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

ABSTRACT:

Background

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,

SUBMITTER: Shah J 

PROVIDER: S-EPMC6923847 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature