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 precision medicine. Particularly

integration of microbiome and metabolome has revealed the mechanism and functionality of microbiome in human health and disease. However

metabolomics 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 metabolomics 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.