Pretreating and normalizing metabolomics data for statistical analysis  

作　　者：Jun Sun Yinglin Xia 

机构地区：[1]Division of Gastroenterology and Hepatology,Department of Medicine,Department of Microbiology/Immunology,UIC Cancer Center,University of Illinois Chicago,Jesse Brown VA Medical Center Chicago(537),Chicago,IL 60612,USA [2]Division of Gastroenterology and Hepatology,Department of Medicine,University of Illinois Chicago,Chicago,IL 60612,USA

出　　处：《Genes & Diseases》2024年第3期188-205,共18页基因与疾病（英文）

基　　金：supported by the Crohn's&Colitis Foundation Senior Research Award(No.902766 to J.S.);The National Institute of Diabetes and Digestive and Kidney Diseases(No.R01DK105118-01 and R01DK114126 to J.S.);United States Department of Defense Congressionally Directed Medical Research Programs(No.BC191198 to J.S.);VA Merit Award BX-19-00 to J.S.

摘　　要：Metabolomics as a research field and a set of techniques is to study the entire small molecules in biological samples.Metabolomics is emerging as a powerful tool generally for pre-cision medicine.Particularly,integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease.However,metabo-lomics data are very complicated.Preprocessing/pretreating and normalizing procedures on metabolomics data are usually required before statistical analysis.In this review article,we comprehensively review various methods that are used to preprocess and pretreat metabolo-mics data,including MS-based data and NMR-based data preprocessing,dealing with zero and/or missing values and detecting outliers,data normalization,data centering and scaling,data transformation.We discuss the advantages and limitations of each method.The choice for a suitable preprocessing method is determined by the biological hypothesis,the characteristics of the data set,and the selected statistical data analysis method.We then provide the perspective of their applications in the microbiome and metabolome research.

关 键 词：Data centering and scaling Data normalization Data transformation Missing values MS-Baseddata preprocessing NMRData preprocessing OUTLIERS Preprocessing/pretreatment 

分 类 号：R339.6[医药卫生—人体生理学]