Imputation for Lipidomics and Metabolomics (ImpLiMet): Online optimization and method selection for missing data imputation

Motivation Missing values are often unavoidable in modern 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. Three missingness patterns have been conceptualized: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Each describes unique dependencies between the missing and observed data. As a result, the optimal imputation method for each dataset depends on the type of data, the cause of the missing data, and the nature of relationships between the missing and observed data. The challenge is to identify the optimal imputation solution for a given dataset. Results Imputation for Lipidomics and Metabolomics (ImpLiMet) is a user-friendly UI-platform that enables users to impute missing data using eight different methods. Using the user’s dataset, ImpLiMet can then suggest the optimal imputation solution through a grid search-based investigation of the error rate for imputation across three missingness data simulations. The effect of imputation can be visually assessed by principal component analysis (PCA) comparing the impact of removing all features and samples with missing data with the chosen imputation method. Availability and implementation ImpLiMet is freely available at https://complimet.ca/shiny/implimet/ with software accessible at https://github.com/complimet/ImpLiMet