Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies
Abstract
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...
Paper Details
Title
Characterization of missing values in untargeted MS-based metabolomics data and evaluation of missing data handling strategies
Published Date
Sep 20, 2018
Journal
Volume
14
Issue
10
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