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 ag...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Publishing Group
2017-07-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-05949-y |
| Summary: | 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. |
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| ISSN: | 2045-2322 |