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Protocol for analysis of liquid chromatography-mass spectrometry metabolomics data using R to understand how
Hong Lin1, Chunyan Hu1, Shuangyuan Wang1
1Department of Endocrine and Metabolic Diseases, Shanghai Institute of Endocrine and Metabolic Diseases, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Shanghai National Clinical Research Center for Endocrine and Metabolic Diseases, Key Laboratory for Endocrine and Metabolic Diseases of the National Health Commission of the PR China, Shanghai National Center for Translational Medicine, Ruijin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, China.
Abstract:
Liquid-chromatography-mass-spectrometry-based metabolomics is widely used in prospective case-control studies for disease prediction. Given the large amount of clinical and metabolomics data involved, data integration and analyses are crucial to provide an accurate understanding of the disease. We provide a comprehensive analysis approach to explore associations among clinical risk factors, metabolites, and disease. We describe steps for performing Spearman correlation, conditional logistic regression, casual mediation, and variance partitioning to investigate the potential effects of metabolites on disease. For complete details on the use and execution of this protocol, please refer to Wang et al. (2022).1.