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Proteomics for Biomarker Discovery pp 225–246Cite as

Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR

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Part of the book series: Methods in Molecular Biology ((MIMB,volume 1959))

Abstract

ProStaR is a software tool dedicated to differential analysis in label-free quantitative proteomics. Practically, once biological samples have been analyzed by bottom-up mass spectrometry-based proteomics, the raw mass spectrometer outputs are processed by bioinformatics tools, so as to identify peptides and quantify them, by means of precursor ion chromatogram integration. Then, it is classical to use these peptide-level pieces of information to derive the identity and quantity of the sample proteins before proceeding with refined statistical processing at protein-level, so as to bring out proteins which abundance is significantly different between different groups of samples. To achieve this statistical step, it is possible to rely on ProStaR, which allows the user to (1) load correctly formatted data, (2) clean them by means of various filters, (3) normalize the sample batches, (4) impute the missing values, (5) perform null hypothesis significance testing, (6) check the well-calibration of the resulting p-values, (7) select a subset of differentially abundant proteins according to some false discovery rate, and (8) contextualize these selected proteins into the Gene Ontology. This chapter provides a detailed protocol on how to perform these eight processing steps with ProStaR.

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Acknowledgment

ProStaR software development was supported by grants from the “Investissement d’Avenir Infrastructures Nationales en Biologie et Santé” program (ProFI project, ANR-10-INBS-08) and by the French National Research Agency (GRAL project, ANR-10-LABX-49-01).

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Authors and Affiliations

  1. Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France

    Samuel Wieczorek, Florence Combes, Hélène Borges & Thomas Burger

  2. CNRS, BIG-BGE, Grenoble, France

    Thomas Burger

Authors
  1. Samuel Wieczorek
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  2. Florence Combes
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  3. Hélène Borges
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  4. Thomas Burger
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Corresponding author

Correspondence to Thomas Burger .

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Editors and Affiliations

  1. Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France

    Virginie Brun

  2. Université Grenoble Alpes, CEA, Inserm, BGE U1038, Grenoble, France

    Yohann Couté

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Wieczorek, S., Combes, F., Borges, H., Burger, T. (2019). Protein-Level Statistical Analysis of Quantitative Label-Free Proteomics Data with ProStaR. In: Brun, V., Couté, Y. (eds) Proteomics for Biomarker Discovery. Methods in Molecular Biology, vol 1959. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-9164-8_15

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  • DOI: https://doi.org/10.1007/978-1-4939-9164-8_15

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  • Publisher Name: Humana Press, New York, NY

  • Print ISBN: 978-1-4939-9163-1

  • Online ISBN: 978-1-4939-9164-8

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