Imputation of label-free quantitative mass spectrometry-based proteomics data using self-supervised deep learning

Henry Webel, Lili Niu, Annelaura Bach Nielsen, Marie Locard-Paulet, Matthias Mann, Lars Juhl Jensen and Simon Rasmussen ()
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Henry Webel: University of Copenhagen
Lili Niu: University of Copenhagen
Annelaura Bach Nielsen: University of Copenhagen
Marie Locard-Paulet: University of Copenhagen
Matthias Mann: University of Copenhagen
Lars Juhl Jensen: University of Copenhagen
Simon Rasmussen: University of Copenhagen

Nature Communications, 2024, vol. 15, issue 1, 1-15

Abstract: Abstract Imputation techniques provide means to replace missing measurements with a value and are used in almost all downstream analysis of mass spectrometry (MS) based proteomics data using label-free quantification (LFQ). Here we demonstrate how collaborative filtering, denoising autoencoders, and variational autoencoders can impute missing values in the context of LFQ at different levels. We applied our method, proteomics imputation modeling mass spectrometry (PIMMS), to an alcohol-related liver disease (ALD) cohort with blood plasma proteomics data available for 358 individuals. Removing 20 percent of the intensities we were able to recover 15 out of 17 significant abundant protein groups using PIMMS-VAE imputations. When analyzing the full dataset we identified 30 additional proteins (+13.2%) that were significantly differentially abundant across disease stages compared to no imputation and found that some of these were predictive of ALD progression in machine learning models. We, therefore, suggest the use of deep learning approaches for imputing missing values in MS-based proteomics on larger datasets and provide workflows for these.

Date: 2024
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DOI: 10.1038/s41467-024-48711-5

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