Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease

Abstract Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several repr...

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Autores principales:	Ling Hao, Jingxin Wang, David Page, Sanjay Asthana, Henrik Zetterberg, Cynthia Carlsson, Ozioma C. Okonkwo, Lingjun Li
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/de4441af776c441d9c4a167986d4be52
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spelling	oai:doaj.org-article:de4441af776c441d9c4a167986d4be522021-12-02T11:40:37ZComparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease10.1038/s41598-018-27031-x2045-2322https://doaj.org/article/de4441af776c441d9c4a167986d4be522018-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-27031-xhttps://doaj.org/toc/2045-2322Abstract Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer’s disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.Ling HaoJingxin WangDavid PageSanjay AsthanaHenrik ZetterbergCynthia CarlssonOzioma C. OkonkwoLingjun LiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-10 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ling Hao Jingxin Wang David Page Sanjay Asthana Henrik Zetterberg Cynthia Carlsson Ozioma C. Okonkwo Lingjun Li Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease
description	Abstract Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis. However, systematic comparison of different metabolomics software tools has rarely been conducted. In this study, several representative software packages were comparatively evaluated throughout the entire pipeline of metabolomics data analysis, including data processing, statistical analysis, feature selection, metabolite identification, pathway analysis, and classification model construction. LC-MS-based metabolomics was applied to preclinical Alzheimer’s disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. A hybrid method combining statistical test and support vector machine feature selection was employed to screen key metabolites, achieving a complementary selection of candidate biomarkers from three software packages. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. Overall, our study demonstrated a systematic evaluation of different MS-based metabolomics software packages for the entire data analysis pipeline which was applied to the candidate biomarker discovery of preclinical AD.
format	article
author	Ling Hao Jingxin Wang David Page Sanjay Asthana Henrik Zetterberg Cynthia Carlsson Ozioma C. Okonkwo Lingjun Li
author_facet	Ling Hao Jingxin Wang David Page Sanjay Asthana Henrik Zetterberg Cynthia Carlsson Ozioma C. Okonkwo Lingjun Li
author_sort	Ling Hao
title	Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease
title_short	Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease
title_full	Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease
title_fullStr	Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease
title_full_unstemmed	Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease
title_sort	comparative evaluation of ms-based metabolomics software and its application to preclinical alzheimer’s disease
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/de4441af776c441d9c4a167986d4be52
work_keys_str_mv	AT linghao comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT jingxinwang comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT davidpage comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT sanjayasthana comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT henrikzetterberg comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT cynthiacarlsson comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT oziomacokonkwo comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease AT lingjunli comparativeevaluationofmsbasedmetabolomicssoftwareanditsapplicationtopreclinicalalzheimersdisease
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Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer’s Disease

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