A1 Refereed original research article in a scientific journal
Enhanced differential expression statistics for data-independent acquisition proteomics
Authors: Tomi Suomi, Laura L. Elo
Publisher: NATURE PUBLISHING GROUP
Publication year: 2017
Journal: Scientific Reports
Journal name in source: SCIENTIFIC REPORTS
Journal acronym: SCI REP-UK
Article number: 5869
Volume: 7
Issue: 1
Number of pages: 8
ISSN: 2045-2322
DOI: https://doi.org/10.1038/s41598-017-05949-y
Web address : https://www.nature.com/articles/s41598-017-05949-y
Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/25088055
We describe a new reproducibility-optimization method ROPECA for statistical analysis of proteomics data with a specific focus on the emerging data-independent acquisition (DIA) mass spectrometry technology. ROPECA optimizes the reproducibility of statistical testing on peptide-level and aggregates the peptide-level changes to determine differential protein-level expression. Using a 'gold standard' spike-in data and a hybrid proteome benchmark data we show the competitive performance of ROPECA over conventional protein-based analysis as well as state-of-the-art peptide-based tools especially in DIA data with consistent peptide measurements. Furthermore, we also demonstrate the improved accuracy of our method in clinical studies using proteomics data from a longitudinal human twin study.
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