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

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 samples from the German KORA F4 cohort (n = 1750). We then evaluated 31 imputation methods in a simulation framework and biologically validated the results by applying all imputation approa

SUBMITTER: Do KT 

PROVIDER: S-EPMC6153696 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature