Enhanced differential expression statistics for data-independent acquisition proteomics
Laura L. Elo; Tomi Suomi
Enhanced differential expression statistics for data-independent acquisition proteomics
Laura L. Elo
Tomi Suomi
NATURE PUBLISHING GROUP
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2021042716967
https://urn.fi/URN:NBN:fi-fe2021042716967
Tiivistelmä
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.
Kokoelmat
- Rinnakkaistallenteet [19207]