Dec 22, 2019

BayesMetab: treatment of missing values in metabolomic studies using a Bayesian modeling approach

BMC Bioinformatics
Jasmit ShahJeremy Gaskins

Abstract

With the rise of metabolomics, the development of methods to address analytical challenges in the analysis of metabolomics data is of great importance. Missing values (MVs) are pervasive, yet the treatment of MVs can have a substantial impact on downstream statistical analyses. The MVs problem in metabolomics is quite challenging and can arise because the metabolite is not biologically present in the sample, or is present in the sample but at a concentration below the lower limit of detection (LOD), or is present in the sample but undetected due to technical issues related to sample pre-processing steps. The former is considered missing not at random (MNAR) while the latter is an example of missing at random (MAR). Typically, such MVs are substituted by a minimum value, which may lead to severely biased results in downstream analyses. We develop a Bayesian model, called BayesMetab, that systematically accounts for missing values based on a Markov chain Monte Carlo (MCMC) algorithm that incorporates data augmentation by allowing MVs to be due to either truncation below the LOD or other technical reasons unrelated to its abundance. Based on a variety of performance metrics (power for detecting differential abundance, area under t...Continue Reading

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Mentioned in this Paper

Simulation
Tooth Loss
Randomization
Finding
Metabolomics
Analysis
Protein Truncation
Myocardial Infarction
Monocyte-specific esterase
Metabolite

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