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Imputation for Lipidomics and Metabolomics
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Journal Article
Imputation for Lipidomics and Metabolomics
2025
Overview
Motivation: Missing values are prevalent in high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. The three missingness patterns, including missing completely at random, missing at random, and missing not at random, describe unique dependencies between the missing and observed data. The optimal imputation method for each dataset depends on the type of data, the cause of the missingness, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset. Availability and implementation: ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ and https://github.com/complimet/ImpLiMet.
Publisher
Oxford University Press
Subject
