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The Comprehensive Assessment of the Normalization Methods Applied to Metabolomics Data

The major bottlenecks of metabolomics are data pre-processing and analysis. Normalization is usually utilized to remove the different types of unwanted systematic variations in high-throughput mass spectrometry-based metabolomics data. Many recent researches compared or evaluated normalization methods used in metabolomics almost based on only one single criterion, such as the normalization method's effect on classification. However, the performances of normalization methods varied wildly and relied on the natural of dataset. In this study, we evaluated systematically existing 12 sample-wise normalization methods from multiple perspectives. By applying multi-criteria, including method's capability of maximal variation between the inter-groups, method's effect on the differentially metabolic features and method's effect on the performance of classification, VSN normalization method performed almost consistently better than other methods. From our investigation, we suggested that researchers should take multiple criteria into consideration when selecting proper normalization methods to ensure the reliability of downstream analysis and experimental validation.