Peak alignment of gas chromatography-mass spectrometry data with deep learning
Peak alignment of gas chromatography-mass spectrometry data with deep learning
复制标题
基于深度学习的气相色谱-质谱峰对齐
DOI：
10.1016/j.chroma.2019.460476
复制
发表时间：
2019-10-25
影响因子：
4.1
通讯作者：
Wang, X. Rosalind
中科院分区：
文献类型：
--
作者：
Li, Mike;Wang, X. Rosalind
我们提出了一种深度学习模型ChromAlignNet，用于气相色谱-质谱仪(GC-MS)数据中的峰对齐。在GC-MS数据中，化合物的保留时间(RT)可能不会在多个色谱图中保持不变。要使用GC-MS数据发现生物标记物，需要对不同样品中相同分析物的RT进行比对。目前的比对方法都是基于一套正式的数学规则。我们提出了一种使用深度学习神经网络的GC-MS比对解决方案，它更擅长于复杂的、模糊的数据集。我们在几个不同复杂性的GC-MS数据集上对我们的模型进行了测试，并对比对结果进行了定量分析。我们证明了该模型具有非常好的性能(对于简单的数据集，AUC类似于1，对于非常复杂的数据集，AUC类似于0.85)。此外，我们的模型在复杂数据集上的性能很容易超过现有的算法。与现有方法相比，ChromAlignNet非常易于使用，因为它不需要用户输入参考色谱图和参数。这种方法可以很容易地适用于其他类似的数据，如那些来自液相色谱的数据。源代码是用Python编写的，可以在网上获得。皇冠版权(C)2019由爱思唯尔B.V.出版。保留所有权利。
We present ChromAlignNet, a deep learning model for alignment of peaks in Gas Chromatography-Mass Spectrometry (GC-MS) data. In GC-MS data, a compound's retention time (RT) may not stay fixed across multiple chromatograms. To use GC-MS data for biomarker discovery requires alignment of identical analyte's RT from different samples. Current methods of alignment are all based on a set of formal, mathematical rules. We present a solution to GC-MS alignment using deep learning neural networks, which are more adept at complex, fuzzy data sets. We tested our model on several GC-MS data sets of various complexities and analysed the alignment results quantitatively. We show the model has very good performance (AUC similar to 1 for simple data sets and AUC similar to 0.85 for very complex data sets). Further, our model easily outperforms existing algorithms on complex data sets. Compared with existing methods, ChromAlignNet is very easy to use as it requires no user input of reference chromatograms and parameters. This method can easily be adapted to other similar data such as those from liquid chromatography. The source code is written in Python and available online. Crown Copyright (C) 2019 Published by Elsevier B.V. All rights reserved.