Addressing Missing Data in GC x GC Metabolomics: Identifying Missingness Type and Evaluating the Impact of Imputation Methods on Experimental Replication

Missing data is a significant issue in metabolomics that is often neglected when conducting data preprocessing, particularly when it comes to imputation. This can have serious implications for downstream statistical analyses and lead to misleading or uninterpretable inferences. In this study, we aim to identify the primary types of missingness that affect untargeted metabolomics data and compare strategies for imputation using two real-world comprehensive two-dimensional gas chromatography (GC X GC) data sets. We also present these goals in the context of experimental replication whereby imputation is conducted in a within-replicate-based fashion-the first description and evaluation of this strategy-and introduce an R package Metablmpute to carry out these analyses. Our results conclude that, in these two GC x GC data sets, missingness was most likely of the missing at-random MAR and missing not-at-random (MNAR) types as opposed to missing completely at-random (MCAR). Gibbs sampler imputation and Random Forest gave the best results when imputing MAR and MNAR compared against single-value imputation (zero, minimum, mean, median, and halfminimum) and other more sophisticated approaches (Bayesian principal component analysis and quantile regression imputation for left-censored data). When samples are replicated, within-replicate imputation approaches led to an increase in the reproducibility of peak quantification compared to imputation that ignores replication, suggesting that imputing with respect to replication may preserve potentially important features in downstream analyses for biomarker discovery.

Automated summary

Missing data is a significant issue in metabolomics, and this study identifies the primary types of missingness and compares strategies for imputation using real-world data sets. The study introduces a within-replicate imputation approach and an R package for analysis. The results show the effectiveness of Gibbs sampler imputation and Random Forest for handling missing data types and suggest that within-replicate imputation improves peak quantification reproducibility for biomarker discovery.