DICP OpenIR
Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study
Ju, Ran1; Liu, Xinyu2; Zheng, Fujian2; Zhao, Xinjie2; Lu, Xin2; Zeng, Zhongda2,3; Lin, Xiaohui1; Xu, Guowang2
Corresponding AuthorZeng, Zhongda(ZENG@chemdatasolution.com) ; Lin, Xiaohui(datas@dlut.edu.cn) ; Xu, Guowang(xugw@dicp.ac.cn)
KeywordHigh-resolution mass spectrometry Metabolomics False positive features Data quality
Source PublicationANALYTICA CHIMICA ACTA
2019-08-27
ISSN0003-2670
DOI10.1016/j.aca.2019.04.011
Volume1067Pages:79-87
Funding ProjectNational Natural Science Foundation of China[81472374] ; National Natural Science Foundation of China[21375011] ; National Key Research and Development Program of China[2017YFC0906900] ; innovation program of science and research from the DICP, CAS[DICP TMSR201601]
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS
WOS SubjectChemistry, Analytical
WOS Research AreaChemistry
WOS KeywordLARGE-SCALE ; QUANTIFICATION ; IDENTIFICATION ; ANNOTATION ; STRATEGIES ; ALIGNMENT
AbstractIn metabolomics research, false positive features from non-sample sources and noises usually exist in the peak table, they will make the results of screening differential metabolites or biomarkers unreliable. In this study, a method to remove false positive features (rFPF) was developed to improve the quality of the peak table. rFPF recognizes real peak profiles based on the information entropy and statistical correlation, and eliminates false positive features from non-sample sources and noises. A standard mixture with 42 standards (14 isotopic labeled internal standards and 28 common standards) and a urine sample were applied to evaluate the effectiveness of the rFPF method. The analysis results of metabolite standards showed that more than 92% false positive features were removed by rFPF, but target standards completely remained. The analysis results of urine sample showed that the number of features was significantly reduced from 7182 to 2522. Interestingly, 98% of the identified metabolites remained after removing false positive features. The proposed rFPF shows great prospects as a new data handling method for metabolomics studies. (C) 2019 Elsevier B.V. All rights reserved.
Language英语
Funding OrganizationNational Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS ; National Natural Science Foundation of China ; National Natural Science Foundation of China ; National Key Research and Development Program of China ; National Key Research and Development Program of China ; innovation program of science and research from the DICP, CAS ; innovation program of science and research from the DICP, CAS
WOS IDWOS:000466150300008
PublisherELSEVIER SCIENCE BV
Citation statistics
Document Type期刊论文
Identifierhttp://cas-ir.dicp.ac.cn/handle/321008/165513
Collection中国科学院大连化学物理研究所
Corresponding AuthorZeng, Zhongda; Lin, Xiaohui; Xu, Guowang
Affiliation1.Dalian Univ Technol, Sch Comp Sci & Technol, Dalian 116024, Peoples R China
2.Chinese Acad Sci, Dalian Inst Chem Phys, CAS Key Lab Separat Sci Analyt Chem, Dalian 116023, Peoples R China
3.Dalian ChemDataSolut Informat Technol Co Ltd, Dalian 116023, Peoples R China
Recommended Citation
GB/T 7714
Ju, Ran,Liu, Xinyu,Zheng, Fujian,et al. Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study[J]. ANALYTICA CHIMICA ACTA,2019,1067:79-87.
APA Ju, Ran.,Liu, Xinyu.,Zheng, Fujian.,Zhao, Xinjie.,Lu, Xin.,...&Xu, Guowang.(2019).Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study.ANALYTICA CHIMICA ACTA,1067,79-87.
MLA Ju, Ran,et al."Removal of false positive features to generate authentic peak table for high-resolution mass spectrometry-based metabolomics study".ANALYTICA CHIMICA ACTA 1067(2019):79-87.
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