Hao, L;
Wang, J;
Page, D;
Asthana, S;
Zetterberg, H;
Carlsson, C;
Okonkwo, OC;
(2018)
Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease.
Scientific Reports
, 8
, Article 9291. 10.1038/s41598-018-27031-x.
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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.
| Type: | Article |
|---|---|
| Title: | Comparative Evaluation of MS-based Metabolomics Software and Its Application to Preclinical Alzheimer's Disease |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1038/s41598-018-27031-x |
| Publisher version: | http://dx.doi.org/10.1038/s41598-018-27031-x |
| Language: | English |
| Additional information: | Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MILD COGNITIVE IMPAIRMENT, SUPPORT VECTOR MACHINES, MASS-SPECTROMETRY DATA, DATABASE, TOOLS, IDENTIFICATION, METABOLITES, PROTEOMICS, DISCOVERY, PLATFORM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10056828 |
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