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Missing values exist widely in mass-spectrometry (MS) based metabolomics data. Various methods have been applied for handling missing values, but the selection can significantly affect following data analyses. Typically, there are three types of missing values, missing not at random (MNAR), missing at random (MAR), and missing completely at random (MCAR).
(Wei et al., 2018) Missing Value Imputation Approach for Mass Spectrometry-based Metabolomics Data. Sci Rep.