Enhanced differential expression statistics for data-independent acquisition proteomics

• Main Authors: Tomi Suomi, Laura L. Elo
• Format: Article
• Language: English
• Published: Nature Publishing Group 2017-07-01
• Series: Scientific Reports
• Online Access: https://doi.org/10.1038/s41598-017-05949-y

Abstract 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.