imputomics

imputomics implements a suite of missing value imputation algorithms to restore completeness in mass spectrometry-derived metabolomics datasets for robust statistical and machine learning analyses.

Key Features:

• Extensive Algorithm Support: Integrates 41 of 52 identified Missing Value Imputation Algorithms (MVIAs) and includes random imputation as a baseline model.
• Algorithm Selection: Provides functionality to select MVIAs based on performance metrics or execution time.

Scientific Applications:

• Data Completeness: Imputes missing values in mass spectrometry-derived metabolomics datasets to improve dataset completeness.
• Statistical Modeling: Enables more reliable downstream statistical analyses by supplying imputed data for model fitting.
• Machine Learning: Supports machine learning workflows by providing complete feature matrices and increasing statistical power.
• Exploratory Analysis and Interpretation: Facilitates exploratory analysis and biological interpretation by reducing biases introduced by missing data.

Methodology:

Imputomics integrates 41 out of 52 identified MVIAs plus random imputation as a baseline and implements algorithm selection based on performance metrics or execution time.