Image Processing and Machine Learning for Automated Identification of Chemo-/Biomarkers in Chromatography-Mass Spectrometry
Image Processing and Machine Learning for Automated Identification of Chemo-/Biomarkers in Chromatography-Mass Spectrometry
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DOI：
10.1021/acs.analchem.1c03163
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发表时间：
2021-10-27
影响因子：
7.4
通讯作者：
Yanagida, Takeshi
中科院分区：
文献类型：
--
作者：
Jirayupat, Chaiyanut;Nagashima, Kazuki;Yanagida, Takeshi
我们提出了一种名为NPFimg的方法，该方法通过结合图像处理和机器学习自动识别色谱-质谱（MS）数据中分析物的多变量化学/生物标志物特征。NPFimg处理二维MS图谱（m/z vs保留时间），以区分分析物并识别和可视化标记物特征。我们的方法使我们能够全面表征MS数据中的信号，而无需传统的峰值拾取过程，该过程会受到假峰检测的影响。在芳香气味和人体呼吸的气相色谱-质谱（GC-MS）的案例研究中，即使在十亿分之一的水平上，标记识别的可行性也得到了成功的证明。与广泛使用的XCMS的比较表明，NPFimg具有良好的可靠性，因为它具有较低的错误率的信号采集和标记识别。此外，我们显示了NPFimg对人类呼吸的非靶向代谢组学的潜在适用性。虽然这项研究显示了有限的应用，但NPFimg可能适用于使用GC-MS和液相色谱-MS的各种代谢组学/化学计量学的数据处理。
We present a method named NPFimg, which automatically identifies multivariate chemo-/biomarker features of analytes in chromatography-mass spectrometry (MS) data by combining image processing and machine learning. NPFimg processes a two-dimensional MS map (m/z vs retention time) to discriminate analytes and identify and visualize the marker features. Our approach allows us to comprehensively characterize the signals in MS data without the conventional peak picking process, which suffers from false peak detections. The feasibility of marker identification is successfully demonstrated in case studies of aroma odor and human breath on gas chromatography-mass spectrometry (GC-MS) even at the parts per billion level. Comparison with the widely used XCMS shows the excellent reliability of NPFimg, in that it has lower error rates of signal acquisition and marker identification. In addition, we show the potential applicability of NPFimg to the untargeted metabolomics of human breath. While this study shows the limited applications, NPFimg is potentially applicable to data processing in diverse metabolomics/chemometrics using GC-MS and liquid chromatography-MS.