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...

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Autores principales:	Tomi Suomi, Laura L. Elo
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7935f0467ab84c5385fae58ec6731df8
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spelling	oai:doaj.org-article:7935f0467ab84c5385fae58ec6731df82021-12-02T16:06:55ZEnhanced differential expression statistics for data-independent acquisition proteomics10.1038/s41598-017-05949-y2045-2322https://doaj.org/article/7935f0467ab84c5385fae58ec6731df82017-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-05949-yhttps://doaj.org/toc/2045-2322Abstract 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.Tomi SuomiLaura L. EloNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-8 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tomi Suomi Laura L. Elo Enhanced differential expression statistics for data-independent acquisition proteomics
description	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.
format	article
author	Tomi Suomi Laura L. Elo
author_facet	Tomi Suomi Laura L. Elo
author_sort	Tomi Suomi
title	Enhanced differential expression statistics for data-independent acquisition proteomics
title_short	Enhanced differential expression statistics for data-independent acquisition proteomics
title_full	Enhanced differential expression statistics for data-independent acquisition proteomics
title_fullStr	Enhanced differential expression statistics for data-independent acquisition proteomics
title_full_unstemmed	Enhanced differential expression statistics for data-independent acquisition proteomics
title_sort	enhanced differential expression statistics for data-independent acquisition proteomics
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/7935f0467ab84c5385fae58ec6731df8
work_keys_str_mv	AT tomisuomi enhanceddifferentialexpressionstatisticsfordataindependentacquisitionproteomics AT lauralelo enhanceddifferentialexpressionstatisticsfordataindependentacquisitionproteomics
_version_	1718384783949561856

Enhanced differential expression statistics for data-independent acquisition proteomics

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