![]() GC-MS is highly reproducible and well suited for thermal stable and volatile compounds or compounds that can be derivatized to be volatile. LC-MS does not require chemical derivatization and offers large sample loading capacities for small molecules with high degree of structural diversity. Different MS-based analytical platforms provide complementary coverage of the complex metabolome in a given biological sample. In particular, hyphenated MS platforms can provide reproducible detection and sensitive measurements for thousands of metabolites in complex samples by online coupling with separation systems prior to MS analysis, including liquid chromatography (LC), gas chromatography (GC), and capillary electrophoresis (CE) 7, 8, 9, 10, 11, 12. ![]() Mass spectrometry (MS) has become a foremost technology for the study of metabolites and their dynamic alterations involved in various diseases 3, 4, 5, 6. Characterization of the metabolome offers a wealth of information regarding both enzymatic activities and environmental factors 1, 2. Metabolomics is defined as the systematic study of small molecules profile within a biological system. 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. Machine learning classification using candidate biomarkers generated highly accurate and predictive models to classify patients into preclinical AD or control category. 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. All three software packages, XCMS Online, SIEVE, and Compound Discoverer, provided consistent and reproducible data processing results. LC-MS-based metabolomics was applied to preclinical Alzheimer’s disease (AD) using a small cohort of human cerebrospinal fluid (CSF) samples (N = 30). 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. However, systematic comparison of different metabolomics software tools has rarely been conducted. Mass spectrometry-based metabolomics has undergone significant progresses in the past decade, with a variety of software packages being developed for data analysis.
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