Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies
Kieu Trinh; Simone Wahl; Johannes Raffler; Sophie Molnos; Michael Laimighofer; Jerzy Adamski; Karsten Suhre; Konstantin Strauch · 2018 · Metabolomics
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The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
BACKGROUND: Untargeted mass spectrometry (MS)-based metabolomics data often contain missing values that reduce statistical power and can introduce bias in biomedical studies. However, a systematic assessment of the various sources of missing values and strategies to handle these data has received little attention. Missing data can occur systematically, e.g. from run day-dependent effects due to limits of detection (LOD); or it can be random as, for instance, a consequence of sample preparation. METHODS: We investigated patterns of missing data in an MS-based metabolomics experiment of serum sa
Abstract by Kieu Trinh; Simone Wahl; Johannes Raffler; Sophie Molnos; Michael Laimighofer; Jerzy Adamski; Karsten Suhre; Konstantin Strauch, Metabolomics (2018) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1007/s11306-018-1420-2