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Related Concept Videos

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Inductively coupled plasma–mass spectrometry (ICP–MS) is a highly selective and sensitive technique for accurate elemental analysis. Though the analysis of ICP–MS mass spectra is comparatively straightforward, it is affected by spectroscopic and non-spectroscopic interferences. Spectroscopic interferences arise when the plasma contains ionic species with an m/z value the same as the analyte ion. Spectroscopic interference can be categorized as isobaric, polyatomic ions, and...
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To Impute or Not To Impute in Untargeted Metabolomics─That is the Compositional Question.

Dennis D Krutkin1,2, Sydney Thomas3, Simone Zuffa3,4

  • 1School of Biological Sciences, University of California San Diego, La Jolla, California 92037, United States.

Journal of the American Society for Mass Spectrometry
|February 26, 2025
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Metabolomic data imputation using k-Nearest Neighbors (kNN) and Random Forest (RF) is unreliable for missing not at random (MNAR) data. Caution is advised with imputation, especially when missing data types are unknown.

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Area of Science:

  • Metabolomics
  • Bioinformatics
  • Statistical Analysis

Background:

  • Untargeted metabolomics generates large datasets with missing values, potentially biasing results.
  • Missing values can arise from biological or technical issues, impacting statistical validity.
  • Imputation methods like kNN and RF are common but their accuracy depends on missing data types (MCAR, MNAR).

Purpose of the Study:

  • To evaluate the accuracy of kNN and RF imputation methods under varying degrees and types of missing data in metabolomics.
  • To assess the influence of compositional data analysis (CoDA) on data interpretation.
  • To provide guidance on the cautious use of imputation in metabolomic studies.

Main Methods:

  • Utilized two datasets: a targeted metabolomic dataset with spiked standards and an untargeted metabolomic dataset.
  • Assessed kNN and RF imputation accuracy based on the proportion and type (MCAR, MNAR) of missing data.
  • Investigated the impact of compositional data approaches (CoDA), including CLR transformation.

Main Results:

  • kNN and RF imputation accuracy decreased with higher proportions of missing data.
  • These imputation methods failed to accurately handle MNAR data, producing erroneous values.
  • The extent of missing data significantly impacted imputation accuracy and subsequent data interpretation.
  • CoDA methods were also affected by the presence and handling of missing values.

Conclusions:

  • Imputation methods like kNN and RF should be used with extreme caution in metabolomics, particularly with MNAR data.
  • The proportion of missing data strongly influences imputation reliability and biological interpretation.
  • Researchers must carefully consider the type of missingness and its potential impact before applying imputation techniques.