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Updated: Nov 26, 2025

Characterization of Neuronal Lysosome Interactome with Proximity Labeling Proteomics
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Missing value imputation in proximity extension assay-based targeted proteomics data.

Michael Lenz1,2, Andreas Schulz2, Thomas Koeck2,3

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Summary
This summary is machine-generated.

For targeted proteomics, the GSimp imputation method better handles missing data than missForest. Both methods show good accuracy for plasma protein quantification, but GSimp introduces less bias in analyses.

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Area of Science:

  • Biochemistry
  • Proteomics
  • Data Science

Background:

  • Targeted proteomics offers sensitive plasma protein quantification.
  • Missing values in proteomics data complicate multivariate analysis.
  • Effective imputation methods are crucial for accurate data interpretation.

Purpose of the Study:

  • To benchmark imputation methods for missing values in targeted proteomics.
  • To compare the performance of 'missForest' and 'GSimp' for handling random missing data.
  • To evaluate imputation accuracy for inflammation-related proteins in a venous thromboembolism cohort.

Main Methods:

  • Assessed 'missForest' and 'GSimp' imputation methods.
  • Used antibody-based proximity extension assays for targeted proteomics.
  • Compared imputed values with remeasured concentrations for 91 proteins in 86 samples.

Main Results:

  • GSimp showed higher median Pearson correlation (71.6%) than missForest (69.0%) between imputed and remeasured protein values.
  • MissForest resulted in significantly greater variance reduction (25.3% vs. 68.6%) and bias.
  • Imputation accuracy varied significantly across the 91 proteins, influenced by signal-to-noise ratio and protein overlap.

Conclusions:

  • GSimp outperformed missForest in imputing missing values for targeted proteomics data.
  • Both methods demonstrate overall good imputation accuracy, but GSimp is preferred due to lower bias.
  • Protein-specific factors significantly impact imputation accuracy, necessitating careful consideration in data analysis.