Enhanced differential expression statistics for data-independent acquisition proteomics.

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.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Suomi T,Elo LL

doi

10.1038/s41598-017-05949-y

subject

Has Abstract

pub_date

2017-07-19 00:00:00

pages

5869

issue

1

issn

2045-2322

pii

10.1038/s41598-017-05949-y

journal_volume

7

pub_type

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