Mechanism-aware imputation: a two-step approach in handling missing values in metabolomics.

ABSTRACT: When analyzing large datasets from high-throughput technologies, researchers often encounter missing quantitative measurements, which are particularly frequent in metabolomics datasets. Metabolomics, the comprehensive profiling of metabolite abundances, are typically measured using mass spectrometry technologies that often introduce missingness via multiple mechanisms: (1) the metabolite signal may be smaller than the instrument limit of detection; (2) the conditions under which the data are collected and processed may lead to missing values; (3) missing values can be introduced randomly. Missingness resulting from mechanism (1) would be classified as Missing Not At Random (MNAR), that from mechanism (2) would be Missing At Random (MAR), and that from mechanism (3) would be classified as M

SUBMITTER: Dekermanjian JP 

PROVIDER: S-EPMC9109373 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature