We found a match
Your institution may have rights to this item. Sign in to continue.
- Title
The use of missing values in proteomic data-independent acquisition mass spectrometry to enable disease activity discrimination.
- 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
- 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.
- Subjects
PROTEOMICS; LONGITUDINAL method; FORECASTING; MACHINE learning; MASS spectrometry
- Publication
Bioinformatics, 2020, Vol 36, Issue 7, p2217
- ISSN
1367-4803
- Publication type
Article
- DOI
10.1093/bioinformatics/btz898