The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination

Abstract Motivation Data-independent acquisition mass spectrometry allows for comprehensive peptide detection and relative quantification than standard data-dependent approaches. While less prone to missing values, these still exist. Current approaches for handling the so-called missingness have cha...

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Published in:Bioinformatics 2020-04, Vol.36 (7), p.2217-2223
Main Authors: McGurk, Kathryn A, Dagliati, Arianna, Chiasserini, Davide, Lee, Dave, Plant, Darren, Baricevic-Jones, Ivona, Kelsall, Janet, Eineman, Rachael, Reed, Rachel, Geary, Bethany, Unwin, Richard D, Nicolaou, Anna, Keavney, Bernard D, Barton, Anne, Whetton, Anthony D, Geifman, Nophar
Format: Article
Language:English
Subjects:
Humans
Longitudinal Studies
Mass Spectrometry
Original Papers
Proteomics
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Summary:Abstract Motivation Data-independent acquisition mass spectrometry allows for comprehensive peptide detection and relative quantification than standard data-dependent approaches. While less prone to missing values, these still exist. Current approaches for handling the so-called missingness have challenges. We hypothesized that non-random missingness is a useful biological measure and demonstrate the importance of analysing missingness for proteomic discovery within a longitudinal study of disease activity. Results The magnitude of missingness did not correlate with mean peptide concentration. The magnitude of missingness for each protein strongly correlated between collection time points (baseline, 3 months, 6 months; R = 0.95–0.97, confidence interval = 0.94–0.97) indicating little time-dependent effect. This allowed for the identification of proteins with outlier levels of missingness that differentiate between the patient groups characterized by different patterns of disease activity. The association of these proteins with disease activity was confirmed by machine learning techniques. Our novel approach complements analyses on complete observations and other missing value strategies in biomarker prediction of disease activity. Supplementary information Supplementary data are available at Bioinformatics online.
ISSN:1367-4803
1460-2059
1367-4811
DOI:10.1093/bioinformatics/btz898