使用 R 分析液相色谱-质谱代谢组学数据的协议，以了解代谢物如何影响疾病。
Protocol for analysis of liquid chromatography-mass spectrometry metabolomics data using R to understand how metabolites affect disease.
机构信息
Department 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.
Department 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.
出版信息
STAR Protoc. 2023 Mar 17;4(1):102137. doi: 10.1016/j.xpro.2023.102137. Epub 2023 Feb 27.
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)..
基于液相色谱-质谱联用的代谢组学广泛应用于疾病预测的前瞻性病例对照研究。鉴于所涉及的大量临床和代谢组学数据，数据集成和分析对于提供对疾病的准确理解至关重要。我们提供了一种全面的分析方法来探索临床危险因素、代谢物和疾病之间的关联。我们描述了执行 Spearman 相关性、条件逻辑回归、因果中介和方差划分的步骤，以研究代谢物对疾病的潜在影响。有关本协议使用和执行的完整详细信息，请参阅 Wang 等人。（2022 年）。
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