Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics
Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data anal...
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| Format: | Article |
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MDPI AG
2021-05-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/26/9/2715 |
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| author | Miao Tian Zhonglong Lin Xu Wang Jing Yang Wentao Zhao Hongmei Lu Zhimin Zhang Yi Chen |
| author_facet | Miao Tian Zhonglong Lin Xu Wang Jing Yang Wentao Zhao Hongmei Lu Zhimin Zhang Yi Chen |
| author_sort | Miao Tian |
| collection | DOAJ |
| description | Untargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics. |
| first_indexed | 2024-03-10T11:41:31Z |
| format | Article |
| id | doaj.art-8662c15e5e40490893ae8af1f293d265 |
| institution | Directory Open Access Journal |
| issn | 1420-3049 |
| language | English |
| last_indexed | 2024-03-10T11:41:31Z |
| publishDate | 2021-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj.art-8662c15e5e40490893ae8af1f293d2652023-11-21T18:27:19ZengMDPI AGMolecules1420-30492021-05-01269271510.3390/molecules26092715Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted MetabolomicsMiao Tian0Zhonglong Lin1Xu Wang2Jing Yang3Wentao Zhao4Hongmei Lu5Zhimin Zhang6Yi Chen7College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, ChinaYunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, ChinaShanghai New Tobacco Product Research Institute Limited Company, Shanghai 200082, ChinaShanghai New Tobacco Product Research Institute Limited Company, Shanghai 200082, ChinaShanghai New Tobacco Product Research Institute Limited Company, Shanghai 200082, ChinaCollege of Chemistry and Chemical Engineering, Central South University, Changsha 410083, ChinaCollege of Chemistry and Chemical Engineering, Central South University, Changsha 410083, ChinaYunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, ChinaUntargeted metabolomics based on liquid chromatography coupled with mass spectrometry (LC–MS) can detect thousands of features in samples and produce highly complex datasets. The accurate extraction of meaningful features and the building of discriminant models are two crucial steps in the data analysis pipeline of untargeted metabolomics. In this study, pure ion chromatograms were extracted from a liquor dataset and left-sided colon cancer (LCC) dataset by K-means-clustering-based Pure Ion Chromatogram extraction method version 2.0 (KPIC2). Then, the nonlinear low-dimensional embedding by uniform manifold approximation and projection (UMAP) showed the separation of samples from different groups in reduced dimensions. The discriminant models were established by extreme gradient boosting (XGBoost) based on the features extracted by KPIC2. Results showed that features extracted by KPIC2 achieved 100% classification accuracy on the test sets of the liquor dataset and the LCC dataset, which demonstrated the rationality of the XGBoost model based on KPIC2 compared with the results of XCMS (92% and 96% for liquor and LCC datasets respectively). Finally, XGBoost can achieve better performance than the linear method and traditional nonlinear modeling methods on these datasets. UMAP and XGBoost are integrated into KPIC2 package to extend its performance in complex situations, which are not only able to effectively process nonlinear dataset but also can greatly improve the accuracy of data analysis in non-target metabolomics.https://www.mdpi.com/1420-3049/26/9/2715Pure Ion ChromatogramUMAPXGBoostKPIC2LC–MS |
| spellingShingle | Miao Tian Zhonglong Lin Xu Wang Jing Yang Wentao Zhao Hongmei Lu Zhimin Zhang Yi Chen Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics Molecules Pure Ion Chromatogram UMAP XGBoost KPIC2 LC–MS |
| title | Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics |
| title_full | Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics |
| title_fullStr | Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics |
| title_full_unstemmed | Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics |
| title_short | Pure Ion Chromatograms Combined with Advanced Machine Learning Methods Improve Accuracy of Discriminant Models in LC–MS-Based Untargeted Metabolomics |
| title_sort | pure ion chromatograms combined with advanced machine learning methods improve accuracy of discriminant models in lc ms based untargeted metabolomics |
| topic | Pure Ion Chromatogram UMAP XGBoost KPIC2 LC–MS |
| url | https://www.mdpi.com/1420-3049/26/9/2715 |
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