A Workflow for Missing Values Imputation of Untargeted Metabolomics Data

A Workflow for Missing Values Imputation of Untargeted Metabolomics Data

Metabolomics studies have seen a steady growth due to the development and implementation of affordable and high-quality metabolomics platforms. In large metabolite panels, measurement values are frequently missing and, if neglected or sub-optimally imputed, can cause biased study results. We provide...

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Bibliographic Details
Main Authors: Tariq Faquih, Maarten van Smeden, Jiao Luo, Saskia le Cessie, Gabi Kastenmüller, Jan Krumsiek, Raymond Noordam, Diana van Heemst, Frits R. Rosendaal, Astrid van Hylckama Vlieg, Ko Willems van Dijk, Dennis O. Mook-Kanamori
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Metabolites
Subjects:
imputation
multiple imputation using chained equations
k-nearest neighbors
untargeted metabolomics
metabolon
simulation
Online Access:https://www.mdpi.com/2218-1989/10/12/486

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https://www.mdpi.com/2218-1989/10/12/486

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