced multiscale Gaussian smoothing-based peak extracHon strategy; (2) a peak annotation, stage is used to,cluster fragment ions that belong to the same compound. With the aid of high resolution mass spectrometer, (3) a time-shift correction across the samples is efficiently performed By a new peak alignment method; (4) components are registered by using a newly developed adaptive network searching algorithm; (5) statistical methods, such as analysis of variance and hierarchical cluster analysis, are then used to identify the underlying marker compounds; finally, (6) compound identification is performed by matching the extracted peak information, involving high-precision m/z and retention time, against our compound library containing more than 500 plant metabolites. A manually designed mixture of 18 compounds is used to evaluate the performance of the method, and all compounds are detected under various concentration levels. The developed method is comprehensively evaluated by an extremely complex plant data set containing more than 2000 components. Results indicate that the performance of the developed method is comparable with the XCMS. The MATLAB GUI code is available from http://software.tObaccodb.org/software/antdas.