ImpLiMet

ImpLiMet imputes missing values in high-throughput lipidomics and metabolomics datasets using eight different methods and suggests an optimal approach through grid search-based investigation of error rates across three missingness data simulations.

Key Features:

• Multiple Imputation Methods: The platform implements eight different imputation methods for missing data imputation.
• Optimal Method Selection: A grid search-based investigation of the error rate for imputation across three missingness data simulations suggests the optimal imputation solution for the user's dataset.
• Visual Assessment: The effect of imputation can be assessed through histogram, kurtosis and skewness analyses, as well as principal component analysis (PCA) comparing the impact of the chosen imputation method on the distribution and overall behaviour of the data.
• Missingness Pattern Support: The tool addresses three missingness patterns: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR).

Scientific Applications:

• Lipidomics Data Analysis: ImpLiMet enables imputation of missing values in lipidomics datasets to support downstream multivariate and machine learning analyses.
• Metabolomics Data Analysis: ImpLiMet enables imputation of missing values in metabolomics datasets to support downstream multivariate and machine learning analyses.
• Imputation Method Optimization: The platform identifies the optimal imputation solution based on the type of data, the cause of the missing data, and the nature of relationships between the missing and observed data.

Methodology:

ImpLiMet uses a grid search-based investigation of the error rate for imputation across three missingness data simulations to suggest the optimal imputation solution. The effect of imputation is assessed through histogram, kurtosis and skewness analyses, as well as principal component analysis (PCA).