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Robust summarization and inference in proteome-wide label-free quantification

View ORCID ProfileAdriaan Sticker, View ORCID ProfileLudger Goeminne, View ORCID ProfileLennart Martens, View ORCID ProfileLieven Clement
doi: https://doi.org/10.1101/668863
Adriaan Sticker
1Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium
2VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
3Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
4Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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Ludger Goeminne
1Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium
2VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
3Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
4Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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Lennart Martens
2VIB-UGent Center for Medical Biotechnology, VIB, Ghent, Belgium
3Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
4Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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Lieven Clement
1Department of Applied Mathematics, Computer Science & Statistics, Ghent University, Belgium
4Bioinformatics Institute Ghent, Ghent University, Ghent, Belgium
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  • ORCID record for Lieven Clement
  • For correspondence: lieven.clement@ugent.be
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Abstract

Label-Free Quantitative mass spectrometry based workflows for differential expression (DE) analysis of proteins impose important challenges on the data analysis due to peptide-specific effects and context dependent missingness of peptide intensities. Peptide-based workflows, like MSqRob, test for DE directly from peptide intensities and outper-form summarization methods which first aggregate MS1 peptide intensities to protein intensities before DE analysis. However, these methods are computationally expensive, often hard to understand for the non-specialised end-user, and do not provide protein summaries, which are important for visualisation or downstream processing. In this work, we therefore evaluate state-of-the-art summarization strategies using a benchmark spike-in dataset and discuss why and when these fail compared to the state-of-the-art peptide based model, MSqRob. Based on this evaluation, we propose a novel summarization strategy, MSqRob-Sum, which estimates MSqRob’s model parameters in a two-stage procedure circumventing the drawbacks of peptide-based workflows. MSqRobSum maintains MSqRob’s superior performance, while providing useful protein expression summaries for plotting and downstream analysis. Summarising peptide to protein intensities considerably reduces the computational complexity, the memory footprint and the model complexity, and makes it easier to disseminate DE inferred on protein summaries. Moreover, MSqRobSum provides a highly modular analysis framework, which provides researchers with full flexibility to develop data analysis workflows tailored towards their specific applications.

Footnotes

  • https://github.com/statOmics/MSqRobSum

  • https://github.com/statOmics/MSqRobSumPaper

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted June 13, 2019.
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Robust summarization and inference in proteome-wide label-free quantification
Adriaan Sticker, Ludger Goeminne, Lennart Martens, Lieven Clement
bioRxiv 668863; doi: https://doi.org/10.1101/668863
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Robust summarization and inference in proteome-wide label-free quantification
Adriaan Sticker, Ludger Goeminne, Lennart Martens, Lieven Clement
bioRxiv 668863; doi: https://doi.org/10.1101/668863

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