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Statistical Analysis of Proteomic Data

Methods and Tools

  • Book
  • © 2023

Overview

Editors:
  1. Thomas Burger

    1. Exploring the Dynamics of Proteomes, Proteomics French Infrastructure, Université Grenoble Alpes, CNRS, CEA, INSERM, Grenoble, France

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  • Includes cutting-edge techniques
  • Provides step-by-step detail essential for reproducible results
  • Contains key implementation advice from the experts

Part of the book series: Methods in Molecular Biology (MIMB, volume 2426)

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Table of contents (17 protocols)

  1. Front Matter

    Pages i-xi
    Download chapter PDF

  2. Unveiling the Links Between Peptide Identification and Differential Analysis FDR Controls by Means of a Practical Introduction to Knockoff Filters

    • Lucas Etourneau, Nelle Varoquaux, Thomas Burger
    Pages 1-24

  3. A Pipeline for Peptide Detection Using Multiple Decoys

    • Syamand Hasam, Kristen Emery, William Stafford Noble, Uri Keich
    Pages 25-34

  4. Enhanced Proteomic Data Analysis with MetaMorpheus

    • Rachel M. Miller, Robert J. Millikin, Zach Rolfs, Michael R. Shortreed, Lloyd M. Smith
    Pages 35-66

  5. Validation of MS/MS Identifications and Label-Free Quantification Using Proline

    • Véronique Dupierris, Anne-Marie Hesse, Jean-Philippe Menetrey, David Bouyssié, Thomas Burger, Yohann Couté et al.
    Pages 67-89

  6. Integrating Identification and Quantification Uncertainty for Differential Protein Abundance Analysis with Triqler

    • Matthew The, Lukas Käll
    Pages 91-117

  7. Left-Censored Missing Value Imputation Approach for MS-Based Proteomics Data with GSimp

    • Runmin Wei, Jingye Wang
    Pages 119-129

  8. Towards a More Accurate Differential Analysis of Multiple Imputed Proteomics Data with mi4limma

    • Marie Chion, Christine Carapito, Frédéric Bertrand
    Pages 131-140

  9. Uncertainty-Aware Protein-Level Quantification and Differential Expression Analysis of Proteomics Data with seaMass

    • Alexander M Phillips, Richard D Unwin, Simon J Hubbard, Andrew W Dowsey
    Pages 141-162

  10. Statistical Analysis of Quantitative Peptidomics and Peptide-Level Proteomics Data with Prostar

    • Marianne Tardif, Enora Fremy, Anne-Marie Hesse, Thomas Burger, Yohann Couté, Samuel Wieczorek
    Pages 163-196

  11. msmsEDA & msmsTests: Label-Free Differential Expression by Spectral Counts

    • Josep Gregori, Àlex Sánchez, Josep Villanueva
    Pages 197-242

  12. Exploring Protein Interactome Data with IPinquiry: Statistical Analysis and Data Visualization by Spectral Counts

    • Lauriane Kuhn, Timothée Vincent, Philippe Hammann, Hélène Zuber
    Pages 243-265

  13. Statistical Analysis of Post-Translational Modifications Quantified by Label-Free Proteomics Across Multiple Biological Conditions with R: Illustration from SARS-CoV-2 Infected Cells

    • Quentin Giai Gianetto
    Pages 267-302

  14. Fast, Free, and Flexible Peptide and Protein Quantification with FlashLFQ

    • Robert J. Millikin, Michael R. Shortreed, Mark Scalf, Lloyd M. Smith
    Pages 303-313

  15. Robust Prediction and Protein Selection with Adaptive PENSE

    • David Kepplinger, Gabriela V. Cohen Freue
    Pages 315-331

  16. Multivariate Analysis with the R Package mixOmics

    • Zoe Welham, Sébastien Déjean, Kim-Anh Lê Cao
    Pages 333-359

  17. Integrating Multiple Quantitative Proteomic Analyses Using MetaMSD

    • So Young Ryu, Miriam P. Yun, Sujung Kim
    Pages 361-374

  18. Application of WGCNA and PloGO2 in the Analysis of Complex Proteomic Data

    • Jemma X. Wu, Dana Pascovici, Yunqi Wu, Adam K. Walker, Mehdi Mirzaei
    Pages 375-390

  19. Back Matter

    Pages 391-393
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Keywords

About this book

This book explores the most important processing steps of proteomics data analysis and presents practical guidelines, as well as software tools, that are both user-friendly and state-of-the-art in chemo- and biostatistics. Beginning with methods to control the false discovery rate (FDR), the volume continues with chapters devoted to software suites for constructing quantitation data tables, missing value related issues, differential analysis software, and more. Written for the highly successful Methods in Molecular Biology series, chapters include the kind of detail and implementation advice that leads to successful results. 

Authoritative and practical, Statistical Analysis of Proteomic Data: Methods and Tools serves as an ideal guide for proteomics researchers looking to extract the best of their data with state-of-the art tools while also deepening their understanding of data analysis.

Editors and Affiliations

  • Exploring the Dynamics of Proteomes, Proteomics French Infrastructure, Université Grenoble Alpes, CNRS, CEA, INSERM, Grenoble, France

    Thomas Burger

Bibliographic Information

  • Book Title: Statistical Analysis of Proteomic Data

  • Book Subtitle: Methods and Tools

  • Editors: Thomas Burger

  • Series Title: Methods in Molecular Biology

  • DOI: https://doi.org/10.1007/978-1-0716-1967-4

  • Publisher: Humana New York, NY

  • eBook Packages: Springer Protocols

  • Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature 2023

  • Hardcover ISBN: 978-1-0716-1966-7Published: 30 October 2022

  • Softcover ISBN: 978-1-0716-1969-8Published: 30 October 2023

  • eBook ISBN: 978-1-0716-1967-4Published: 29 October 2022

  • Series ISSN: 1064-3745

  • Series E-ISSN: 1940-6029

  • Edition Number: 1

  • Number of Pages: XI, 393

  • Number of Illustrations: 50 b/w illustrations, 477 illustrations in colour

  • Topics: Biostatistics, Chemistry/Food Science, general, Bioinformatics

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