Enhanced differential expression statistics for data-independent acquisition proteomics



: Tomi Suomi, Laura L. Elo

Publisher: NATURE PUBLISHING GROUP

: 2017

: Scientific Reports

: SCIENTIFIC REPORTS

: SCI REP-UK

: 5869

: 7

: 1

: 8

: 2045-2322

DOI: https://doi.org/10.1038/s41598-017-05949-y

: https://www.nature.com/articles/s41598-017-05949-y

: 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.

Last updated on 2024-26-11 at 10:43