{"title":"Norm ISWSVR提高了大规模靶向量化代谢组学数据的可重复性和准确性。","authors":"Jinpeng Bai, Chenxi Li, Mingmin Qian, Xian Ding, Qian Li, Yanhua Chen, Zhaoying Wang, Zeper Abliz","doi":"10.1021/jasms.4c00467","DOIUrl":null,"url":null,"abstract":"<p><p>Targeted quantification metabolomics provides dynamic insights across various domains within the life sciences. Nevertheless, maintaining high-quality data obtained through liquid chromatography-mass spectrometry presents ongoing challenges. It is essential to develop normalization methods to correct for unwanted variations in metabolomic profiling such as batch effects and analytical drift. In this study, we assessed the normalization efficacy of Norm ISWSVR in targeted quantification metabolomics by comparing it with IS normalization and SERRF normalization. Consequently, Norm ISWSVR demonstrated exceptional efficacy in mitigating batch effects and reducing the relative standard deviation of quality control samples, in addition to correcting signal drift. Following normalization with Norm ISWSVR, the number of metabolites suitable for quantification increased with high precision. Collectively, Norm ISWSVR proves to be a robust and reliable method for enhancing data quality in targeted metabolomics, establishing itself as a promising approach for metabolomics research.</p>","PeriodicalId":672,"journal":{"name":"Journal of the American Society for Mass Spectrometry","volume":" ","pages":""},"PeriodicalIF":3.1000,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Norm ISWSVR Enhanced Data Repeatability and Accuracy in Large-Scale Targeted Quantification Metabolomics.\",\"authors\":\"Jinpeng Bai, Chenxi Li, Mingmin Qian, Xian Ding, Qian Li, Yanhua Chen, Zhaoying Wang, Zeper Abliz\",\"doi\":\"10.1021/jasms.4c00467\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Targeted quantification metabolomics provides dynamic insights across various domains within the life sciences. Nevertheless, maintaining high-quality data obtained through liquid chromatography-mass spectrometry presents ongoing challenges. It is essential to develop normalization methods to correct for unwanted variations in metabolomic profiling such as batch effects and analytical drift. In this study, we assessed the normalization efficacy of Norm ISWSVR in targeted quantification metabolomics by comparing it with IS normalization and SERRF normalization. Consequently, Norm ISWSVR demonstrated exceptional efficacy in mitigating batch effects and reducing the relative standard deviation of quality control samples, in addition to correcting signal drift. Following normalization with Norm ISWSVR, the number of metabolites suitable for quantification increased with high precision. Collectively, Norm ISWSVR proves to be a robust and reliable method for enhancing data quality in targeted metabolomics, establishing itself as a promising approach for metabolomics research.</p>\",\"PeriodicalId\":672,\"journal\":{\"name\":\"Journal of the American Society for Mass Spectrometry\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2025-04-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of the American Society for Mass Spectrometry\",\"FirstCategoryId\":\"92\",\"ListUrlMain\":\"https://doi.org/10.1021/jasms.4c00467\",\"RegionNum\":2,\"RegionCategory\":\"化学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of the American Society for Mass Spectrometry","FirstCategoryId":"92","ListUrlMain":"https://doi.org/10.1021/jasms.4c00467","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Norm ISWSVR Enhanced Data Repeatability and Accuracy in Large-Scale Targeted Quantification Metabolomics.
Targeted quantification metabolomics provides dynamic insights across various domains within the life sciences. Nevertheless, maintaining high-quality data obtained through liquid chromatography-mass spectrometry presents ongoing challenges. It is essential to develop normalization methods to correct for unwanted variations in metabolomic profiling such as batch effects and analytical drift. In this study, we assessed the normalization efficacy of Norm ISWSVR in targeted quantification metabolomics by comparing it with IS normalization and SERRF normalization. Consequently, Norm ISWSVR demonstrated exceptional efficacy in mitigating batch effects and reducing the relative standard deviation of quality control samples, in addition to correcting signal drift. Following normalization with Norm ISWSVR, the number of metabolites suitable for quantification increased with high precision. Collectively, Norm ISWSVR proves to be a robust and reliable method for enhancing data quality in targeted metabolomics, establishing itself as a promising approach for metabolomics research.
期刊介绍：
The Journal of the American Society for Mass Spectrometry presents research papers covering all aspects of mass spectrometry, incorporating coverage of fields of scientific inquiry in which mass spectrometry can play a role.
Comprehensive in scope, the journal publishes papers on both fundamentals and applications of mass spectrometry. Fundamental subjects include instrumentation principles, design, and demonstration, structures and chemical properties of gas-phase ions, studies of thermodynamic properties, ion spectroscopy, chemical kinetics, mechanisms of ionization, theories of ion fragmentation, cluster ions, and potential energy surfaces. In addition to full papers, the journal offers Communications, Application Notes, and Accounts and Perspectives
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
