Selected Publication


Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies

The publication made it to the cover page of Metabolomics.

Untargeted MS-based metabolomics data often contain missing values that reduce statistical power and introduce bias in biochemical studies. We investigated patterns of missing data and evaluated 31 imputation methods based on both a real metabolomics experiment from the German KORA F4 cohort and artificial data from various simulation frameworks. Based on our results, we recommend that KNN-based imputation is performed on observations with variable pre-selection.

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