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Related Experiment Video

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Large Scale Non-targeted Metabolomic Profiling of Serum by Ultra Performance Liquid Chromatography-Mass Spectrometry UPLC-MS
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Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study.

Marietta Kokla1, Jyrki Virtanen2, Marjukka Kolehmainen2,3

  • 1Institute of Public Health and Clinical Nutrition, University of Eastern Finland, Kuopio Campus, P.O. Box 1627, FI-70211, Kuopio, Finland. marietta.kokla@uef.fi.

BMC Bioinformatics
|October 12, 2019
PubMed
Summary

Accurate metabolomics data analysis requires effective imputation of missing values. Random forest imputation is recommended for its superior performance across various missingness types and rates in LC-MS data.

Keywords:
High dimensional dataImputationMARMCARMNARMetabolomicsMissing valuesRF

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Data Science

Background:

  • Liquid chromatography-mass spectrometry (LC-MS) enables comprehensive molecular profiling.
  • Non-targeted metabolomics generates large datasets with frequent missing values.
  • Missing data hinders accurate statistical analysis and requires robust imputation strategies.

Purpose of the Study:

  • To evaluate the performance of nine imputation methods for missing data in metabolomics.
  • To identify the most accurate imputation strategy across different missingness mechanisms and rates.

Main Methods:

  • Assessed nine imputation techniques using metabolomics datasets.
  • Introduced missing values at varying percentages and origins (MAR, MCAR, MNAR).
  • Evaluated imputation performance using Normalized Root Mean Squared Error (NRMSE) over 100 repetitions.

Main Results:

  • Random forest (RF) demonstrated the lowest NRMSE for Missing at Random (MAR) and Missing Completely at Random (MCAR) data.
  • Minimum value imputation was optimal for Missing Not at Random (MNAR) data.
  • RF consistently provided the most accurate imputation across all tested scenarios and missing data origins.

Conclusions:

  • The type and rate of missing data significantly impact imputation method performance.
  • Random forest imputation is the most accurate and recommended method for metabolomics data, especially when missingness types are unknown.
  • Effective imputation is crucial for reliable statistical analysis of LC-MS metabolomics data.