MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data
Abstract Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolit...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-09-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-42395-5 |
| _version_ | 1797576757913583616 |
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| author | Xin Feng Zheyuan Dong Yingrui Li Qian Cheng Yongxian Xin Qiaolin Lu Ruihao Xin |
| author_facet | Xin Feng Zheyuan Dong Yingrui Li Qian Cheng Yongxian Xin Qiaolin Lu Ruihao Xin |
| author_sort | Xin Feng |
| collection | DOAJ |
| description | Abstract Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results. |
| first_indexed | 2024-03-10T21:58:17Z |
| format | Article |
| id | doaj.art-3c9cd269baf941f38eba631eb0c3f6f6 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-10T21:58:17Z |
| publishDate | 2023-09-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-3c9cd269baf941f38eba631eb0c3f6f62023-11-19T13:01:59ZengNature PortfolioScientific Reports2045-23222023-09-0113111210.1038/s41598-023-42395-5MSFC: a new feature construction method for accurate diagnosis of mass spectrometry dataXin Feng0Zheyuan Dong1Yingrui Li2Qian Cheng3Yongxian Xin4Qiaolin Lu5Ruihao Xin6School of Science, Jilin Institute of Chemical TechnologyCollege of Information and Control Engineering, Jilin Institute of Chemical TechnologyCollege of Information and Control Engineering, Jilin Institute of Chemical TechnologyCollege of Information and Control Engineering, Jilin Institute of Chemical TechnologyCollege of Business and Economics, Australian National UniversitySchool of Artificial Intelligence, Jilin UniversityCollege of Information and Control Engineering, Jilin Institute of Chemical TechnologyAbstract Mass spectrometry technology can realize dynamic detection of many complex matrix samples in a simple, rapid, compassionate, precise, and high-throughput manner and has become an indispensable tool in accurate diagnosis. The mass spectrometry data analysis is mainly to analyze all metabolites in the organism quantitatively and to find the relative relationship between metabolites and physiological and pathological changes. A feature construction of mass spectrometry data (MSFS) method is proposed to construct the features of the original mass spectrometry data, so as to reduce the noise in the mass spectrometry data, reduce the redundancy of the original data and improve the information content of the data. Chi-square test is used to select the optimal non-redundant feature subset from high-dimensional features. And the optimal feature subset is visually analyzed and corresponds to the original mass spectrum interval. Training in 10 kinds of supervised learning models, and evaluating the classification effect of the models through various evaluation indexes. Taking two public mass spectrometry datasets as examples, the feasibility of the method proposed in this paper is verified. In the coronary heart disease dataset, during the identification process of mixed batch samples, the classification accuracy on the test set reached 1.000; During the recognition process, the classification accuracy on the test set advanced to 0.979. On the colorectal liver metastases data set, the classification accuracy on the test set reached 1.000. This paper attempts to use a new raw mass spectrometry data preprocessing method to realize the alignment operation of the raw mass spectrometry data, which significantly improves the classification accuracy and provides another new idea for mass spectrometry data analysis. Compared with MetaboAnalyst software and existing experimental results, the method proposed in this paper has obtained better classification results.https://doi.org/10.1038/s41598-023-42395-5 |
| spellingShingle | Xin Feng Zheyuan Dong Yingrui Li Qian Cheng Yongxian Xin Qiaolin Lu Ruihao Xin MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data Scientific Reports |
| title | MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data |
| title_full | MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data |
| title_fullStr | MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data |
| title_full_unstemmed | MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data |
| title_short | MSFC: a new feature construction method for accurate diagnosis of mass spectrometry data |
| title_sort | msfc a new feature construction method for accurate diagnosis of mass spectrometry data |
| url | https://doi.org/10.1038/s41598-023-42395-5 |
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